**3.3 ABM integration with dynamic traffic assignment**

In parallel with the travel demand modeling, on the supply side, the conventional supply models used to be static, which import constant origindestination flows as an input and produce static congestion patterns as an output. Consequently, these models were unable to represent the flow dynamics in a clear and detailed manner. Dynamic traffic assignment (DTA) models have emerged to address this issue and are capable of capturing the variability of traffic conditions throughout the day. It is evident that the shift of analysis from trips to activities in the demand modeling, as well as, the substitution of the static traffic assignment with dynamic traffic assignment in the supply side, can provide more realistic results in the planning process. Furthermore, the combination of ABM and DTA can better represent the interactions between human activity, their scheduling decision, and the underlying congested networks. Nevertheless, according to the study of [11], the integration of ABM with DTA received little attention and still requires further theoretical development. There are different approaches to the integration of ABM and DTA, which started with a sequential integration. In this type of integration, exchanging data between two major model components (ABM and DTA) happens at the end of the full iteration, to generate daily activity patterns for all synthetic population in an area of study, the activity-based model is run for the whole period of a complete day. The outputs of the ABM model which are lists of activities and plans are then fed into the DTA model. The DTA model generates a new set of time-dependent skim matrices as inputs to ABM for the next iteration. This process is continued until the convergence will be reached in the OD matrices output. Model systems applying the sequential integration paradigm can be found in most of the studies in the literature. For example, Castiglione [129] integrated DaySim which is an activity-based travel demand model developed for Sacramento with a disaggregate dynamic network traffic assignment tool TRANSIMS router. Bekhor [130] investigated the possibility of coupling the Tel Aviv activity-based model with MATSim as an agent-based dynamic assignment framework. Hao [116] integrated the TASHA model with

**37**

is presented in **Table 2**.

order to save model runtime.

*Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability*

MATSim. Ziemke [107] integrated CEMDAP, which is an activity-based model with MATSim to check the possibility of transferring an activity-based model from one geographic region to another. Lin [131] introduced the fixed-point formulation of integrated CEMDAP as an activity-based model with an Interactive System for Transport Algorithms (VISTA). Based on the mathematical algorithm of household activity pattern problem (HAPP), ABM and DTA were integrated [132] by presenting the dynamic activity-travel assignment model (DATA) which

is an integrated formulation in the multi-state super network framework.

In the sequential integration, the ABM and DTA models run separately until they reach convergence. At the end of an iteration, these models perform data exchange before iterate again. Therefore, this kind of integrated framework cannot react quickly and positively to network dynamics and is unable to adapt to real-time information available to each traveler. In addressing this limitation, integrated models that adopt a much tighter integration framework have been developed recently. This approach is quite similar to the sequential approach, however; the resolution of time for ABM simulation is one minute rather than 24 hours (complete day). Relating to this level of dynamic integration, Pendyala [133] investigated the possibility of integrating OpenAMOS which is an activitytravel demand model with DTA tool name MALTA (Multiresolution Assignment and loading of traffic activities) with appropriate feedback to the land-use model system. For increasing the level of dynamic integration of ABM and DTA models, dynamic integration having pre-trip enroute information with full activity-travel choice adjustments has been introduced. In this level of ABM & DTA integration, it is assumed that pre-trip information is available for travelers about the condition of the network. It means that travelers are capable of adjusting activity-travel choices since they have access to pre-trip and Enroute travel information. Another tightly integrated modeling framework was proposed in [134] to integrate ABM (openAMOS) and DTA (DTALite) to capture activity-travel demand and traffic dynamics in an on-line environment. This model is capable of providing an estimation of traffic management strategies and real-time traveler information provision. Zockaie et al. [135] presented a simulation framework to integrate the relevant elements of an activity-based model with a dynamic traffic assignment to predict the operational impacts related to congestion pricing policies. Auld et al. [38] developed an agent-based modeling framework (POLARIS) which integrates dynamic simulation of travel demand, network supply, and network operations to solve the difficulty of integrating dynamic traffic assignment, and disaggregate demand models. A summary of the current literature on ABM and DTA integration

The above discussion illustrates that most of the model integration platforms between ABM + DTA work based on sequential integration. This loose coupling platform is the most straightforward and popular approach albeit is not responsive to network short-term dynamics and real-time information. Efforts to develop a comprehensive simulation model that can account for all components of dynamic mobility and management strategies continue. Further developments will have to deal with the implementation of an integrated ABM + DTA platform on a large network to support decision-makers, focus on the integration between activity-based demand models and multimodal assignment [143] as well as reducing computational efforts via better data exchange procedure and improving model communication efficiency. Defining practical convergence criteria is another issue which needs further investigations. Fully realistic convergence is normally never happened in sequential integration due to applying a pre-defined number of feedback loops in

*DOI: http://dx.doi.org/10.5772/intechopen.93827*

### *Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx.doi.org/10.5772/intechopen.93827*

MATSim. Ziemke [107] integrated CEMDAP, which is an activity-based model with MATSim to check the possibility of transferring an activity-based model from one geographic region to another. Lin [131] introduced the fixed-point formulation of integrated CEMDAP as an activity-based model with an Interactive System for Transport Algorithms (VISTA). Based on the mathematical algorithm of household activity pattern problem (HAPP), ABM and DTA were integrated [132] by presenting the dynamic activity-travel assignment model (DATA) which is an integrated formulation in the multi-state super network framework.

In the sequential integration, the ABM and DTA models run separately until they reach convergence. At the end of an iteration, these models perform data exchange before iterate again. Therefore, this kind of integrated framework cannot react quickly and positively to network dynamics and is unable to adapt to real-time information available to each traveler. In addressing this limitation, integrated models that adopt a much tighter integration framework have been developed recently. This approach is quite similar to the sequential approach, however; the resolution of time for ABM simulation is one minute rather than 24 hours (complete day). Relating to this level of dynamic integration, Pendyala [133] investigated the possibility of integrating OpenAMOS which is an activitytravel demand model with DTA tool name MALTA (Multiresolution Assignment and loading of traffic activities) with appropriate feedback to the land-use model system. For increasing the level of dynamic integration of ABM and DTA models, dynamic integration having pre-trip enroute information with full activity-travel choice adjustments has been introduced. In this level of ABM & DTA integration, it is assumed that pre-trip information is available for travelers about the condition of the network. It means that travelers are capable of adjusting activity-travel choices since they have access to pre-trip and Enroute travel information. Another tightly integrated modeling framework was proposed in [134] to integrate ABM (openAMOS) and DTA (DTALite) to capture activity-travel demand and traffic dynamics in an on-line environment. This model is capable of providing an estimation of traffic management strategies and real-time traveler information provision. Zockaie et al. [135] presented a simulation framework to integrate the relevant elements of an activity-based model with a dynamic traffic assignment to predict the operational impacts related to congestion pricing policies. Auld et al. [38] developed an agent-based modeling framework (POLARIS) which integrates dynamic simulation of travel demand, network supply, and network operations to solve the difficulty of integrating dynamic traffic assignment, and disaggregate demand models. A summary of the current literature on ABM and DTA integration is presented in **Table 2**.

The above discussion illustrates that most of the model integration platforms between ABM + DTA work based on sequential integration. This loose coupling platform is the most straightforward and popular approach albeit is not responsive to network short-term dynamics and real-time information. Efforts to develop a comprehensive simulation model that can account for all components of dynamic mobility and management strategies continue. Further developments will have to deal with the implementation of an integrated ABM + DTA platform on a large network to support decision-makers, focus on the integration between activity-based demand models and multimodal assignment [143] as well as reducing computational efforts via better data exchange procedure and improving model communication efficiency. Defining practical convergence criteria is another issue which needs further investigations. Fully realistic convergence is normally never happened in sequential integration due to applying a pre-defined number of feedback loops in order to save model runtime.

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

different activity types, which have different influences on the happiness that each individual experienced. Well-being can be integrated into activity-based models based on random utility theory. In terms of modeling, a framework was introduced [122] considering well-being data to improve activity-based travel demand models. According to their hypothesis, well-being is the final aim of activity patterns. They applied a random utility framework and considered well-being measures as indicators of the utility of activity patterns, and planned to test their framework empirically by adding well-being measurement equations to the DRCOG's activity-

The above literature review showed the importance of applying traffic models to generate input data to other models such as the air quality model. The accuracy of emission models is highly dependent on the level of detail in transport demand model inputs. Activity-based and agent-based models are supposed to describe reality more accurately by providing more detailed traffic data. Beyond measurement of air quality, well-being and health have drawn increasing attention. The health impact of changes in travel behavior, health inequalities, and social justice can be assessed within the activity-based platform [128]. With the help of geospatial data acquisition technologies like GPS, behavioral information with health data can be integrated into the development of an activity-based model to provide policies that

**36**

based model.

affect the balance of transport and well-being.

**3.3 ABM integration with dynamic traffic assignment**

In parallel with the travel demand modeling, on the supply side, the conventional supply models used to be static, which import constant origindestination flows as an input and produce static congestion patterns as an output. Consequently, these models were unable to represent the flow dynamics in a clear and detailed manner. Dynamic traffic assignment (DTA) models have emerged to address this issue and are capable of capturing the variability of traffic conditions throughout the day. It is evident that the shift of analysis from trips to activities in the demand modeling, as well as, the substitution of the static traffic assignment with dynamic traffic assignment in the supply side, can provide more realistic results in the planning process. Furthermore, the combination of ABM and DTA can better represent the interactions between human activity, their scheduling decision, and the underlying congested networks. Nevertheless, according to the study of [11], the integration of ABM with DTA received little attention and still requires further theoretical development. There are different approaches to the integration of ABM and DTA, which started with a sequential integration. In this type of integration, exchanging data between two major model components (ABM and DTA) happens at the end of the full iteration, to generate daily activity patterns for all synthetic population in an area of study, the activity-based model is run for the whole period of a complete day. The outputs of the ABM model which are lists of activities and plans are then fed into the DTA model. The DTA model generates a new set of time-dependent skim matrices as inputs to ABM for the next iteration. This process is continued until the convergence will be reached in the OD matrices output. Model systems applying the sequential integration paradigm can be found in most of the studies in the literature. For example, Castiglione [129] integrated DaySim which is an activity-based travel demand model developed for Sacramento with a disaggregate dynamic network traffic assignment tool TRANSIMS router. Bekhor [130] investigated the possibility of coupling the Tel Aviv activity-based model with MATSim as an agent-based dynamic assignment framework. Hao [116] integrated the TASHA model with


**39**

*Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability*

Time-sensitive traffic network model (DTALite)

**integration**

DTA (DynusT) Sequential Evaluate different

Dynamic integration **Insights**

Sequential Show the efficiency of

resolution

convergence measurements: ABM demand, DTA in terms of a

gap of costs

The resulting gains in computational efficiency and performance allow planning models to include previously separate aspects of the urban system

the model over the static assignment-based ABM capturing behavioral changes at a finer time

**Paper ABM structure DTA Structure Method of** 

exchange of information between the ABM

*DOI: http://dx.doi.org/10.5772/intechopen.93827*

[38] POLARIS, which executes a continuous

and DTA components

[92] Advanced demand models (InSITE ABM)

(CT-RAMP)

[142] The ABM

**Table 2.**

**3.4 ABM and travel demand management applications**

*A summary of the empirical literature on ABM and DTA integration.*

Travel demand management (TDM) strategies are implemented to increase the efficiency of the transportation system and reduce traffic-related emissions. Some examples include mode shift strategies (encouraging people to use public transport) [144], time shift (to ride in off-peak hours, congestion pricing), and travel demand reduction [145] (using shared mobility service or teleworking). Shared transport services including car sharing, bike sharing, and ridesharing have been implemented in most of the transport planning systems across the world. Applying activity-based travel demand models to study the optimal fleet size can be found in different studies in the literature [146, 147]. Parking price policies and their impacts on car sharing were investigated using MATSim in [148]. Results show shared vehicles use more efficient parking spaces in comparison to private vehicles. In the first attempt to model car sharing on more than one typical day [149] the agent-based simulation (mobitopp) was extended with a car-sharing option to study the travel behavior of the population in the city of Stuttgart in one week. In the recent study of [150], car sharing was integrated into an activity-based dynamic user equilibrium model to show the interaction between the demand and supply of car sharing. Among all the TDM strategies, telecommuting can be implemented in a shorter time [151–153]. The results of these studies present a reduction in vehicle-kilometers-traveled (VKT) during peak hours mainly because telecommuters change their trip timetable during these times. This plan rescheduling is also investigated and addressed in different studies [154] based on the statistical analysis of worker's decisions about choice and frequency of telecommuting. While the plan rescheduling leads to reducing commute travel, the overall impacts of telecommuting on the formation of worker's daily activity-travel behavior is challenging. For example, this policy reduced total distance traveled by 75% on telecommuting days while telecommuting could reduce the total commute distance up to 0.8% and 0.7% respectively [151, 155]. Based on the adoption and frequency of telecommuting, a joint discrete choice model of homebased commuting was developed for New York city using the revealed preference (RP) survey [156]. Their results show a powerful relationship among individuals'


*Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx.doi.org/10.5772/intechopen.93827*

#### **Table 2.**

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

Multiagent Simulation (MATSim)

Simulation (MATSim)

Multi-agent Simulation (MATSim)

Disaggregate dynamic network assignment tool (TRANSIMS)

Disaggregate dynamic network assignment tool (TRANSIMS)

[138] CEMDAP (VISTA) Sequential Show the impacts of

**integration**

**Insights**

Sequential Discuss the disadvantages

times

results

Sequential Show improved run times,

matrices

Sequential Running time limitations

Sequential Show the advantages

of the integration of ABM and DTA using OD matrices and link travel

of the microsimulation approach over conventional methodologies relying heavily on temporal or spatial aggregation

multiple time interval portioning and varying step size on reaching faster and more stable convergence

the full activity list can be used directly, without creating origin-destination

prevent the models to realistically represent the impacts of network events or disruptions on activity-

in the interaction of ABM and DTA makes integration

Show the proposed model is capable of simulating the behavioral pattern of human activity in space, time, and networks

Show the model is capable of providing an estimation of traffic management strategies and real-time traveler information

Show the power of the model to capture multimodal and multi-activity trip chaining at equilibrium states while sensitive to policy interventions

A user-based approach to evaluate equilibrium

provision

conditions

travel patterns

more accurate

Sequential Choosing smaller time steps

Dynamic integration

integration

Dynamic integration

Dynamic integration

**Paper ABM structure DTA Structure Method of** 

[136] Kutter Model

of Berlin

[130] Tel Aviv activitybased model

[140] Agent-based

DaySim ABM model developed for the Sacramento and Jacksonville

Dynamic Activity Planning and Travel Scheduling (ADAPTS) developed for the Chicago region

[133] Simulator of travel, route, activity, vehicles, emission and land use (SimTRAVEL) that integrates land-use, activity-based travel

[134] ABM (openAMOS) and DTA (DTALite) Dynamic

demand with DTA models

[132] Formulation of a dynamic activity-travel

[141] Integrated ABM-DTA framework to

ABM and DTA

network

assignment (DATA) model in the multistate supernetwork framework combining

consider congestion pricing in a large-scale

[129, 139]

developed for the city

[137] TASHA model Multiagent

**38**

*A summary of the empirical literature on ABM and DTA integration.*

#### **3.4 ABM and travel demand management applications**

Travel demand management (TDM) strategies are implemented to increase the efficiency of the transportation system and reduce traffic-related emissions. Some examples include mode shift strategies (encouraging people to use public transport) [144], time shift (to ride in off-peak hours, congestion pricing), and travel demand reduction [145] (using shared mobility service or teleworking). Shared transport services including car sharing, bike sharing, and ridesharing have been implemented in most of the transport planning systems across the world. Applying activity-based travel demand models to study the optimal fleet size can be found in different studies in the literature [146, 147]. Parking price policies and their impacts on car sharing were investigated using MATSim in [148]. Results show shared vehicles use more efficient parking spaces in comparison to private vehicles. In the first attempt to model car sharing on more than one typical day [149] the agent-based simulation (mobitopp) was extended with a car-sharing option to study the travel behavior of the population in the city of Stuttgart in one week. In the recent study of [150], car sharing was integrated into an activity-based dynamic user equilibrium model to show the interaction between the demand and supply of car sharing. Among all the TDM strategies, telecommuting can be implemented in a shorter time [151–153]. The results of these studies present a reduction in vehicle-kilometers-traveled (VKT) during peak hours mainly because telecommuters change their trip timetable during these times. This plan rescheduling is also investigated and addressed in different studies [154] based on the statistical analysis of worker's decisions about choice and frequency of telecommuting. While the plan rescheduling leads to reducing commute travel, the overall impacts of telecommuting on the formation of worker's daily activity-travel behavior is challenging. For example, this policy reduced total distance traveled by 75% on telecommuting days while telecommuting could reduce the total commute distance up to 0.8% and 0.7% respectively [151, 155]. Based on the adoption and frequency of telecommuting, a joint discrete choice model of homebased commuting was developed for New York city using the revealed preference (RP) survey [156]. Their results show a powerful relationship among individuals'

### *Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

attributes, households' demographics, and work-related factors, and telecommuting adoption and frequency decisions. A similar study [157] estimated the telecommuting choice and frequency by using a binary choice model and ordered-response model respectively. In terms of using activity-based modeling, [158] POLARIS activity-based framework was applied to research telecommuting adoption behavior and apply MOVES emission simulator model to assess the consequences of implementing this policy on air quality. Their results show that considering 50% of workers in Chicago with flexible working time hours in comparison to the base case with 12% flexible time hour workers, telecommuting can reduce Vehicle Mile Traveled (VMT) and Vehicle Hour Traveled (VHT) by 0.69% and 2.09% respectively. This policy reduces greenhouse gas by up to 0.71% as well. Pirdavani et al. [159] investigated the impact of two TDM scenarios (increasing fuel price and considering teleworking) on traffic safety. In this work, FEATHERS model, which is an activity-based model, was applied to produce exposure matrices to have a more reliable assessment. The results show the positive impacts of two scenarios on safety (**Figure 2**).

**Figure 2.**

*Travel demand management policies within the activity-based platform.*

The above section explores the relationship between transport demand management policies and travel behavior in the ABM context. The use of an activity-based travel demand model provides flexibility to employ a range of policy scenarios, and at the same time, the results are as detailed as possible to obtain the impact of policies on a disaggregated level. The finding highlights the importance of implementing different transportation policies management together to reach the most appropriate effect in terms of improving sustainability and the environment. The discussion emphasizes the need for considering more comprehensive transportation and environmental policies concerning sustainability to tackle travel planning in light of the increasingly diverse and complex travel patterns.
