Sustainable and Digital Twin-Based Approaches for the Transport Infrastructure

#### **Chapter 5**

## Providing Sustainable Transport Infrastructure through Internalization of External Costs: A Case Study from South-Eastern European Countries

*Christina Nikolova*

#### **Abstract**

The most important goals for transport systems development in the European countries are related to increasing transport system efficiency and sustainability and pushing national economies' competitiveness. After a thorough analysis of transport costs, a system of measures should be undertaken to achieve these goals. All these issues are on the top of the political agenda so far, considering the impacts of the COVID pandemic in recent years and the current developments of Just Transition and the European Green Deal ambitions. However, they could not be reached without accounting for transport's social costs, especially external ones. The chapter's main objective is to demonstrate the opportunities of the internalization approach and its updates for evaluating marginal external transport costs on a national level for South-Eastern European countries. As a result, a background will be provided to help policymakers in these counties to prioritize measures and projects envisaged in inland modes of transport based on potential savings for the society, which is not done so far. The chapter also discusses the effects of improving transport infrastructure functioning and performance by using internalization of external costs.

**Keywords:** sustainable transport, external costs for transport, internalization of external costs, infrastructure charging, transport policy

#### **1. Introduction**

The main ambition of the Transport policy in the EU is to provide efficient and sustainable transport systems and services to societies and to push national economies' competitiveness. These goals could be reached through a system of measures undertaken after a thorough analysis of transport costs [1]. However, this analysis needs the application of contemporary cost accounting approaches in transport and up-to-date infrastructure charging principles.

The infrastructure charging system in transport in the EU is based on the "user is to pay" principle. However, besides the internal costs (private costs) calculated in infrastructure charges, other costs are generally not reflected in charges but influence external parties. Hence, it is necessary to differentiate charges to account for external costs for different modes of transport. The differentiation could be achieved by internalizing external costs for transport in infrastructure charges by applying a common approach for infrastructure charging in all modes of transport.

All these issues appear to be of utmost importance when analyzing transport activities and the opportunities for funding infrastructure projects in South-Eastern European Countries and to achieve respective transport policy goals.

The evaluation of marginal external transport costs on a national level for South-Eastern European countries is suggested in this chapter to clarify the application of the approach and its opportunities to balance transport modes sustainably. Furthermore, the results could help countries' policymakers prioritize measures and projects envisaged in inland modes of transport based on potential savings for society, which has not been done so far. Finally, the chapter suggests measures for improving transport infrastructure funding and performance.

#### **2. Methodology for evaluation of external infrastructure costs**

On the European level, many projects and studies were carried out to estimate the proper impact of externalities and to translate it into societal costs (GRACE, UNITE, RECORDIT, SPECTRUM, HEATCO, NEWEXT, etc.). Although the transferability of results remains limited, a considerable number of different researchers pave the way toward proper valuation. The Handbook on the external costs of transport and its updates give a detailed overview of what is being done in the field of cost estimation so far [2].

The cost data used by infrastructure companies are insufficient, heterogeneous, and inappropriate for thorough analysis and evaluation. On the other hand, using complex cost categories and new information sources is a resource- and timeintensive, which is unacceptable in the short term [3]. Therefore, existing practices and methods for determining infrastructure charges are the initial basis for specifying cost categories and the data used for estimating marginal social costs for transport [4].

The necessity to develop a common framework for charging for transport infrastructure use is defined at the European Union level [5]. It is because infrastructure charges affect the conditions of competition in the internal market. Furthermore, they are related to ensuring access to the transport market and significantly affect the development of international transport [6]. Therefore, in order to achieve the objectives set, the following basic principles are justified:


• They must differ only where there are fundamental differences in the cost and quality of services and should not be discriminatory regarding users' nationality and origin.

The only approach that fully meets these criteria is charging based on marginal social costs, i.e., users pay the costs (internal and external) that they trigger when using the infrastructure. This approach incentivizes consumers to reduce infrastructure costs while maximizing individual benefits and economic and social well-being.

Besides the costs reflected in the applied infrastructure charges (internal costs), some costs are not paid by the users causing them but affect parties external to the transport process and are not included in the charges [2]. Some of these external costs are marginal. The procedures for allocating costs depend on the valuation method applied. Cost allocation is usually done indirectly through theoretical and empirical cost analysis, using different indicators and coefficients of the other influencing factors [7]; these allocation indices can be combined to establish marginal costs for different vehicle types [6]. The allocation indices can be combined to evaluate marginal costs for different vehicle types.

The introduction of proper charging for the use of transport infrastructure provides for charges to be set entirely based on full social costs, i.e., variable and fixed infrastructure costs and external costs [8]. To this end, it is necessary to specify how to assess the different types of external costs. In doing so, their calculation is again linked to the setting of marginal costs.

Three major groups of external costs could be specified as follows:


In assessing the *congestion costs*, three leading indicators are used that affect the level of these costs – the assessment of the travel time, the ratio of " travel time – demand for transport services," and the function of demand. The value of travel time can be evaluated in several different ways: first, by assessing the value of time for society as a whole. In this case, a distinction should be made between the time to work and the rest spent on travel. The salary per hour may reflect the production result that can be achieved for the time used for transport [11].

Concerning rest time, the willingness to pay for actual or hypothetical consumer preferences should be assessed. These preferences are studied using surveys. The assessment of private travel is carried out using the neoclassical model of individuals maximizing the utility of the consumption of services under certain budgetary constraints [12]. The costs for business trips take into account individual aspects of a person's productivity. Further research is needed in this area to justify better the indicators that determine the assessment of the travel time.

The optimal level of congestion charges is determined by the intersection of the cost curve of transport infrastructure users and the demand curve for transport services. This level reflects the demand for access to transport infrastructure and responds to the necessity of internalizing these costs in the charges, thus providing for cost reduction. Thus, the demand function determines the relationship between actual external costs and equilibrium charges to address the consequences of congestion.

The flow of vehicles providing transport services can be explained as a physical relationship between the number of vehicles using the transport infrastructure over a specified period and the corresponding speed at which those vehicles move. The ratio of "travel time – demand for transport services" can be described with different functions (hyperbolic, logit, linear) [8]. In this case, it is crucial to consider the relative share of demand diverted due to congestion, which will be directed to another time or alternative route.

A significant factor influencing the occurrence of congestion is the costs of individual infrastructure users. These costs increase as the number of users increases. Thus, if the charges paid by users are set to reflect the actual external congestion costs, the demand for access will be reduced and, together with it, the external costs themselves.

Several studies have been carried out in the EU on external congestion costs. However, these congestion impacts have not been sufficiently investigated [13]. Moreover, it is unclear whether these costs should be defined as external when scheduled services are offered. For example, the costs associated with delays due to congestion caused by one rail operator to another are external. However, it is debatable whether delays in the presence of only one operator should be considered essential for price fixing or included in them. With this in mind and based on the conclusions on the state and use of infrastructure in different modes of transport, it can be summarized that these costs must be considered when determining infrastructure charges for road and air transport and are less critical in rail. In this respect, it is necessary to study them further and include them in the charges when reaching the appropriate level of use of the infrastructure.

The *environmental costs* are related to eliminating heterogeneous transport influences such as noise, air, water, and soil pollution. However, the valuation of these influences is hampered by the fact that it is not goods and services that can be sold and bought. Therefore, different methods are used [14], such as:

• the market for substitute goods and services, respectively, transport costs where consumers benefit from public recreational facilities, are used to estimate these costs. Alternatively, they could be evaluated by using the consumer assessment of individual goods and services depending on their exposure to pollution or noise. Thus, assessing such goods and services concerning their environmental characteristics can be used. The environmental costs can also be assessed by examining the members of society willing to pay to reduce or eliminate adverse

environmental effects. There are some difficulties associated with the sensitivity of individuals to the ecological factor in applying this method;


Measuring the different adverse effects is complex, but there are still some studies [15] attempting to quantify them accurately. For example, noise is measured depending on the duration and sensitivity of the human ear. Its impacts are usually assessed by lowering the prices of buildings in noisier areas. Noise also impacts people's health due to its influence on the cardiovascular system and sleep, which are general health components. Air pollution is measured by the amounts of harmful vehicle emissions – nitrogen and sulfur oxides, particulate matter, and volatile organic compounds. Assessing their impact on people, animals, plants, and buildings is complicated. There are still significant inconsistencies in determining the long-term effect of these on human health. The values estimates include the direct costs of disability, the cost of protection from emissions, and the assessment of consumers' willingness to pay to avoid damage caused by air pollution.

The *accident costs* are inherent in the transport industry [16]. They have a high value depending on the number of people killed and injured in accidents, the cost of human life, or the damage caused. The value of human life is most often assessed by assessing human capital and calculating losses or reduced production due to damage caused. It is also possible to assess the willingness to pay extra for transport at greater risk.

For the calculation of the accident costs, it is necessary to consider not only the assessment of the value of human life but also the value of the damage caused to persons and property, as well as the production losses resulting from the absence of employees from work. The cost of the damage includes direct (medical expenses, transport of victims, etc.), indirect costs (loss of production), and subjective assessments (of pain and suffering). Therefore, it is impossible to determine to what extent these costs are covered by transport insurance [12]. Furthermore, there are also fluctuations in the extent to which these external costs are related to the transport volume, respectively, with the flow of vehicles and at driving speeds. These issues also require studying the interaction between congestion and the number of transport accidents.

The possibilities for internalizing external costs are not yet fully used in the infrastructure charging systems in transport sectors of the South-Eastern European countries [6]. Except for charges on liquid fuels and excise duties relating to covering the costs of protecting and preventing environmental pollution, there are only a few to consider these costs (for example, environmental and noise-related markups in

airport charges). Therefore, there is a need for in-depth and concrete research into these opportunities and the definition of approaches and methods for evaluating external costs and their internalization in infrastructure charges. Furthermore, the revenues from such charges may be used to finance future investments. There is no need to set a uniform approach to measuring external costs, but it must be determined how different cost estimates or approaches can be applied correctly. However, the possibilities for internalizing external costs can be determined by whether the marginal cost function is increasing or decreasing [17]. Therefore, determining the marginal costs of transport infrastructure should be based on the average of the elasticity factors of maintenance and repair costs to changes in transport volume.

Thus, if the cost function is decreasing, as in rail and waterborne transport, the marginal costs have not reached their minimum, and there are opportunities for economies of scale. That is, with a 1% increase in transport volume, costs increased by less than 1%. Therefore, in this case, it is possible to apply supplements and include external costs in infrastructure charges without drastically reducing revenue. Conversely, when the cost function is increasing, i.e., a 1% increase in transport volume corresponds to a more significant cost increase, there is a decreasing return on a scale. In this case, the inclusion of external costs will cause a substantial increase in charges and affect the usage of transport infrastructure.

The discussed charging approach is critical to ensure the efficient use of infrastructure and creates the conditions for its financing from users' payments, which require new and different funding models. Transport operators and users paying the actual costs have clear incentives to make their choice, for example:


Consequently, the infrastructure charging system developed in implementing such a common approach provides incentives to improve transport performance through more comprehensive benefits by reducing the costs associated with external effects. Even transport operators who have not changed their services will benefit from changes undertaken by others, such as reducing congestion and improving infrastructure conditions, reducing the risk of accidents, etc.

#### **3. Methodology for internalization of external costs**

Based on the analysis carried out of the current principles of infrastructure charges in transport, it is found that the charges in the different modes of transport are based on the average or marginal cost of maintaining, repairing, and operating the infrastructure concerned [1]. Therefore, to take into account marginal social costs for transport, general principles for evaluating costs, including external ones, should apply to all modes of transport.

Cost grouping is essential as it reflects the content of the costs already determined and is a step toward their allocation. At this stage, the existing cost categorization may be used, or accounting information sources adapted to the theory of marginal costs [18]. However, to justify the inclusion of the relevant group of costs in determining infrastructure charges, it is necessary to define more detailed and precise cost categories. In doing so, an account should be taken of the information limitations according to which specific categories of expenditure are aggregated, and it is not possible to accurately reflect the different variable costs [19].

In some cases, determining the reasons for different costs is relatively easy, and in others – not so much. Therefore, the different cost categories may be further grouped according to their reasons. The individual elements of the variable costs shall then be defined using the relevant qualitative technical and economic indicators.

After grouping the costs according to their intended purpose, it is crucial to create the necessary prerequisites for their transfer to those who cause them through the system of infrastructure charges [8]. Ideally, charges should change as each cost changes. However, the practical application of such an approach is not easy, so it is necessary to use more generalized categories. First, an analytical approach may be applied when examining infrastructure costs based on the total costs allocated to one vehicle. The second option is to apply the synthesis approach, which collects information on the costs associated with individual vehicles and summarizes these costs. The availability of data on the costs concerned their categorization and the possibilities for allocating them predetermine the use of one of the two approaches. Synthesis is appropriate when the objective is to determine the function of the total costs from which to infer the first derivative (the function of marginal costs). Where the total costs are known and the individual elements are not, it is more appropriate to use the analysis.

For cost allocation purposes, it is necessary to link the different cost categories to the relevant indicators, using data on physical and technical interaction between vehicles and roads or railways. In applying this approach, the allocation procedure must be transparent. These requirements may be tailored as follows:

When allocating marginal costs depending on the weight and number of vehicles, axle load indicators are used mainly. The mileage indicator is used to allocate costs that do not depend on the gross weight of the vehicles. In order to improve the information base, it is necessary to extend the above analysis method (in terms of mixed traffic, the share of different heavy goods vehicles).

Estimates of individual costs differ; some have a direct financial dimension, others depend on the likelihood of an event, and others have a physical or physiological expression (see **Table 1**).

The focus at this stage should be on the cost drivers and not on the incurrence of the costs themselves. Infrastructure costs, as well as environmental and congestion costs, can be directly attributed to the transport volume [20]. In this regard, the infrastructure charges imposed on consumers are the most appropriate toolbox for ensuring adequate price signals in the transport infrastructure market. However, concerning the costs caused by transport accidents, this toolbox can be assessed as inappropriate. On the other hand, approaches based on general taxation and specific transport taxes and charges (e.g., vehicle tax and charges on liquid fuels) are not particularly precise as they are not based on the specific costs incurred due to transport accidents. Furthermore, these taxes and charges do not alert consumers to correct their behavior because of accidents.


**Table 1.**

*Costs elements related to the use and maintenance of infrastructure.*

Introducing the charges related to externalities will lead to the provision of additional revenue from infrastructure charges. From a fairness point of view, it is desirable to use the accumulated money to compensate victims of accidents, for example, or to finance measures to limit future negative influences. Furthermore, even higher cost recovery levels can be achieved if the funds are allocated to achieve common infrastructure objectives [21]. Analyses carried out on the European level provide a reason to summarize that the total revenues for the transport system will exceed infrastructure costs.

Differentiation of charges taking into account external costs is also possible as different modes of transport have different external costs. Such a measure would more effectively impact the charges for using the infrastructure. In this respect, it is necessary to introduce simultaneously environmental charges related to noise, harmful emissions, transport accidents, and congestion in different modes of transport. Demand for infrastructure capacity changes depending on the hours of the day, the type of traffic, and the direction of alternative routes. In principle, transport operators should pay different fees for different destinations and times of day in order to adequately reflect the insufficient (depleted) capacity and ensure its more efficient allocation [8]. This guideline should be used to increase the efficiency and sustainability of the use of transport infrastructure.

Improving the infrastructure charging system by internalizing external transport costs will lead to more efficient use of infrastructure and higher coverage of the costs of maintaining and operating it. In addition, this process will create prerequisites for financing the construction of new infrastructure. In combination with subsidies provided directly by the state to offset the overall public benefit to non-direct users of infrastructure, a high, or perhaps full, level of covering maintenance and operation costs is likely to be achieved. Suppose full coverage is not ensured, and the state

wishes to ensure a higher level. In that case, this can be achieved by imposing additional, fixed, non-discriminatory user charges that do not change the proportions between modes of transport. In addition, investment projects will, at least in the medium term, require a high level of cost coverage. In such cases, higher charges may be applied for a particular time, following the rules on non-discrimination and providing guarantees that monopoly profits will not be allowed to be realized.

In the presence of sufficiently reliable and detailed methodologies based on the described approach, it is possible to recalculate the marginal costs for each year [22]. Thus, in the event of a change in cost ratios or a significant change in the use of infrastructure (e.g., when capacity is exhausted), changes will be able to be reflected promptly and infrastructure charges updated. In this way, they will consider the actual conditions for using the infrastructure and provide adequate revenue for undertakings offering access to it.

However, it should be taken into account that the marginal costs do not change proportionately as the volume of transport changes. Therefore, it cannot be assumed that the mathematical function of the costs is linear. Furthermore, it is necessary to determine what other factors affect the costs of maintaining and operating transport infrastructure. All these limitations require an examination of the type of cost function.

#### **3.1 Evaluation of marginal costs function**

Research carried out at the European level has shown that the main costs, which vary according to the volume of transport for railways and road infrastructure, are the cost of maintenance and repair. The leading indicators used to allocate costs are defined in this respect. For terminal infrastructures, such as airports and ports, these are the labor costs of staff engaged in servicing aircraft/vessels and passengers or handling goods [22]. In road and rail transport, the leading indicators are the volume of traffic in gross tonne-kilometers, the number of bridges and tunnels, the level of electrification, and the infrastructure's operation duration. Regarding terminal infrastructure, the airports and ports shall consider the number of air movements, passengers served, and ships served. Seasonal and weekly fluctuations in transport volume should also not be overlooked.

The function describing the change in the cost of maintaining and repairing the transport infrastructure presents the relationship between these costs and the transport volume. For the definition of this heading, the relationship between the total marginal costs of transport infrastructure (TCinfra), the volume of traffic (Q), and the factors influencing them should be clarified. Influencing factors may be, for example, infrastructure parameters (I), the cost of construction of the infrastructure (p), vehicle weight (W), speed of movement (S), weather conditions (Z), etc. Therefore, the overall type of cost function suggested by the author is:

$$\text{TC}\_{\text{infra}} = \text{f} \left( \text{Q, p, W, S, I, Z, \dots} \right) \tag{1}$$

Research carried out in EU countries gives rise to the transcendental logarithmic function being considered the most accurate for studying infrastructure costs in road and rail transport [23]. It provides possibilities for initial analysis of the total costs and phasing out the function according to the type of infrastructure. Another advantage is that it is a flexible mathematical model that gives good results in studying unknown products or cost functions. This model also meets the requirements of neoclassical

economic theory related to the substitution of production factors, economies of scale of production, and technological changes [24]. The limitations of using the transcendental logarithmic function are not significant. They relate only to possible changes in the vehicle technologies in use.

The type of aggregated function adapted to railway infrastructure conditions and the necessary cost data is as follows (adapted and suggested by the author):

ln Cð Þ¼ <sup>m</sup> α0 þ αl*:* ln l þ αk*:* ln kt þ αQg*:* ln Qg þ αSw*:* ln Sw þ αNt*:* ln Nt þ ln l ½βll*:* ln l þ βlk*:* ln kt þ βQg*:* ln Qg þ βSw*:*<sup>l</sup> *:* ln Sw þ βNtl*:* ln Nt þ lnkt ½βktkt*:* lnkt þ βktQg*:* ln Qg þ βSwkt*:* ln Sw þ βNtkt*:* ln Nt þ lnQg ½βQgQg*:* ln Qg þ βNtQg*:* ln Nt þ ln Nt ½βNtNt ð Þ *:* ln Nt , (2)

Where the dependent variable Cm reflects the costs of maintaining railway infrastructure, and the independent variables are:

l – the length of the railway sections;

kt – the variable determining the electrification of railway lines;

Sw – the number of arrows in each plot;

Qg – the gross traffic volume on the relevant section;

Nt – the number of trains passing on the sections for a certain period (e.g., for 1 year);

α<sup>0</sup> – constant;

α – the elasticity coefficient;

β – the correlation factor between the indicators.

Data availability and quality influence costs and are crucial for econometric analysis. In this respect, it is necessary to provide detailed data and adapt them to the regression analysis needs. A similar model is suitable for describing the cost function in road transport. The model includes the cost of repairing and maintaining individual road sections, variables for road category, and annual average daily transport volumes.

Concerning airport infrastructure, an appropriate form for the cost function is cubic, as it best describes the cost dependency on the volume of transport at airports with predominant international traffic. The main indicators to be included in the model are as follows: number of staff (n), respectively, duration of work in personhours by type of activity, annual cost of carrying out the different types of services (C), number of air movements (m), respectively number of passengers served. The study of the type of cost function for different indicators follows the model (adapted and suggested by the author):

$$\mathbf{C} = \mathfrak{B}\_0 + \mathfrak{B}\_{\mathbf{n}1}\mathbf{n} + \mathfrak{B}\_{\mathbf{n}2}\mathbf{n}^2 + \mathfrak{B}\_{\mathbf{n}3}\mathbf{n}^3 + \mathfrak{B}\_{\mathbf{n}4}\mathbf{n}^4 + \mathfrak{e}\_{\mathbf{t}} \tag{3}$$

This model must also consider seasonal and weekly fluctuations in transport volume and differences in service standards. In this way, higher reliability of the analysis can be ensured.

In the short term, facilities' wear and tear costs are not so high for port infrastructure. Therefore, the main costs to be considered in the analysis are the costs of loading and unloading operations and the labor costs of port workers. The model describing the type of cost function may include the following indicators: annual costs of using the port (TC), the quantities of freight passing through the port per year (Q), and the

total quantity of goods passing through the port over the entire period (Qcum). It is also possible to include the annual investment costs, the number of persons employed in ships'servicing, and the labor costs for those persons. Studies carried out in EU countries show that the most appropriate form of the function is the logarithmic Cobb-Douglas specification of the type (adapted and suggested by the author):

$$\log \text{TC} = \text{loga} + \text{blockQ} + \text{clog} \text{Q}\_{\text{cum}} + \text{d}\_{\text{y}} \tag{4}$$

In the absence of sufficiently detailed and reliable data for econometric analysis (large statistical rows of at least 50 meanings are required), it should be clarified that the summaries made are theoretically valid but require further practical and applied analyses. In addition, they should present the specific results of the correlation between the change in costs and the factors influencing them.

Implementing the first stages of the described approach provides for basic infrastructure charges for roads, railways, ports, and airports to be defined. However, it should be taken into account that the marginal costs do not account for all variable costs, i.e., they need to be included in infrastructure charges in other ways to ensure higher or even full cost recovery.

The marginal costs of transport infrastructure shall be determined by the use of the econometric models or only by the simple determination of the cost elasticity factors relative to the transport volume. In the absence of a sufficiently detailed database of the cost categories, quantifying the marginal costs of transport infrastructure may be done using the principles for the transfer of research results as recommended by the Handbook on external transport costs [2]. The relevant reference values by cost category shall be selected, and the results obtained using the econometric approach shall be applied.

Following the suggested methodological approach, the next stage for infrastructure charging involves markups' calculation to reflect the external costs for transport and harmonize the infrastructure charging systems in different modes of transport. Then, depending on the indicators included in the study of the cost function and calculated elasticity factors, it is necessary to determine the amount of marginal external costs related to each indicator. Thus, the remaining costs (external) can be allocated based on the marginal costs already allocated.

#### **3.2 Calculation of external marginal costs for SEEC for evaluating markups to infrastructure charges**

The calculation of markups to marginal infrastructure costs can be carried out by using the transferring tool for the results of econometric studies as suggested in the Handbook on the external costs of transport [2]. However, detailed data on different indicators for certain road sections or individual infrastructure sites should be considered in this case. The coefficients obtained should not be applied directly. Instead, they should be adapted to the using conditions, the characteristics of the country's infrastructure, and the year of calculation. The calculated cost dependency factors for the transport volume will determine the marginal external costs. The remaining additional costs may be allocated proportionally to predetermined cost dependency factors from the average daily traffic volume per category of vehicles. Overall workflow of the model in terms of input data, model variables and output, is presented in **Figure 1**.

Considering recent updates of the Handbook on external transport costs on the European level [2], there are no projects or studies conducted in most South-Eastern

**Figure 1.** *Model workflow from data input to expected results.*

European countries. However, external cost evaluation for some of the countries in this region has been included in the OECD report on external transport costs in Central and Eastern Europe [1]. Still, other relevant studies do not cover most of the SEE region countries.

The Handbook on the external costs of transport represents one of the possible reference bases for further external costs studies in the South-Eastern European countries [25]. The methodology for the external cost calculation can be widely used since the unit values for input figures are presented in monetary terms related to the specific value, such as Euro per hour, per accident, per unit of emission, per life year lost, etc. The output values are presented in a form that can be translated for internalization. The central unit for the infrastructure pricing is the cost per vehicle- or tonne-kilometers. Similar to other studies of external costs, a transfer of cost per passenger or tonne-kilometer has been carried out to compare different modes. Where relevant or valuable, other output unit values are shown. When applying the results to the SEE region, it should be considered that the figures are directly applicable to some SEE countries (EU members). However, for others (non-members), the value transfer approach is used to transfer the data to these countries. It can still provide reliable data for policy purposes at lower accuracy based on the guidelines for estimating external transport costs. The Handbook provides ready estimations with limited case-specific data; total/average and marginal external cost figures are provided for all countries and transport modes. Where relevant, differentiations to relevant vehicle characteristics (e.g., fuel type, size class, etc.) and traffic situation (type of road, day/night, thin/dense traffic, etc.) are provided [2].

The example provided in this section presents the calculations of total marginal external costs for pilot routes in SEE countries (for road and rail infrastructure) by using marginal values in order to present the potential of the described approach to defining markups to marginal infrastructure costs for charging for the use of infrastructure in these countries.

Calculating marginal external costs for specific routes in SEE countries is based on the reference values of the marginal external costs (€ct/vehicle for accident costs and €ct/tkm for all other costs) and transport modes provided by the Handbook referring to 2016.

These values are adjusted by using GDP per capita in PPPs coefficients for 2016 by country and by respective coefficients related to harmonized indices of consumer prices (HICP) for 2021 relative to 2016 (counted to index 2020 = 100). Through this adjustment, the reference values have been updated in line with current economic conditions and reflect the specificities of each SEE country (see **Tables 2** and **3**).

For the approach validation, the calculations of marginal external costs have been made for the pilot routes presented in **Table 4**, which presents the characteristics of each pilot route in detail.

The external costs for pilot routes are calculated according to the recommendations in the Handbook [2] and Annex 2: General instructions for the calculation of external costs [24]. In addition, the following methodology has been applied:



#### **Table 2.**

*Adjustment factors for calculating marginal and total external costs.*



#### **Table 3.**

*Reference and adjusted values of marginal external costs for transport in SEE countries, €ct/tkm.*


Finally, the calculation of potential markups included in the infrastructure charges as part of internalizing external transport costs could be calculated per km for every pilot route, as suggested in **Table 5**.

Calculating the marginal external costs by type of vehicles, modes of transport, and different pilot routes are used to present the total external marginal costs for each route by cost category. This creates an opportunity for comparing the costs for different routes. However, it should be considered that the value transfer to different EU countries is sensitive to national and local specifications and is only undertaken because no national studies are available. Therefore, the respective results represent rough estimates only.

As the final calculations show, the total marginal external costs for the movement of different types of vehicles are the lowest for the rail routes. Furthermore, the load capacity of the trains is many times higher than road vehicles, thus providing a better performance of rail transport and lower costs for internalization. Considering the calculated total marginal external costs per km and type of vehicle/train, they could be used for the final calculation of markups to be included in railway infrastructure charges and tolls. The results show that the respective markups increase with the load capacity of vehicles and are the highest for heavy goods vehicles. Something more, the higher the vehicle capacity in road transport, the higher the markups.

In conclusion, it should be noted that it is impossible to compare directly respective costs for different pilot routes because the vehicles used for calculations are different for each mode of transport and have different load capacities. However, if traffic data (for example, number of vehicles running on each route) are available, it would be possible to evaluate the total external costs for the usage of each route for a certain period.

The discussed approach provides an opportunity for higher cost recovery, especially in reflecting external transport costs. Where charges reflect infrastructure, congestion, and other external costs, transport services will ensure full cost recovery



**Table 4.** *Pilot routes characteristics.*

and thus help to balance the country's transport system further. Nevertheless, of course, it is a matter of transport policy decision to define the exact level of external costs covered by the infrastructure charges and to provide a reasonable explanation for the rest of these costs to be covered by the society as a whole, not only by the transport users.

#### **4. Socioeconomic effects of the internalization of external costs for transport**

#### **4.1 Distribution effects**

The aim of the internalization of external costs of transport in infrastructure charges is to increase the efficiency of transport activity and to provide proper pricing signals to the users for the actual social costs they impose by their modal choices. A compensatory mechanism should be proposed to ensure fair pricing and competition in the transport market if the internalization leads to increased infrastructure charges and undesirable allocation effects. The volume of transport, in general, is increasing, meaning wealthier households spend most of their income on transport. Therefore, determining transport charges based on the proposed approach to internalizing the external costs may have a positive rather than a negative effect on allocation. However, the final effect will depend to a large extent on the increase in costs in the respective mode of transport and on the type of compensation mechanism applied by the state. Thus, the real disposable income will increase for each socioeconomic group.

The implementation of the approach to setting infrastructure charges based on marginal social costs will provide a significant benefit to the whole society, as well. It will lead directly to improved technological, operational, and organizational



*Notes: \* The marginal accident costs are calculated based on the reference values in €ct/vehicle km. \*\*Values for 3,5 t vehicle are for morning peak (max).\*\*\*Values for 3.5 t vehicles are for gasoline.\*\*\*\*Values for 3,5 t vehicle and 400 t train are for the day and dense traffic.*

#### **Table 5.**

*Total marginal external costs by cost category and pilot routes.*

efficiency, the necessary minimum changes in the modal shift, and a minimal reduction in demand for transport.

#### **4.2 Effects in the field of integration of underdeveloped areas**

Implementing the infrastructure charging system, in line with the proposed approach, will also change transport prices in peripheral or underdeveloped areas. The charges will be differentiated to have a lower impact on areas with less congestion and pollution. Therefore, charges reflecting the related costs in rural and peripheral areas where infrastructure is low and there is no congestion will be lower. Furthermore, as highlighted above, the system is likely to generate significant benefits that can be targeted at less developed areas. In case higher infrastructure charges hamper the economic development of peripheral and underdeveloped areas, the reform of the infrastructure charging system must be implemented flexibly and smoothly, providing that it does not distort competition. Difficulties will arise when the infrastructure

facilities concerned are the only links with the rest of the country or are important business centers for the local economy. On the other hand, where transport infrastructure capacity is relatively low, significant investments are needed to increase accessibility to accommodate increased traffic. Therefore, there may be a need to apply charges leading to higher cost recovery.

Differentiated infrastructure charges will cause changes in the structure and distribution of transport costs. They will reduce transport costs for the whole society and reduce direct costs for some producers. Moreover, transport costs will increase for producers who cannot change their behavior per these charges. As already stated, transport costs have a relatively low share in the total production costs of industrial enterprises. In the short term, some producers will be partially affected if they are located in peripheral areas, dependent on the only mode of transport, and selling their products in small markets in competition with other domestic producers. Local authorities in these peripheral areas may take measures to support the competitiveness of the producers concerned in the central markets. They may assist them in adapting the product structure to support products of higher value and with a higher relative share and by improving the quality of the main transport links.

#### **4.3 Economic effects**

From a general economic point of view, the long-term effect of external costs' internalization will have little and no indirect impact on GDP growth but will allow for secondary benefits through revenue growth. Improving the infrastructure charging system will provide a more accurate basis for comparing returns on investment in transport and improving conditions for private investment and infrastructure operation. When introducing direct infrastructure charges, each shipment can be assessed according to the costs and benefits incurred, as all costs will be considered. This will create opportunities for transport services to deliver economic profit. On the other hand, internalizing the environmental costs will increase environmental efficiency and sustainability, i.e., where the charges reflect the costs of removing harmful emissions, the level of such emissions will fall to the point where the costs of reducing them will equalize the benefits of this measure. In this way, in terms of social efficiency, the well-being of society will be maximized, not the number of trips.

From a financial point of view, more efficient use of the transport system will reduce the need for government spending on infrastructure, health, and environmental protection. The net effect in the commercial sector will be positive. The direct effect of higher transport charges will be offset by reducing congestion and accident costs and any possible tax reductions provided by the government. There may be some decrease in transport-intensive industries where transport costs are high at the final cost of production. However, this decrease will be slight as the overall increase in transport prices will be slow, and companies will regulate (adjust) their material and technical supply and production.

For each transport mode, the relative price changes will vary depending on the cost structure as well as the initial structure of the infrastructure charging system. Nevertheless, the primary data from the various studies in the EU concerning the impact of changes in transport charges show that the net well-being of consumers is improving. Furthermore, these results show that the benefits achieved by reducing congestion and pollution and reducing tax payments outweigh the losses arising from the price increases of the transport services concerned.

Urban transport surveys show that price changes are causing positive technological changes, with peak hour traffic in cities reduced by 19–33% and external costs reduced by 13–35%. In public transport, the use of private vehicles has decreased, while the volume of public passenger transport has increased. The number of road accidents was reduced by 20%, and the average waiting time during peak periods was reduced by 16%. Therefore, introducing the approach based on internalizing external costs can lead to an overall positive outcome in society's well-being [21]. By returning fee revenues to the economy through reductions in income taxes, production, employment, and economic growth will be stimulated. All these effects will outweigh the impact of increased transport prices.

Establishing an infrastructure accounting system in transport must focus on allocating responsibilities between different levels of state governance (local, regional, infrastructure managers, country). In order to assess the actual infrastructure costs properly, it is necessary to focus the efforts on coordination between the different transport and infrastructure operators and the institutions concerned to improve information security and statistics. In this context, it is imperative to implement appropriate and applicable policy rules and actions to provide cost data and other economic and social information for the transport sector. The measures of such a policy must be aimed at drawing up guidelines and proposals for legislation on the setting of transport infrastructure charges.

Appropriate actions in this direction are as follows:


#### **5. Conclusions**

The discussed approach to improving the infrastructure charging system in transport by internalizing external costs guarantees the effectiveness and linking of charges for the use of transport infrastructure with the relevant costs in all transport modes. Its implementation will increase the efficiency of the transport industry as a whole. Changing the charges by applying this approach will impact the level of infrastructure usage and lead to a higher level of cost recovery directly from users. Furthermore, the aim is not to increase or decrease charges for certain modes of transport but to justify the size of the different elements in setting them and the need for state subsidies [26]. Thus, better communication between transport infrastructure market participants will be achieved, and the actions and interests of each of them will be synchronized. This measure will create an opportunity to achieve the objectives related to improving transport infrastructure usage and sustainable development of the transport systems. In addition, significant economic and social effects will be achieved at the national level.

It is also necessary to improve existing infrastructure, ensure a shift to environmentally friendly modes of transport and use economic instruments to reduce fuel consumption, greenhouse gas emissions, and noise. The main objective of this is to increase the efficiency and sustainability of the countries' transport systems and to stimulate the competitiveness of the national economies.

The discussed approach is based on the following basic principles:


The internalization of external infrastructure costs applies to all modes of transport. However, the costs' content and values vary depending on the transport mode and the conditions for access to the infrastructure (e.g., time of day and place). Cost analysis by type of transport infrastructure shows insufficient information assurance concerning part of the marginal, respectively, variable costs. This fact means that it is impossible to accurately assess all marginal costs (internal and external), and acceptable approximations are required to establish a relatively objective basis for allocating costs and charges. The non-reflectance of external costs in infrastructure charges currently leads to a significant distortion and rebalancing of intermodal competition. On the other hand, this is also the reason for the application of charges, which reduce the efficiency of transport infrastructure and send wrong price signals to transport users.

#### **Sponsorship**

The research presented in the chapter is funded by an academic project № INI – KP-06-DК2/4 of 30.03.2021 supported by the Bulgarian National Science Fund of the Ministry of Education and Science.

#### **Author details**

Christina Nikolova University of National and World Economy, Sofia, Bulgaria

\*Address all correspondence to: hrnikolova@unwe.bg

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] OECD. External Costs of Transport in Central and Eastern Europe. Vienna: OECD Environment Directorate; 2010

[2] Essen H et al. Handbook on the External Costs of Transport: Version 2019 – 1.1. Directorate-General for Mobility and Transport, European Commission. Brussels: Publications Office; 2020. DOI: 10.2832/51388

[3] Wijngaarden L et al. Sustainable Transport Infrastructure Charging and Internalization of Transport Externalities: Executive Summary. Directorate-General for Mobility and Transport, European Commission. Brussels: European Commission; 2019. DOI: 10.2832/246834

[4] Monden R, El Beyrouty K, Gatto M, et al. Overview of Transport Infrastructure Expenditures and Costs. Directorate-General for Mobility and Transport, European Commission. Brussels: Publications Office; 2019. DOI: 10.2832/853267

[5] European Commission. White Paper: Fair Payment for Infrastructure Use: A Phased Approach to a Common Transport Infrastructure Charging Framework in the EU. Brussels: European Commission; 1998

[6] Beyrouty K et al. Transport Taxes and Charges in Europe: An Overview Study of Economic Internalization Measures Applied in Europe. Directorate-General for Mobility and Transport, European Commission. Brussels: Publications Office; 2019. DOI: 10.2832/ 416737

[7] LIBERAIL. Methodology for Determination of the Costs of Using the Infrastructure, Work Package 3. Brussels: LIBERAIL; 2001

[8] Doll C, Jansson J. User costs and benefits. In: Matthews B, Nash C, editors. Measuring the Marginal Social Costs of Transport. Amsterdam, Oxford: Elsevier; 2005

[9] Bickel P et al. Deliverable 11: Environmental Marginal Cost Case Studies. Leeds: UNITE: Institute for Transport Studies, University of Leeds; 2003

[10] Preiss P, Friedrich R, Klotz V. New Energy Externalities Development for Sustainability (NEEDS). Stuttgart: University of Stuttgart; 2008

[11] ITT. WP 6: ADB and Green Transport, Deliverable 6.1 External Costs of Transport in ADB Area: Lessons Learnt. Ljubljana: Institute of Traffic and Transport; 2013

[12] Proost S, Van Dender K. Marginal social cost pricing for all transport modes and the effects of modal budget constraints. Working Paper Series. 2003; **11**:1-34

[13] Quinet E, Vickerman R. Principles of Transport Economics. Cheltenham: Edward Elgar; 2004. pp. 134-146

[14] Bickel P. Environmental marginal cost case studies. In: Matthews B, Nash C, editors. Unification of Accounts and Marginal Costs for Transport Efficiency. Leeds: University of Leeds; 2003

[15] High-Level Group on Infrastructure Charging. Calculating Transport Environmental Costs. Brussels: Final Report of the Expert Advisors to the High-Level Group on Infrastructure Charging; 1999

[16] High-Level Group on Infrastructure Charging. Calculating Transport

Accident Costs. Working Group 3, Final Report of the Expert Advisors to the High-Level Group on Infrastructure Charging, 27 April 1999. Brussels: European Commission. p. 1999

[17] High-Level Group on Infrastructure Charging. Calculating Transport Infrastructure Costs. Working Group 1, Final Report of the Expert Advisors to the High-Level Group on Infrastructure Charging, 28 April 1999. Brussels: European Commission. p. 1999

[18] Miola A et al. Review of the Measurement of External Costs of Transportation in Theory and Practice. Maritime Transport. Report 1. Institute for Environment and Sustainability, Joint Research Centre. Brussels: Publications Office; 2011

[19] Brooks L, Liscow Z. Infrastructure costs. Washington: American Economic Journal: Applied. 2019:1-76. DOI: 10.2139/ssrn.3428675

[20] High-Level Group on Transport Infrastructure Charging. Final Report on Estimating Transport Costs, 26 May 1999. Brussels: European Commission; 1999

[21] Becker U, Becker T, Gerlach J. The True Costs of Automobility: External Costs of Cars - Overview on Existing Estimates in EU-27. Dresden: Technical University of Dresden; 2012

[22] Link H, Herry M, et al. Deliverable 10: Case studies on marginal infrastructure costs. In: Unification of Accounts and Marginal Costs for Transport Efficiency. Leeds: University of Leeds; 2002. p. 5

[23] Bossche M et al. Marginal cost methodology. In: Unification of Accounts and Marginal Costs for

Transport Efficiency. Leeds: University of Leeds; 2002

[24] Johansson P, Nilsson J. An Economic Analysis of Track Maintenance Costs. Deliverable 10: Annex A3. Unification of Accounts and Marginal Costs for Transport Efficiency. Leeds: University of Leeds; 2002. p. 6

[25] Nikolova C. Evaluating average external costs of inland freight transport in south-eastern European countries: Policy implications, economic. Alternatives Journal. 2015;**3**:64-81

[26] Institute of Traffic and Transport Ljubljana. WP 6: ADB and Green Transport, Deliverable 6.1 External Costs of Transport in ADB Area: Lessons Learnt. Ljubljana: Institute of Traffic and Transport; 2013. pp. 218-240

#### **Chapter 6**

### Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why Framework

*Ashtarout Ammar, Hala Nassereddine and Gabriel Dadi*

#### **Abstract**

The advent and spread of the COVID-19 pandemic shifted the world's focus toward investing in social structure projects that would improve urbanization and enhance equity. This shift compiled with the emergence of innovative technologies namely Digital Twins, allowed for investigating new approaches for designing and delivering infrastructures, thus paving the road toward smarter infrastructures. Smart infrastructures achieved by connecting the physical aspect of the infrastructure with its digital aspect will allow for optimizing the performance of infrastructure systems by digitally enhancing the asset value and leveraging the value of asset data. Digital Twins can be applied to several civil infrastructure projects including the transportation sector. Also, Digital Twins can be implemented for different spatial scales, on a national level, on the level of the city, and for a network of assets. Few case studies described how to transfer a Digital Twin vision to practice; thus, this chapter presents the journey for a holistic Digital Twin for a highway system formed of a network of assets by discussing the Why, How, and What framework. A holistic highway Digital Twin will allow for cross-asset data analysis, conducting predictive and preventive maintenance, and efficient resource allocation based on data-driven decision-making.

**Keywords:** digital Twins, data management, data management system, civil infrastructure systems, highway system, transportation assets

#### **1. Introduction**

In 2019, the world witnessed a global crisis caused by the emergence and spread of COVID-19 an infectious disease that caused the death of millions of people [1]. With the emergence of the pandemic, it was evident that the lack of focus on sustainable development goals (SDGs), especially the ones related to people and the environment, played an important role in the emergence and spread of infectious diseases, including COVID-19 [2]. The resulting chaos caused by the pandemic urged a shift in goals and investments toward health and infrastructure [3]. Thus, the pandemic has accelerated the shift toward social infrastructure projects targeting urbanization, healthcare, infrastructure, and Global Water, Sanitation, & Hygiene (WASH) projects, which will help cities to face future pandemics [4, 5]. This shift motivated governments to upgrade systems to achieve a more resilient and sustainable infrastructure ecosystem

and consider implementing and adopting technologies within the architectural, engineering, construction, and operation (AECO) industry [6, 7].

Some governments started this shift before the pandemic to help overcome the challenges of the AECO industry and improve the industry's digitization capability. For instance, the United Kingdom (UK) in their 2018 construction sector deal, a partnership between the government and the construction sector, dedicated £600 billion of investment in infrastructure including £31billion to boost digital construction and smart infrastructure [8]. Conversely, in the United States (US), in November 2021, the Infrastructure Investment and Jobs Act (IIJA) was officially legislated. The IIJA is a \$1.2 trillion investment, representing the "largest investment in the nation's critical infrastructure systems in a generation or more" [9]. The new law will allow for the investment of \$110 billion in funding for roads, bridges, and major infrastructure projects. This investment will support mitigating the impact of climate change, building resilient and sustainable infrastructure, enhancing equity, and improving safety for road users of all modes [10]. Moreover, the law dedicated \$100 million to fund the digital construction management systems and related technologies program. It is anticipated that this program will support the investment in technologies and tools including visualbased inspection technologies, construction management tools, electronic ticketing (e-ticketing), Digital Twins, and unmanned aerial vehicles (UAVs) [11].

Smart infrastructure—or the combination of the physical aspect of the infrastructure with its digital aspect—is a global opportunity worth £2 trillion to £4.8 trillion [12]. Smart infrastructures will allow for a better understanding of the performance of the existing infrastructure system, thus optimizing its efficiency and supporting the design and delivery of new infrastructure systems [12]. The concept of connecting the physical aspect of assets with its digital model aligns with the concept of Digital Twins, which was defined by several researchers as the digital representation of physical assets, or a digital model created to depict either an existing, ongoing, or future construction project and linked throughout its lifecycle [13, 14]. The authors of [14] highlighted the notion of the Digital Twins for the construction project lifecycle and emphasized the different capabilities of the technology including increased transparency of information, real-time monitoring, analysis, and feedback, better stakeholder collaboration, advanced preventive measures, advanced what-if scenario analysis and simulation, real-time tracking, and higher accuracy.

The concept of Digital Twins emerged with the Fourth Industrial Revolution, known as Construction 4.0 in the AECO industry. However, among the technologies under the umbrella of Construction 4.0, Digital Twins is the least researched in the industry [15] and, while its definitions and applications vary from one sector to the other, its framework is similar [12]. For smart infrastructures, the framework of Digital Twins can be presented as a pyramid formed of a base and three layers. The base is formed of raw data collected using several tools and methods including customer billing, sensors, drone surveys, laser surveys, building information modeling (BIM), geographic information system (GIS), and control systems, among others. On top of the base, resides the data management layer such as data storage, data cleaning, data structure, and other practices related to data management. The layer above data management is data analysis and interpretation or making sense of the existing data. Finally, the crown of the pyramid (i.e., the third layer) is decision-making. As such, to leverage the process of data-driven decision-making, it is essential to lower the data volume (i.e., layer 1) and increase the data value (i.e., layer 3) where the three layers are internally and externally connected by communicating information as presented in **Figure 1** [12].

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

**Figure 1.** *Digital Twins model for smart infrastructure (adopted from [12]).*

This chapter thoroughly investigates the existing research on Digital Twins in the infrastructure industry (Section 2), the applications of Digital Twins in the transportation sector (i.e., bridges, rails, tunnels, and highways) (Section 3), and the implementation of Digital Twins for different spatial scales in the civil infrastructure industry (i.e., national level, city level, and for a network of assets) (Section 4). Additionally, this chapter discusses the vision for a holistic Digital Twin for a highway system (Section 5) and summarizes major findings in a conclusion section (Section 6).

#### **2. Existing research on Digital Twins in the infrastructure industry**

Digital Twins originated in aerospace engineering; however, with the advancement in technology, the concept is no longer limited to complex systems and can be implemented to provide a digital representation of any system [16]. This feasibility encouraged several researchers from different industries to investigate the concept of Digital Twins. The increased focus on data and the opportunities created by digitization promoted the potential of the emerging phenomenon of Digital Twins in the manufacturing industry with the wave of Industry 4.0 to fulfill the requirements of smart factory [17–19]. The concept of Digital Twins then found its way to the oil and gas industry to optimize offshore operations, reduce risks to health, safety, and environment, and facilitate complex integrated processes [20]. Similarly, the construction industry began considering Digital Twins a key enabler for its digital transformation that could improve the industry's poor record in digitization [14, 21]. Moreover, the digital transformation of engineering assets increased the interest of both researchers and practitioners to investigate the implementation of the concept of Digital Twins in the civil infrastructure domain [22].

Several researchers investigated the implementation of Digital Twins in the infrastructure sector. The authors of [23] examined the current practices of implementing Digital Twins through a series of semi-structured interviews with experts and executives from the UK infrastructure industry. Their study found that the current implementations are still not mature and most of them are under development. These implementations included 3D modeling of physical assets, integration of real-time

weather forecasts, asset data and information projects, integration of different information systems coming from several organizations (in the construction phase), and contract management systems throughout the supply chain (in the construction phase), procurement of a network management system and its integration with existing systems, sensor data collection, implementation of common data environments, and modeling of systems and control philosophies. Similarly, the authors of [24] conducted a comprehensive review to investigate the implementation of Digital Twins within the civil infrastructure systems (i.e., transportation, energy telecommunication, water and waste, and smart cities). They thoroughly investigated the existing literature, in addition to surveying professionals and interviewing stakeholders. It was also found that the concept of Digital Twins in the infrastructure industry is still in the early stages of development. The authors also highlighted that the major adoption of Digital Twins within the civil infrastructure system was to optimize operations. Also, they emphasized that it is critical to investigate how Digital Twins can be retrofitted into existing infrastructure systems to improve the efficiency of their operation and maintenance. More recently, the authors of [22] conducted a systematic literature review to map the applications of Digital Twins within the road and rail system, telecommunication, and electricity networks. This study showed that the available studies are scarce and that the use of Digital Twins is mainly perceived for the operation and maintenance phase. It was also noted that while Digital Twins can leverage the value of asset information and, thus, improve the process of data-driven asset management decisions, the impact of Digital Twins on the development of asset management programs within infrastructure organizations should be addressed. Since the focus of this chapter is on transportation systems, a comprehensive review of the existing studies on the application of Digital Twins for transportation systems is discussed in the following section.

#### **3. Digital Twins applications for the transportation system**

The transportation system includes the sectors of bridges, railroads, tunnels, and highways. Transportation systems are critical for the development of any jurisdiction, they represent the arteries necessary for connecting people, delivering goods, and providing services for economic development. The failure of these systems can result in considerable economic losses [25]. To mitigate the risks of failure and to improve the management of such a critical and aging system, the transportation sector followed suit and investigated the implementation of Digital Twins with a wide variety of applications.

#### **3.1 Digital Twin applications for bridges**

Bridges are complex engineering systems and are considered high-value assets with a relatively extended life span; thus, their continuous maintenance to prevent their deterioration and mitigate the risks of their failure is always a priority for infrastructure organizations. As such, the ability to conduct preventive maintenance and lifecycle monitoring of bridges became an essential strategy in the bridge industry since it can support proactive measures to maintain the structural integrity of bridges throughout their entire lifecycle. The capability of Digital Twins to aggregate real-time data and historical data, support data analysis, and enable insights on preventive maintenance captured the attention of researchers in the bridge industry

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

to investigate the potential of implementing Digital Twins in their bridge design, construction, operation and maintenance, and deterioration [26, 27].

A Digital Twin solution to enhance bridge maintenance by integrating a maintenance information management system based on a 3D information model with a digital inspection system using image processing was proposed by the authors of [26]. The proposed model was validated for maintaining pre-stressed concrete bridges where it facilitated the detection of surface damages in a real-time manner, thus providing early warnings of any potential distress and enabling early remedial interventions. Additionally, a closed lifecycle fatigue management driven by Digital Twins for steel bridges was developed by the authors of [27]. The authors discussed the implementation mechanism of Digital Twins in the bridge design phase, the bridge construction phase, the bridge service and operation phase, and the bridge retirement phase. The authors noted that a Digital Twin-driven fatigue management system can support the integration of a diversity of fatigue data including historical fatigue information after the bridge retirement thus providing better insights for the design and management of new bridges.

#### **3.2 Digital Twin applications for railroads**

The railway system is no less important than bridges, which is also a complicated system made of several subsystems, where their maintenance and condition monitoring is critical to mitigate the risks and ensuring safety. Asset managers should have access to continuous information related to the railroad conditions to allow for the early detection of any surface abnormalities caused by temperature change, degradation, or component failure and to have the ability to automate and modify railway turnouts or railway switches automatically, in case of an emergency. In that manner, Digital Twins with its capability of integrating multi-sourced data such as data collected by sensors depicting the railroad conditions and weather conditions allows it to act as a centralized data source that can act as a traffic management and control system [28, 29]. The authors of [28] applied the concept of Digital Twins to monitor railway turnouts, a very complicated system by nature of design and construction. Sensors were used to measure the rail temperature promptly and cyclically to support the data acquisition of rail turnouts and to capture emergency conditions. This automated and continuous analysis of the rail components increased the efficiency of managing and maintaining railroad turnouts. Moreover, the concept of Digital Twins for the European Train Control System (ETCS) applications was investigated [29]. The Digital Twin served as a repository and universal simulation environment to support the automatic check and compliance of organizational and operational requirements, system requirement specifications, design principles, regulations governing railway operations, interfaces with the control-command basic layer system, and requirements for the verification process of the control-command subsystem. This resulted in a significant reduction in the duration and cost of validating the ETCS applications.

#### **3.3 Digital Twin applications for tunnels**

The development of smart cities and the advancement of available technologies enhanced the utilization of urban underground spaces to construct urban infrastructures such as tunnels. Tunnels are complicated spatial structures and have multiple electromechanical components, such as ventilation systems, necessary for

their operation. Digital Twins can support the operation and maintenance of tunnels and allow for lifecycle management analysis [30, 31]. A Digital Twins prototype of noise barrier tunnels (NBTs) was implemented to predict the life and condition of the tunnel components using numerical behavior analysis. Sensors were used to link the behavior of the physical model of the NBT (i.e., a digital model created using a 3D printer) and the digital model of NBT (i.e., a shape-generation script comprised the NBT shape-generation, component-shape generation, and component mapping and layout models) [30]. The implemented Digital Twins prototype allowed for the lifecycle management analysis, which can help reduce NBT installation costs, support the use of recycled materials, and make the process of installation more sustainable by identifying components that should be replaced at the early stages of the NBT design. Moreover, the authors of [31] proposed a Digital Twins-based decision analysis framework for the operation and maintenance of tunnels. The proposed framework defined the decision analysis and an extended Construction-Operations Building information exchange COBie standard-based organization method employed to define information for assets that are delivered as part of facility construction projects and used to document the data with BIM—and integrating data using semantic web technologies. The proposed framework was validated by operating and maintaining complex electromechanical systems such as fans that are used to eliminate harmful gases and control visibility inside the tunnel. The Digital Twin framework supported the early detection of any anomalies in the operation of fans and determining the fault causes. This allowed for taking timely maintenance interventions to ensure better operation of the tunnel.

#### **3.4 Digital Twin applications for highways**

Highways are less complicated when compared to other transportation systems in terms of their structure, nevertheless due to their expansion over a vast network and the use of vast quantities of materials their lifecycle management and material durability are always a concern. Multi-sourced data integration enabled by Digital Twins facilitates the prediction of highway materials performance, allows better visualization of key performance indicators (KPIs), and supports reliable and datadriven decision-making [32, 33]. A framework combining Digital Twins and multiple time series stacking (MTSS) was employed to predict the performance of highway tunnel pavement performance [32]. The Digital Twins is formed of three key components: (1) data collection module including pavement performance data collected by performance indicators such as sensors and high precision accelerometers, main maintenance records extracted from the operation and maintenance system, tunnel highway structure, traffic flow, and tunnel environment; (2) prediction model, where a pavement performance prediction model was developed based on multiple differentiated models generated to predict the future performance of selected pavement section; and (3) parametric analysis model, established in the dynamo environment to integrate the multisource spatial-temporal data of the BIM model and the physical assets. The proposed framework was validated using a case study, and it was found that the visuals provided by Digital Twins allowed for conducting preventive maintenance. Similarly, the authors of [33] established a fully functioning Digital Twin of a road constructed using secondary raw materials (SRMs). The model included structured geometric and attribute data related to SRMs such as material data, chemical and mineralogical composition, strength, and unstructured data including real-time material characteristics collected by sensors. The developed Digital Twins supported data centralization and allowed the graphical presentation of KPIs.

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

#### **3.5 Section summary**

Infrastructure projects are complicated construction projects with a lifespan that might expand for a couple of decades. It is well known in the construction industry that the cost of operation and maintenance can be up to two or three times the cost of the initial construction and that it can equate from 60 to 80% of the total lifecycle cost, with the extended lifecycle of infrastructure projects this cost can be significant [34]. Infrastructure asset management constitutes several processes that are data-intensive and necessitates continuous data collection and analysis to support decision-making. The emergence of Construction 4.0 technologies, mainly Digital Twins, facilitated by the availability of powerful and cheaper sensors and cyberphysical systems (CPS) and with the aid of computational technologies such as big data analytics, semantic web-based technologies, and the Internet of Things (IoT) coupled with the drive toward digitizing infrastructure assets and transforming them into "smart infrastructures" provided a more holistic and innovative approach for managing infrastructure assets and informing decision-making.

The use of Digital Twins for transportation systems was mainly implemented to help asset managers better operate and maintain complex and interconnected sub-systems, monitor the performance of different system components, enhance the visualization of information necessary to conduct preventive maintenance and detection of abnormalities, conduct lifecycle management analysis, and take proactive interventions to mitigate failure risks and enhance the system's structural integrity. Moreover, the capability of Digital Twins to act as a centralized hub of meta-data with multiple sources supported the establishment of a universal simulation environment and data repository, thus allowing asset managers to have access to reliable information and therefore allowing them to conduct informed and quality-based decisionmakings. However, the discussed studies showed that the implementation of Digital Twins for the transportation system is not mature yet. Most of the studies presented either a practical or conceptual Digital Twins framework that was driven to satisfy its purpose of solving an identified problem. No large-scale Digital Twin for transportation systems was employed where the full potential of Digital Twin is achieved, and their capabilities are optimized. Nevertheless, the proposed frameworks showed promising results toward adopting Digital Twins in the transportation sector.

#### **4. Digital Twins for different spatial scales in the civil infrastructure industry**

Sections 2 and 3 showed that Digital Twins can be used for many purposes. It can be used for potential future planning by running what-if simulations, and predictive and preventive maintenance management. Also, it can be implemented in the project's current state for operation and maintenance, real-time monitoring and control, and early detection of abnormalities to optimize the performance of assets. Moreover, it can act as an archive for historical data and provide key insights from past lessons. However, Digital Twins can be used for multiple purposes and can be employed for different spatial scales, that is, on the level of an asset, for a network of assets, for a system or city, and at a national level. As was mentioned in the previous sections, most of the applications of Digital Twins were for a specific purpose and on a small scale and this could pertain to the fact that the concept of Digital Twins in the civil infrastructure industry is not mature yet. Previous studies showed very promising

results, and this encouraged several governments, institutions, and organizations to explore the practicality of the concept and build a vision toward optimizing the potential of Digital Twins on a holistic spatial scale and investigate different strategies, frameworks, and roadmaps to bring the vision into action and transform the way of planning, verifying, delivering, and operating the built environment.

#### **4.1 Digital Twins on a national level**

The case studies on envisioning the implementation of Digital Twins on a national level are very limited since this is a huge investment and requires the collaboration of several stakeholders. However, the UK is leading in this initiative and the government generated the Industrial Strategy Transforming Construction Programme and established the Center for Digital Built Britain (CDBB) at the University of Cambridge to set definitions and principles across the built environment to develop a roadmap toward a National Digital Twin (NDT); an ecosystem of connected Digital Twins of physical assets to leverage the use of asset data for the benefit of the public [35]. The vision for Digital Built Britain is to "enhance the natural and built environment, thereby driving up commercial competitiveness and productivity as well as quality of life and wellbeing for the public. This will be achieved through better planning, delivery and whole-life management of infrastructure and the wider built environment—enabled by mustering the full power of the information value chain" [13]. It is perceived that the use of an information management framework and an NDT in a coordinated and considered manner will support the release of data from isolated silos and enable the creation of a centralized data repository, resulting in an additional £7 billion/year of benefits across the UK infrastructure sectors [36]. The road map for delivering the information management framework can be addressed by answering the following questions [13]:


To guide the development of the framework and the NDT, CDBB defined the Gemini Principle as the conscience of the framework identified by nine principles and distributed over three clusters (1) purpose, (2) trust, and (3) function [13]. The NDT should have a clear purpose and must be used to provide good to the public, enable value creation and improve performance, and must provide insights into the built environment. Additionally, it should be trustworthy to the public; otherwise it will lose value, data sharing should be open as possible but at the same time should

be secured to ensure its integrity and should rely on quality data. Finally, the NDT should function effectively in support of its purpose, must be based on standards with clear data ownership and data governance, and should be flexible to develop and adopt any technological or system evolutions in the future.

#### **4.2 Digital Twins on a city level**

Historically, Singapore faced several challenges while planning its built environment to provide a better quality of life to its residents considering that its area is 728.6 km2 and its urban density is the third worldwide. To face these challenges, the government of Singapore with the aid of several authorities and research foundations developed "Virtual Singapore: A Digital Twin for Planning," a platform providing a dynamic 3D virtual model of the urban areas of Singapore. The government used 2D maps to solve urban challenges; however, with the emergence of 3D models they realized the potential of 3D data in offering planners a more comprehensive platform to design and pilot urban solutions. This innovative program was operated with three strategic objectives, (1) to consolidate research in 3D data, (2) to develop an operational 3D city model and data platform that integrates BIM, 3D GIS, and simulation for planning use by researchers, citizens, and authorities, and (3) develop a 2D/3D Digital Twin for Singapore that offers planners and citizens tools to examine spatial data and test-bed concepts, and observe the impacts of projects in a "Virtual Singapore" before delivery in the real world [37]. The applications of Virtual Singapore include flood risk analysis, the potential for solar panels and green roofs, and monitoring the impact of wind load on vegetation [37].

Similar to Singapore, the city of Vienna has been experiencing continued growth and demand for new buildings, pressuring the city to issue thousands of new building permits every year. The process of building validation and verification is sophisticated and prone to the loss of information between the building authority and the planners and investors. Digitizing this process and using BIM at the early stages of design and being aware of the benefits of Digital Twins will allow building owners to have a Digital Twin model for their facility that they can use throughout the facility lifecycle and will provide authorities with an efficient building verification and a permission process. For that purpose, the city of Vienna worked with TU Wein and experts from different engineering and architectural firms and consultants on a project titled BRISE-Vienna, an openBIM-based building submission process aiming to integrate the building authority into the Digital Twin of a construction project throughout its lifecycle [14]. The development and maintenance of a Digital Twin for all buildings provide the building authority access to up-to-date Digital Twins throughout the phases of the building lifecycle. The sum of the Digital Twins of all buildings results in a Digital Twin of the city, named an Urban Digital Twin (UDT). UDT creates new opportunities for strategic considerations (urban mining, area analysis, and fire protection analysis) and further research activities (data basis for AI training and thermal simulation). Hence, UDT allows the city to perform advanced what-if scenario analysis and simulations to simulate the change of power supply or heating systems in a whole area for instance (i.e., change to district heating) [14].

#### **4.3 Digital Twins for a network of assets**

The government in the UK is very ambitious about being the world leader in shaping the future of infrastructure; for that purpose, they set their vision for Digital


#### **Table 1.**

*Core themes for UK Digital Road Vision 2025.*

Roads from 2020 to 2025 and are working on the longer vision of 2050. It is perceived that Digital Roads will harness data, technology, and connectivity to enhance the way the strategic road network (SRN) is designed, constructed, operated, and used. This will allow for safer traveling, faster delivery, and an optimized customer experience [38]. The vision toward Digital Roads is built based upon three core themes as presented in **Table 1** [39].

By the same token, several state Departments of Transportation (DOTs) in the US followed suit and investigated how they can embrace Digital Twins to leverage the value of enterprise asset information to conduct daily operations, monitor performance, and provide transparency to the public. For instance, Utah DOT (UDOT) envisioned Digital Twins as an information management strategy to connect enterprise asset information to a geospatial model of individual physical assets. Digital Twins is foreseen to support the documentation of the planned and as-constructed (as-built) updates and therefore to fill the gap in the information across project development for priority assets, thus ensuring the collection of the necessary information to document asset histories and proper governance of the asset current state [40, 41]. Moreover, at the organizational level, the Digital Twins of the transportation asset systems will provide a single source of reliable, real-time information that will be used across different divisions of the department and the public enabling UDOT to perform complex analyses and make holistic decisions to improve safety, enhance mobility, and preserve the transportation infrastructure [41]. Additionally, UDOT identified several tactical goals with a two-year horizon considered high-value activities with no pre-requisites, and strategic goals with a five-year horizon which are also considered high-value activities with pre-requisites and requires further collaboration. The identified tactical and strategic goals required for the achievement of the overarching objective of adopting Digital Twins for infrastructure assets are summarized in **Table 2** [41].

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*


#### **Table 2.**

*Tactical and strategic goals identified by the Utah Department of Transportation for a successful implementation of Digital Twins for infrastructure assets.*

#### **4.4 Section summary**

Envisioning Digital Twins on a large spatial scale is starting to sound appealing among governments, institutions, and organizations. Few envisioned implementing Digital Twins on a national level, others proposed its implementation to face the challenges associated with planning urban environments in developed cities, while some intended to use it for a network of infrastructure assets to digitize the design, construction, and operation of transportation networks or to manage the data of priority assets and fill the gap of the enterprise information.

Each presented vision started by identifying the purpose behind adopting Digital Twins for infrastructures and they established a set of certain principles, themes, strategic objectives, or goals that they considered necessary for achieving the overarching objective of this adoption. Also, they discussed the expected benefits resulting from this implementation. However, there is a lack of pilot projects or actual studies that implemented Digital Twins and explained the transition from vision to action. In the next section, we are going to discuss the vision toward a holistic Digital Twin for a highway system and to further understand how to move from concept to practice, a case study is presented.

#### **5. Journey for a holistic Digital Twin for a highway system—the why, what, and how**

A vision for a holistic Digital Twin is initiated by identifying the purpose by answering the *Why* component, or in other words, *Why is a holistic Digital Twin for a* 

**Figure 2.** *The why, how, and what components of a holistic Digital Twin for a highway system.*

*highway system needed*? where a clear statement describing the purpose is generated. After declaring the overall purpose, a set of principles, themes, strategies, or goals are formed for setting the *How* component, that is, the process, or in other words, *How can the vision for a holistic Digital Twin for a highway system be achieved?*, which can be described by setting short- and long-term objectives that should be achieved to fulfill the overarching objective for adopting a holistic Digital Twin. Finally, *What* component should be investigated, that is, *What is the outcome resulting from the implementation of a holistic Digital Twin for a highway system?* The Why, How, and What components of a holistic Digital Twin for a highway system are presented in **Figure 2***.* In the following sub-sections, the three research questions will be addressed.

#### **5.1 Why the need for a holistic Digital Twin for a highway system**

A highway asset system within a state Department of Transportation (DOT) can be classified into three categories: bridges, pavements, and ancillary assets. Usually, state DOTs prioritize the management of high-value assets and highly-visible ones such as pavements and bridges. However, transportation systems extend beyond pavement and bridges to include a wide variety of ancillary assets [42]. Ancillary assets represent a significant investment of public funds and many are essential for the safe and efficient operation of highway facilities including, for instance, access ramps, guardrail end treatments, pavement markings, signs, culverts, drain inlets and outlets, communication systems, and intelligent transportation system (ITS), among others. DOTs are responsible for managing highway assets; thus, they are responsible for collecting, storing, managing, and analyzing vast amounts of asset data to support the process of transportation asset management (TAM) [43]. Every year state DOTs conduct hundreds of projects and make changes to existing ones. New construction projects require the generation of new sets of data to be included in the current database. On the other hand, reconstruction, rehabilitation, asset demolition, or other major maintenance activities require revising and updating the database. All changes conducted for assets through the project execution and its whole lifecycle should be collected accurately and promptly to ensure effective TAM and proper operation and maintenance (O&M) [44]. However, state DOTs are facing

#### *Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

several challenges integrating data across systems and throughout the asset lifecycle [45], and they need to focus on six major data management practices that are data collection, data handling, data flow, data transfer, data governance, and data integration to achieve a seamless data management approach and improve the value of data to enable informed decision-making [46].

Conversely, with the emergence of technologies and the notion of data-driven decision-making, where data itself is considered a high-value asset, state DOTs changed their perspective on operational performance where they focused more on operating and maintaining the existing transportation system instead of expanding it, and thus, they are investigating new approaches to manage and operate their transportation assets [47]. However, operating and maintaining existing assets is a sophisticated process and requires complicated decision-making since state DOTs need to identify for each asset a defined management strategy, select priority assets, and make cross-asset resource allocation decisions that would consider multiple objectives [42]. Moreover, transportation asset management requires the existence of spatial metadata with heterogeneous data features coming from multi-sources, in addition to data throughout the asset lifecycle, that is, asset history (e.g., as planned asset data), the current state of the asset (e.g., as-constructed asset data), and realtime data about the asset condition to allow for asset condition monitoring and control. Given the relatively extended life of assets, state DOTs need to use tools and technologies that will enable them to access reliable and informative data to support a lifecycle management approach and adopt data management approaches that can evolve with the evolution of technologies and future data requirements.

Furthermore, asset management can be explained as the task of connecting the fundamental mission of an organization of operating the infrastructure, that is, connecting the digital aspect of the asset and its physical aspect to ensure better asset operation and maintenance and support decision making [48]. This concept of asset management of connecting the physical and digital aspects of assets intersects with the concept of Digital Twins for infrastructure [40]. As such, with the capabilities of Digital Twins in supporting decision-making by providing one source of data registry and a simulation environment to allow for the conduction of prognostic and diagnostic maintenance, in addition to its ability to integrate multi-sourced data and provide enhanced data visualizations, the concept of Digital Twins can be adopted by state DOTs to transform their way of designing, delivering, and operating and maintaining their transportation assets. However, to optimize the use of the concept of Digital Twins, a holistic Digital Twin for the highway system is necessary, since a globalized highway Digital Twin includes informative data about all assets constituting a highway ecosystem and how they interact with each other and with the surrounding environment, thus allowing state DOTs to have better insights toward managing cross-asset systems and make decisions on resource allocation to optimize the performance of cross-asset systems, and the overall highway system.

#### **5.2 How to achieve the vision for a holistic Digital Twin for a highway system**

After identifying *Why* is a holistic Digital Twin for a highway system is needed, it is important to understand how the vision can be translated into action. The best way to fulfill this understanding is to learn from leading organizations that succeeded in putting visions into practice. A case study is a form of qualitative research that offers an in-depth examination of the topic [48]. The case study presented in this section started as a vision where the facility management department within the Sydney

Opera House imagined having a 3D model of the facility where they can fly around, select different assets, and view all the related information in one system. This vision was put into practice, where a Digital Twin providing a single source of information for regular building operational requirements and ongoing projects was created.

#### *5.2.1 Digital Twins from vision to action: a facility management case study*

The Sydney Opera House was added to UNESCO's World Heritage List in 2007 and is a multi-venue performing art center located at Sydney Harbor in Sydney, New South Wales, Australia. It is one of the country's most iconic and distinctive buildings. The building welcomes more than 8.2 million visitors a year, presenting more than 2000 shows 363 days a year for more than 1.5 million people [49]. The management and maintenance of the building are very challenging because of the special purpose that the building serves and the required technicalities, for instance, when the Sydney Symphony Orchestra is on stage in the concert hall, the room temperature must be maintained at 22.5 degrees to ensure the instruments stay in tune [50]. The building has more than 1000 rooms and managing the use of spaces is dynamic. For instance, spaces might be used for other purposes such as using lifts as dressing rooms for certain shows, partitioning different rooms, or merging spaces to expand the capacity and make use of the added space. The budget for the yearly maintenance of the Sydney Opera House amounts to \$30 million AUS (equivalent to £16.5 million or \$20.7 million) [51], thus aiming to reduce this high cost, the managers of the Sydney Opera House started investigating innovative solutions to support them in achieving their objective.

As such, in 2013 the BIM Academy (one of the world's leading research and strategic consultants in the global digital built environment established by Northumbria University and Ryder Architecture) won a worldwide tender for the contract to write a detailed BIM strategy to support the facility management (FM) of the Sydney Opera House. Writing the technical piece of the BIM-based facility management required a comprehensive review and thorough investigation of the existing disparate systems that were used to operate the building, methods of documentation, and the current approaches to information modeling. More than 350 interviews with the Sydney Opera House facility managers and employees were conducted to establish a roadmap and write a detailed technical specification for the BIM for FM interface that would connect the facility existing data and future data to the BIM model. The technical specification identified the requirements, developed a model management plan describing the process of developing the 3D models on site, and identified the information that should be included. This phase of the project facilitated the achievement of the second phase which is delivering the Digital Twins platform of the Sydney Opera House.

For the second phase of the project, the BIM Academy collaborated with AECOM (a global multidisciplinary consultancy), and EcoDomus (a leading software developer), to tender and subsequently win the delivery of a Digital Twin-enabled facility management platform for the Sydney Opera House. Alongside that, they won a bid to reformat their documentation system and their spatial management system by expanding their capacity to support the newly developed system. This phase of the project was executed in two stages. The first stage involved recouping and integrating information from the existing database with the newly established database within the 3D model. The second stage introduced a broader range of functional systems that can be added to the BIM interface over time. The aim was to improve the existing Technical Document database (TDOC) and develop a new spatial record management system. The new system encompasses the 3D model and will act as a parent system for

#### *Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

all other sub-systems such as the document management system, spatial management system, asset register, and condition management system, and will support the establishment of one source of true data. TDOC will improve efficiency by eliminating wasted efforts and providing usability improvements. Changes such as updating the types of information that could be added to a document, editing information, adding functionalities to edit significant numbers of documents in bulk, and updating the system became more feasible and can be done promptly.

The vision of delivering a Digital Twin platform for the Sydney Opera House was thus achieved over two phases where each phase had several stages. Business analysis was first conducted, followed by comprehensive, structured research and a review of existing information systems and technical and IT infrastructure. Next, integration for systems was designed and innovative potential solutions were solicited. Then, model management was planned and a technical specification with a road map was established. Finally, Digital Twin delivery and implementation were achieved, and user approval, training, and testing were accomplished. The created Digital Twin platform enabled facility managers to have access to reliable geometry and to operate the building easily. Moreover, the platform is not only used by facility managers, but it can also be used by marketing teams to organize events and design and plan seats; security teams to plan logistics around new events; and site management, for instance, removing large bits of equipment and implementing others. The Sydney Opera House implemented the Digital Twin platform as an innovative solution to have better access to asset information, so they can have a diagnostic approach and solve problems promptly. It is expected that the feasible access to information would save on average 30 minutes per work order, for instance, if they have 20,000 work orders per year with an average cost of \$50 per hour, they can save up to \$500,000 per year. In the future, the Sydney Opera House is envisioning optimizing the use of the Digital Twin platform to support solving any future problems that they might face and keep updating the platform to manage any additional subsystems or any future advancement in data and technologies.

#### *5.2.2 Digital Twins from vision to action: case of a highway system*

State DOTs, responsible for managing and maintaining highway assets, have created, collected, and stored transportation asset data; however, the available data structures are not consistent, and they are either unstructured data (e.g., documents), semi-structured (e.g., excel data sheets), or structured data (e.g., databases). State DOTs are trying to improve their digitization capacity by collecting data in digital formats and abandoning the use of papers, improving the quality of collected data, and using automated and remote techniques to collect asset data [40]. However, data exist in isolated silos, with no further understanding of how these data should be integrated and managed. Therefore, to achieve the vision of Digital Twins for a highway system, state DOTs need to put a set of objectives and goals as a roadmap toward a holistic highway Digital Twin. Based on key takeaways from the presented Sydney Opera House case study and previous studies, state DOTs need to consider the following proposed objectives that should be considered for a successful implementation of Digital Twins for a highway system. These objectives include the following:

1.**Improving digital capacity.** State DOTs need to consider having a full data digitization lifecycle from "cradle to cradle" by enhancing digital design and construction, digital operation, and digital data integration with users. Designs should be enabled digitally, integrate automated construction when possible,

and adopt modular design and construction approaches. Moreover, enhancing the digital skills of workers to allow for smart asset management and support customer engagement by improving the digital capabilities of operations.


#### **5.3 What is the outcome resulting from the implementation of a holistic Digital Twin for a highway system**

Implementing Digital Twins for the highway system will provide an innovative smart management system that integrates asset semantic and geometric data, in addition to spatial data based on the asset location and the surrounding environment. This based Digital Twin management system can support the monitoring and control of the asset condition promptly, employ an advanced machine-learning algorithm to predict the asset condition, and allow for conducting preventive and predictive maintenance.

The developed Digital Twin-based management system will allow for the integration of data extracted from BIM and GIS. BIM can provide rich geometric and semantic asset data including but not limited to asset models (i.e., available 2D models or 3D models), asset specifications, required level of details, asset documentation, data schemes, and ontologies. Additionally, GIS can integrate many types of data while analyzing the spatial location of the asset and organizing layers of information into visualizations using maps and 3D scenes. Moreover, GIS can handle and process spatial data of the individual physical asset, system of assets, and the surrounding environment. The integration of asset data extracted from BIM and GIS can provide a digital representation of the asset architectural entity and will support the management of spatial information of the asset and the surrounding environment, thus providing a better understanding of how the individual physical asset or system of assets interacts with its surrounding [52].

A Digital Twin can reflect the asset condition in a real-time manner; data collected using sensors or cyber-physical-systems (CPS) can also be integrated to allow for comprehensive control and monitoring of the asset or system of assets condition, thus allowing for the early detection of abnormalities and therefore enhancing preventive maintenance. Moreover, the Digital Twin platform can enable the conduction of what-if simulations by utilizing the digitally enhanced asset models, aggregated asset historical data, real-time data, and data related to factors that might affect the asset performance, for instance, data related to the temperature of the surrounding

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

environment, thus allowing for predictive maintenance by simulating the future asset condition based on multi-sourced high quality and reliable data.

The Digital Twin-based management system will result in creating one source of true data, and the generation of a repository environment with simulation capabilities by making use of existing databases and systems. This data management system will release data from isolated silos, improve visualizations, enhance safety, optimize asset performance, allow for better resource allocation, and support data-driven decision-making.

#### **6. Conclusions**

Governments and organizations worldwide are investigating innovative approaches to change the design and delivery of civil infrastructure systems, with the ultimate objective of constructing and operating more resilient and sustainable infrastructure that would support equity, enhance safety, and target urbanization. This aim was further emphasized with the shift toward digitizing assets to leverage the value of asset data and optimize the performance of infrastructure assets. As such, the concept of smart infrastructure emerged, that is, connecting the physical aspect of the assets with its digital aspect by the bidirectional communication of information. Smart infrastructure also aligns with the concept of Digital Twins, or the digital representation of physical assets. Given the potential of Digital Twins, several organizations, researchers, and practitioners investigated the implementation of the concept in the civil infrastructure industry.

The concept of Digital Twins in the civil infrastructure industry is not mature yet; however, the adoption of the concept was investigated to address several problems related mainly to the operation and management of infrastructure systems. Few studies have investigated the implementation of Digital Twins throughout the lifecycle of infrastructure projects, but these studies mainly presented a framework for implementation. Additionally, Digital Twins can be applied at different spatial scales including the national level such as the National Digital Twin (NDT) initiative by the UK, on the city level to help with urban planning such as Virtual Singapore and BRISE-Vienna, or for a network of assets such as the vision for Digital Roads or the implementation of Digital Twins as a management information system.

This chapter also presented the journey toward a holistic Digital Twin for a highway system. The Why, How, and What components were investigated. A holistic Digital Twin will support managing cross-asset systems, and how they interact with each other and with the surrounding environment. Moreover, to understand how to translate the vision to practice, a case study related to the implementation of BIM for facility management of the Sydney Opera House was presented and recommendations on implementing a holistic Digital Twin for a highway system were discussed. Finally, data integration between BIM and GIS with the aid of the integration of real-time data about the asset condition, and employment of machine learning algorithm was proposed for a successful implementation of a Digital Twin-based management system that can integrate existing subsystems and make use of existing databases. The proposed smart data management system will allow for conducting preventive and predictive maintenance, support visualization, release data from isolated silos, allow for feasible access to data by creating one source of true data, and enhance decisionmaking to optimize the overall performance of the highway system.

#### **Acknowledgements**

The authors would like to thank Dr. Graham Kelly, director at BIM Academy for providing information about the presented case study.

### **Author details**

Ashtarout Ammar\*, Hala Nassereddine and Gabriel Dadi University of Kentucky, Lexington, KY, USA

\*Address all correspondence to: ashtrout.ammar@uky.edu

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

#### **References**

[1] WHO Coronavirus (COVID-19) Dashboard [Internet]. World Health Organization. 2022. Available from: https://covid19. who.int/ [Accessed: 8 August 2022]

[2] Tonne C. Lessons from the COVID-19 pandemic for accelerating sustainable development. Environmental Research. 2021;**193**:110482

[3] Ogunbiyi D. Opinion: Power in a pandemic—Why energy access matters during coronavirus [Internet]. 2020. Available from: https://news.trust. org/item/20200331134807-w6a0h/ [Accessed: 8 August 2022]

[4] Saldinger A. AIIB launches health infrastructure investments in response to COVID-19 [Internet]. 2020. Available from: https://www.devex.com/news/ aiib-launches-health-infrastructureinvestments-in-response-to-covid-19-96958 [Accessed: 8 August 2022]

[5] Cheshmehzangi A. Revisiting the built environment: 10 potential development changes and paradigm shifts due to COVID-19. Journal of Urban Management. 2021;**10**(2):166-175

[6] Balasubramanian S, Shukla V, Islam N, Manghat S. Construction industry 4.0 and sustainability: An enabling framework. IEEE Transactions on Engineering Management. 2021:1-19. DOI: 10.1109/TEM.2021.3110427

[7] Forcael E, Ferrari I, Opazo-Vega A, Pulido-Arcas JA. Construction 4.0: A literature review. Sustainability. 2020;**12**(22):9755

[8] HM Government. Industrial Strategy Construction Sector Deal [Internet].

London, United Kingdom. 2018. Available from: https://assets.publishing. service.gov.uk/government/uploads/ system/uploads/attachment\_data/ file/731871/construction-sector-dealprint-single.pdf

[9] ASCE. ASCE Statement on President Biden Signing the Infrastructure Investment and Jobs Act into Law [Internet]. American Society of Civil Engineers. Washington, DC, United States. 2021. Available from: https:// www.asce.org/publications-and-news/ civil-engineering-source/society-news/ article/2021/11/15/asce-statementon-president-biden-signing-theinfrastructure-investment-and-jobsact-into-law?utm\_campaign=GR-2022-5-20-TWiW%20Email%20 Short&utm\_medium=email&utm\_ source=Eloqua [Accessed: 20 July 2021]

[10] Updated Fact Sheet: Bipartisan infrastructure investment and jobs act [Internet]. The White House. 2021. Available from: https://www. whitehouse.gov/briefing-room/ statements-releases/2021/08/02/ updated-fact-sheet-bipartisaninfrastructure-investment-and-jobsact/#:~:text=The%20bipartisan%20%20 Infrastructure%20Investment%20%20 and%20Jobs%20Act%20will%20%20 invest%20%24110,of%20committee%20 %20earlier%20this%20year [Accessed: 20 July 2021]

[11] Obando S. Construction Techies Laud \$100M in Infrastructure Act, Push for More [Internet]. Construction Dive. Washington, D.C., United States. 2021. Available from: https:// www.constructiondive.com/news/ construction-tech-praise-110 million-infrastructure-bill-push-formore/610126/

[12] Bowers KA, Buscher, Dentten R, Edwards MB, England J, Enzer M, et al. Smart Infrastructure Getting more from strategic assets. [Internet]. Centre for Smart Infrastructure and Construction. 2016. Available from: https://wwwsmartinfrastructure.eng.cam.ac.uk/ system/files/documents/the-smartinfrastructure-paper.pdf

[13] Bolton A, Butler L, Dabson I, Enzer M, Evans M, Fenemore T, et al. Gemini principles [Internet]. Apollo—University of Cambridge Repository. 2018. Available from: https://www.repository.cam.ac.uk/ handle/1810/284889 [Accessed: 11 December 2021]

[14] Ammar A, Nassereddine H, AbdulBaky N, AbouKansour A, Tannoury J, Urban H, et al. Digital Twins in the construction industry: A perspective of practitioners and building authority. Frontiers in Built Environment. 2022;**8**:834671

[15] Ammar A, Nassereddine H. Blueprint for construction 4.0 technologies: A bibliometric analysis. IOP Conference Series: Materials Science and Engineering. 2022;**1218**(1):012011

[16] Niederer SA, Sacks MS, Girolami M, Willcox K. Scaling Digital Twins from the artisanal to the industrial. Nature Computational Science. 2021;**1**(5): 313-320

[17] Negri E, Fumagalli L, Macchi M. A review of the roles of Digital Twin in CPS-based production systems. Procedia Manufacturing. 2017;**11**:939-948

[18] Kritzinger W, Karner M, Traar G, Henjes J, Sihn W. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine. 2018;**51**(11):1016-1022

[19] Tao F, Qi Q, Wang L, Nee AYC. Digital Twins and cyber-physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering. 2019;**5**(4):653-661

[20] Wanasinghe TR, Wroblewski L, Petersen BK, Gosine RG, James LA, De Silva O, et al. Digital Twin for the oil and gas industry: Overview, research trends, opportunities, and challenges. IEEE Access. 2020;**8**:104175-104197

[21] Brilakis I, Pan Y, Borrmann A, Mayer HG, Rhein F, Vos C, et al. Built environment Digital Twinning. In: Report of the International Workshop on Built Environment Digital Twinning Presented by TUM Institute for Advanced Study and Siemens AG. Germany: Technical University of Munich; 2019

[22] Vieira J, Poças Martins J, Marques de Almeida N, Patrício H, Gomes MJ. Towards resilient and sustainable rail and road networks: A systematic literature review on Digital Twins. Sustainability. 2022;**14**(12):7060

[23] Broo DG, Schooling J. Digital Twins in infrastructure: Definitions, current practices, challenges and strategies. International Journal of Construction Management. 2021:1-10. DOI: 10.1080/15623599.2021.1966980

[24] Callcut M, Cerceau Agliozzo JP, Varga L, McMillan L. Digital Twins in civil infrastructure systems. Sustainability. 2021;**13**(20):11549

[25] Costin A, Adibfar A, Hu H, Chen SS. Building information modeling (BIM) for transportation infrastructure—Literature review, applications, challenges, and recommendations. Automation in Construction. 2018;**94**:257-281

[26] Shim CS, Dang NS, Lon S, Jeon CH. Development of a bridge maintenance

*Perspective Chapter: Roadmap to a Holistic Highway Digital Twin – A Why, How, and Why… DOI: http://dx.doi.org/10.5772/intechopen.108546*

system for prestressed concrete bridges using 3D Digital Twin model. Structure and Infrastructure Engineering. 2019;**15**(10):1319-1332

[27] Jiang F, Ding Y, Song Y, Geng F, Wang Z. An architecture of lifecycle fatigue management of steel bridges driven by Digital Twin. Structural Monitoring and Maintenance. 2021;**8**(2):187-201

[28] Kampczyk A, Dybeł K. The fundamental approach of the Digital Twin application in railway turnouts with innovative monitoring of weather conditions. Sensors. 2021;**21**(17):5757

[29] Kochan A. Digital Twin concept of the ETCS application. WUT Journal of Transportation Engineering. 2020;**131**: 87-98

[30] Kim J, Kim SA. Lifespan prediction technique for Digital Twin-based noise barrier tunnels. Sustainability. 2020;**12**(7):2940

[31] Yu G, Wang Y, Mao Z, Hu M, Sugumaran V, Wang YK. A Digital Twinbased decision analysis framework for operation and maintenance of tunnels. Tunnelling and Underground Space Technology. 2021;**116**:104125

[32] Yu G, Zhang S, Hu M, Wang YK. Prediction of highway tunnel pavement performance based on Digital Twin and multiple time series stacking. Lin JR, editor. Advances in Civil Engineering. 2020;**2020**:1-21

[33] Meža S, Mauko Pranjić A, Vezočnik R, Osmokrović I, Lenart S. Digital Twins and road construction using secondary raw materials. Biancardo SA, editor. Journal of Advanced Transportation. 2021;**2021**:1-12

[34] Rounds D. Design for maintainability: The importance of operations and maintenance considerations during the

design phase of construction projects [Internet]. Whole Building Design Guide. 2018. Available from: https://www.wbdg. org/resources/design-for-maintainability [Accessed: 20 August 2022]

[35] National Infrastructure Commission. Data for the Public Good [Internet]. London, United Kingdom. 2017. Available from: https://nic.org.uk/app/ uploads/Data-for-the-Public-Good-NIC-Report.pdf

[36] Deloitte. New technologies case study: Data sharing in infrastructure [Internet]. London, United Kingdom. 2017. Available from: https://nic.org. uk/app/uploads//Data-sharing-ininfrastructure.pdf

[37] Ider Batbayar. Virtual Singapore: A Digital "Twin" For Planning [Internet]. 2022. Available from: https://city2city. network/virtual-singapore-digital-twinplanning-innovation-type-institutionalpioneer [Accessed: 10 August 2022]

[38] National Highways. Digital Roads [Internet]. Birmingham, United Kingdom. 2021. Available from: https://nationalhighways.co.uk/ media/2chotw13/introducing-digitalroads.pdf

[39] National Highways.Digital Roads 2025 Roadmap [Internet]. Birmingham, United Kingdom. 2021. Available from: https://nationalhighways.co.uk/media/ r3nn1h3m/digital-roads-2025-interactiveroadmap\_final.pdf

[40] Ammar A, Nassereddine H, Dadi G. State departments of transportation's vision toward Digital Twins: Investigation of roadside asset data management current practices and future requirements. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2022;**V-4-2022**:319-327

[41] Utah Department of Transporation (UDOT). Digital Twin Strategic Plan [Internet]. Taylorsville, Utah, United States. 2021

[42] AASHTO TAM Guide [Internet]. American Association of State Highway Transportation Officials (AASHTO). Washington, D.C., United States. 2022 Available from: https://www.tamguide. com/guide/

[43] Federal Highway Administration (FHWA). Handbook for Including Ancillary Assets in Transportation Asset Management Programs [Internet]. Washington, DC, United States. 2019. Available from: https://www. fhwa.dot.gov/publications/research/ infrastructure/19068/19068.pdf

[44] Le T, Le C, David Jeong H. Lifecycle data modeling to support transferring project-oriented data to asset-oriented systems in transportation projects. Journal of Management in Engineering. 2018;**34**(4):04018024

[45] National Academies of Sciences, Engineering, and Medicine. Guidebook for Data and Information Systems for Transportation Asset Management. Washington, DC, United States: The National Academies Press; 2021. DOI: 10.17226/26126

[46] Ammar A, Dadi G, Nassereddine H. Transportation asset data management: BIM as a holistic data management approach. In: Construction Research Congress 2022 [Internet]. Arlington, Virginia: American Society of Civil Engineers; 2022. pp. 208-217. Available from: http://ascelibrary. org/doi/10.1061/9780784483954.022 [Accessed: 8 August 2022]

[47] Rahn PK. State DOT Mission Evolution. American Association of State Highway and Transportation Officials

(AASHTO). Washington, DC, United States. 2013. p. 30 Report No.: NCHRP Project 20-24 (84)

[48] Marczyk GR, DeMatteo D, Festinger D. Essentials of research design and methodology. Hoboken, New Jersey, United States: John Wiley & Sons; 2010

[49] Sydney Opera House [Internet]. 2022. Available from: https://www. gatherlearning.com/institutions/sydneyopera-house [Accessed: 12 August 2022]

[50] Interesting Facts About the Sydney Opera House [Internet]. 2022. Available from: https://www.sydneyoperahouse. com/our-story/sydney-opera-house-facts. html [Accessed: 12 August 2022]

[51] Elaine Knutt. Sydney Opera House Trials New BIM for F.M. Service [Internet]. 2014. Available from: https:// www.bimplus.co.uk/sydney-ope5rahous2e-tria1ls-new-bim-fm-service/ [Accessed: 12 August 2022]

[52] Ammar A, Dadi G. Towards an integrated Building Information Modeling (BIM) and Geographic Information System (GIS) platform for infrastructure. In: Feng C, Linner T, Brilakis I, Castro D, Chen PH, Cho Y, et al., editors. Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC). Dubai, UAE: International Association for Automation and Robotics in Construction (IAARC); 2021. pp. 129-136

### *Edited by Antonio Di Pietro and Josè Martì*

Modern critical infrastructures (CIs) (e.g., electricity, water, transportation, telecommunications, and others) form complex systems with a high degree of interdependencies from one CI to the others. Natural disasters (e.g., earthquakes, floods, droughts, landslides, and wildfires), humanmade disasters (e.g., sabotage and terrorism), and system faults (due to structural and equipment failures) will affect not only the directly impacted CI but all interdependent CIs. Risk assessment, therefore, has to be done over the entire system of CIs and should also include the social and personal impacts. According to a 2022 report, 80% of cities have been affected by significant climate change hazards represented by extreme heat (46%), heavy rainfall (36%), drought (35%), and floods (33%). The impacts of climate change, therefore, affect the complex system of the built environment and result in interrelated consequences at different scales ranging from single buildings to urban spaces and territorial infrastructures. Since it is not possible to reduce the severity of natural hazards, the main opportunity for lowering risk lies in reducing vulnerability and exposure. Vulnerability and exposure are related to urban development choices and practices that weaken the system's robustness. This volume reviews recent insights from risk identification and reduction to preparedness and financial protection strategies and proposes new approaches for better CIs and built environment protection.

Published in London, UK © 2024 IntechOpen © eugenesergeev / iStock

Critical Infrastructure - Modern Approach and New Developments

Critical Infrastructure

Modern Approach and New Developments

*Edited by Antonio Di Pietro and Josè Martì*