**2. Literature review**

In general, travel mode choices may be updated between different periods of time (period-to-period) or within the same trip (within-day). In the period-to-period choice process all the available transport modes are considered. Users have the option

**7**

*Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review…*

to choose among the available modes and their decision-making processes converge

Almost the totality of the existing scientific contributions assume that travellers choose their mode of transport through a one-step decision as a planned behavior, only few exceptions explore the alternatives. In particular, travellers' mode choices are usually reported to be habitual in several travel behaviour studies [14–16]. In general, habits depend on the perception and preference towards a travel mode and it can hardly be modified. As a matter of fact, it is a common approach to investigate and model the habitual behaviour (holding behaviour), and neglect the dynamic element of the choice process. Within this framework, the mode choice analysis may depend on the different interpretative paradigms that can be assumed for modelling travel demand:

a.Trip-based. It implicitly assumes that the choices relating to each origin– destination trip are made independently of the choices for other trips within

b.Trip chaining. It assumes that the choices concerning the entire journey influence each other. In this case, the choice of an intermediate destination, if any, takes into account the preceding or following destinations in the trip chain; the choice of transport modes takes into account the whole sequence of trips in the

c.Activity-based. It analyses transport demand as the outcome of the need to participate in different activities in different places and at different times. It therefore takes into account the relationships among different journeys made by the same person during the day and, in the most general case, between

The trip-based paradigm is the most widely adopted, and relies on a consolidated theoretical literature [17] and operational literature, which has predominantly investigated mono-modal transport systems competing with each other (e.g. [18–34]). Minor attention, yet increasing in the last years, has been paid to individuals' preferences in multi-modal networks where different transport modes are integrated and a possible choice alternative is a combination of them (e.g. [22, 35–40]). Nevertheless, it should be noted that most of them consider public or private transport modes separately or, consider integrated transport modes, for instance when combined with park-and-ride. More Recently, [41] attempt to model the full range of choice options in multimodal network settings using a stated preferences approach, and approach the problem as a route choice problem. But they only

Trip-chain and activity-based paradigms model pre-trip behaviour in a more realistic behavioural context, hence may allow a better interpretation and simulation of the travellers' mode choices. However, they are usually rather complex for

Among the pre-trip choice paradigms, pre-trip switching approaches have also been developed to understand and simulate potential modal shift (e.g. [51–56]).

calibration and implementation. Some examples include:

i.mode and departure time [41, 42];

journeys made by the various members of the same household.

towards a stable choice that, once reached, can be considered as habitual.

*DOI: http://dx.doi.org/10.5772/intechopen.98432*

the same and other journeys.

chain, and so on.

investigate pre-trip choices.

ii.trip chain [42–47]

iii. activity-based [32, 46–50];

<sup>1</sup> Throughout this paper, real-time information is treated as a special case of real-time events.

### *Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review… DOI: http://dx.doi.org/10.5772/intechopen.98432*

to choose among the available modes and their decision-making processes converge towards a stable choice that, once reached, can be considered as habitual.

Almost the totality of the existing scientific contributions assume that travellers choose their mode of transport through a one-step decision as a planned behavior, only few exceptions explore the alternatives. In particular, travellers' mode choices are usually reported to be habitual in several travel behaviour studies [14–16]. In general, habits depend on the perception and preference towards a travel mode and it can hardly be modified. As a matter of fact, it is a common approach to investigate and model the habitual behaviour (holding behaviour), and neglect the dynamic element of the choice process. Within this framework, the mode choice analysis may depend on the different interpretative paradigms that can be assumed for modelling travel demand:


The trip-based paradigm is the most widely adopted, and relies on a consolidated theoretical literature [17] and operational literature, which has predominantly investigated mono-modal transport systems competing with each other (e.g. [18–34]).

Minor attention, yet increasing in the last years, has been paid to individuals' preferences in multi-modal networks where different transport modes are integrated and a possible choice alternative is a combination of them (e.g. [22, 35–40]). Nevertheless, it should be noted that most of them consider public or private transport modes separately or, consider integrated transport modes, for instance when combined with park-and-ride. More Recently, [41] attempt to model the full range of choice options in multimodal network settings using a stated preferences approach, and approach the problem as a route choice problem. But they only investigate pre-trip choices.

Trip-chain and activity-based paradigms model pre-trip behaviour in a more realistic behavioural context, hence may allow a better interpretation and simulation of the travellers' mode choices. However, they are usually rather complex for calibration and implementation. Some examples include:

i.mode and departure time [41, 42];

ii.trip chain [42–47]

iii. activity-based [32, 46–50];

Among the pre-trip choice paradigms, pre-trip switching approaches have also been developed to understand and simulate potential modal shift (e.g. [51–56]).

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

the influence of real-time events on mode choices,1

behavioural sciences.

have become an important field of inquiry in cross-disciplinary research spanning transport engineering, computing, mathematics, psychology, and social and

The underlying assumption of most existing studies on travel mode choice is that a traveller chooses a specific mode before commencing his/her trip, which is categorized as planned behaviour. However, some studies have identified and demonstrated

public transport; some examples include real-time passenger information, weather, and transport disruptions [11–13]. These real-time events may lead travellers to assess various modes in an adaptive way to the extent that the aforementioned planned behaviour plays a less significant role in the final outcome of the mode choices.

related to the evolution of the system (e.g. the dynamic network loading).

• A novel adaptive mode choice behavioural paradigm able to incorporate

real-time events (both pre-trip and en-route), which advances state-of-the-art modelling approaches that mostly rely on static attributes and simulate mode

• A pilot survey that shows the viability and validity of the adaptive mode choice behaviour for real-world scenarios, where a number of mode options and real-time events are defined and combined to analyse user responses under

The rest of this chapter is organized as follows. Section 2 provides an extensive review of state-of-the-art mode choice approaches. In Section 3 the bi-level mode choice behaviour paradigm that explicitly accounts for real-time events is proposed. A real-world scenario pertaining to the hypothesized adaptive behaviour is introduced in Section 4, which also presents the pilot survey study, which assesses the behavioural validity of this new concept at a qualitative level, and discusses the survey results. Section 5 introduces some remarks on the main issues of MAAS and the research perspectives regarding the proposed interpretative hypermode paradigm.

In general, travel mode choices may be updated between different periods of time (period-to-period) or within the same trip (within-day). In the period-to-period choice process all the available transport modes are considered. Users have the option

<sup>1</sup> Throughout this paper, real-time information is treated as a special case of real-time events.

The main contribution made by this paper includes:

choices as planned behaviour.

different circumstances.

**2. Literature review**

This chapter aims to give a literature overview of the existing approaches, the aims to propose an adaptive mode choice behaviour paradigm which takes into account real-time events, and provides an empirical validation of this mode choice paradigm. The real-time events include, but not limited to, availability of shared bikes at the docking station, real-time information on bus arrival time, scheduled or unexpected local disruptions, and weather conditions. This research is an important undertaking as it not only identifies a set of new factors that influence mode choices, but also presents a novel framework to study mode choice behaviour. This behavioural paradigm may pose interesting challenges from a modelling perspective and may require an integrated modelling approach for both mode choice and traffic assignment to fully capture the adaptive behaviour. The latter statement stems from the observation that many real-time factors identified above have a dynamic and stochastic nature that is

particularly for travellers using

**6**

Finally, different attempts have also been made to model an habitual behaviour (holding decision), but taking into account temporal correlation for the same user, thereby showing how tastes can vary for the same traveller using short-term crosssectional data [57–59].

The pre-trip and habitual choices have been extensively investigated in the literature; however, some scholars have also dealt with the explicit simulation of mode choice dynamics with regard to both short- or long-term scenarios. In particular, [60–63] study short-term mode choice dynamics using discrete choice method and panel data. With regard to long-term mode choice dynamics, [64] investigate commuting behavior within the traditional maximum utility framework, whereas alternative approaches have tried to take into account more complex behavioural determinants and processes such as habits and learning. In particular, [65] derived decision rules based on neural networks to predict activity scheduling and mode choice; [66] developed a computational process model to mimic travel decisionmaking process; [67] developed an agent-based process to simulate travel behavior in terms of information acquisition, learning, adaptation and decision heuristics. Recently, the Markov chain approach has been fruitfully adopted to model and interpret the decision-making process [32, 68–72].

With regard to the within-day travel mode choice behavior, it can be assumed that a typical traveller chooses a transport mode (or a combination of transport modes) and may change his/her initial choice by switching to other modes before leaving the origin and/or during the trip. Obviously, such behaviour is reasonable only in a multi-modal or inter-modal context. On the one hand not many contributions can be founded in the literature; on the other hand the landscape of available mode options is evolving particularly at the urban level. Multi-modal networks are rapidly growing, and a new generation of mobile, personalised information systems and intelligent transport systems are ready to support this flexible and adaptive behaviour by providing assistance in the planning and implementation of multimodal trips [73].

As a matter of fact, an increasing number of users may reconsider their initial travel choices. However, not many contributions can be found in literature. From a psychological viewpoint, the study undertaken by [74] considers a two-level approach to simulate the mode choice. At the first level (more related to the person) the authors apply the comprehensive action determination model, which assumes that intentional processes, habitual processes, and normative processes lead to a certain level of propensity to use the private car. The second level of choice (more related to the trip) is characterized by situational influences, where trip purpose, disruptions on public transport, and weather are identified as predictors. The authors conclude that the multi-level approach is a promising alternative to conventional models. These insights from the field of psychology are valuable for the correct interpretation of the decision-making processes of travellers, and will be considered in the hypermode approach proposed in this paper.

Different contributions have analysed mode choice as a sort of path choice in a broader context of a multimodal network [49, 75–77], by considering interconnected networks, one for each different transport mode. Such an issue has been addressed in a multi-modal context through the well-established supernetworks [78, 79]. However, such an approach has been mainly adopted/used for modelling elastic demand assignment problems; it is not very flexible to address possible adaptive behaviour.

Contribution by [80] take the mode availability into consideration, but mainly from an assignment perspective as both model the user decision to change mode at each node. In [81] each option (mode and route) is associated with a probability of immediate availability, which is one for private modes and less than one for public transport, the latter being a specific value affected by service frequency. The author therefore revised the transit assignment problem by taking into account

**9**

choice [41].

ing requirements:

field by [74];

**3. The hypermode concept**

*Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review…*

mode availability at each node, which represents a decision point for the users. The proposed assignment model entails sequential choices at each intermediate node in the multimodal network and seeks an equilibrium. [80] proposes the strategy of adaptive multimodal least expected time in order to determine the hyperpath associated with the least cost in a multimodal network. In addition to the modes of walking and driving, each public transport line is considered a separate mode. The authors consider a delay associated with the transfer between modes, and model the users' capability to reassess the costs at each node and determinate if switching mode may be a better option. However, the assessment of switching from one mode to another is merely based on time as this is a reasonable assumption for assignment algorithms, but it is not sufficient to capture the decision making process at the mode choice level. Moreover, users are quite reluctant to have too many transfers and reassess all mode options at every en-route node unless a disruption occurs. In conclusion, mode choice behaviour may rely on an extensive scientific literature, but it predominantly deals with habitual behaviour including pre-trip behaviour, pre-trip switching behaviour or travellers' behaviour at specific nodes of the transportation network. Most of the existing efforts have been focused on multi-modal networks in which different transit modes are connected or in which individual transport modes (car, motorbike, cycling) and collective transit modes may interact with each other (Park and Ride). However, the choice contexts are always pre-trip and not much can be found with regard to multimodal contexts in which the transport mode can be changed during the trip (transit alternatives and shared modes). For example, the introduction of shared modes (car/motorbike/ bike-sharing) and their integration with the various existing transit systems lead to a significant flexibility that cannot be neglected. Instead, they should be carefully analysed and interpreted with behavioural paradigms that are different from the traditional ones. Furthermore, the literature suggests that weather conditions have the potential to influence mode choices [12, 82], and that there is a lack of comprehensive evaluation of costs, time and service quality in multimodal travel

In conclusion, this paper focuses on the decision making process that leads users

• it captures a more realistic mode choice behaviour, which is influenced by realtime events, building on the multi-level approach proposed in the psychology

• it is able to subsume planned behaviour as a special case, which addresses travellers whose mode choices are not adaptive; this is particularly true for

• it is validated with a pilot study through a preliminary qualitative survey to

Currently, mode choice is considered a planned behavior and is embedded within traffic assignment procedures only in a static context [83], which obviously does not capture the influence of any real-time events. With regard to dynamic modelling, mode choice is usually considered a fully pre-trip behavior. This paper

travellers who use their own vehicles (private cars and bikes);

demonstrate the validity of the approach.

to take a specific mode in the presence of different mode options and real-time information/events. The proposed novel mode choice paradigm satisfies the follow-

*DOI: http://dx.doi.org/10.5772/intechopen.98432*

### *Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review… DOI: http://dx.doi.org/10.5772/intechopen.98432*

mode availability at each node, which represents a decision point for the users. The proposed assignment model entails sequential choices at each intermediate node in the multimodal network and seeks an equilibrium. [80] proposes the strategy of adaptive multimodal least expected time in order to determine the hyperpath associated with the least cost in a multimodal network. In addition to the modes of walking and driving, each public transport line is considered a separate mode. The authors consider a delay associated with the transfer between modes, and model the users' capability to reassess the costs at each node and determinate if switching mode may be a better option. However, the assessment of switching from one mode to another is merely based on time as this is a reasonable assumption for assignment algorithms, but it is not sufficient to capture the decision making process at the mode choice level. Moreover, users are quite reluctant to have too many transfers and reassess all mode options at every en-route node unless a disruption occurs.

In conclusion, mode choice behaviour may rely on an extensive scientific literature, but it predominantly deals with habitual behaviour including pre-trip behaviour, pre-trip switching behaviour or travellers' behaviour at specific nodes of the transportation network. Most of the existing efforts have been focused on multi-modal networks in which different transit modes are connected or in which individual transport modes (car, motorbike, cycling) and collective transit modes may interact with each other (Park and Ride). However, the choice contexts are always pre-trip and not much can be found with regard to multimodal contexts in which the transport mode can be changed during the trip (transit alternatives and shared modes). For example, the introduction of shared modes (car/motorbike/ bike-sharing) and their integration with the various existing transit systems lead to a significant flexibility that cannot be neglected. Instead, they should be carefully analysed and interpreted with behavioural paradigms that are different from the traditional ones. Furthermore, the literature suggests that weather conditions have the potential to influence mode choices [12, 82], and that there is a lack of comprehensive evaluation of costs, time and service quality in multimodal travel choice [41].

In conclusion, this paper focuses on the decision making process that leads users to take a specific mode in the presence of different mode options and real-time information/events. The proposed novel mode choice paradigm satisfies the following requirements:

