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Mobility-as-a-Service (MaaS) can be considered the latest innovative technological solution to sustainable transportation. It offers travelers access to a pre-paid bundle of transportation modes on a single app with the additional convenience of the integration of planning, booking, and payment. Capturing the impact of this new technology on travel behavior remains a challenge, given the scarce number of comprehensive MaaS pilots. In this study, a stated adaptation experiment was designed to determine travelers’ propensity toward using this new service. Such knowledge sheds light on the potential of MaaS to alter daily travel patterns.

Route choice modeling under travel time uncertainty is essential for analyzing travelers’ mobility patterns. This paper investigates the impact of travel time uncertainty on route choice behavior in user equilibrium models based on a generalized mean-variance metric (GMV). This model can capture the influence of risk attitudes and schedule unpunctuality on route choice using a generalization of expected travel time, variance, and expected early or late arrival penalties, of which travelers are assumed to minimize the GMV of trips considering a certain on-time arrival probability. This paper establishes the properties of GMV and formulates the GMV-based static user equilibria as a variational inequality (VI) problem, for which the existence and uniqueness of the solutions are also analyzed. An effective traffic assignment algorithm without path enumeration is developed to solve the proposed user equilibrium problem. Numerical examples are conducted to demonstrate the properties of the proposed model. KEYWORDS: Travel time uncertainty, route choice, generalize mean-variance, user equilibrium

This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a public charging station location-allocation model using a scenario-based stochastic programming (SP) approach. In order to capture the dynamic charging behaviour of PEV users, a chi-squared automatic interaction detector (CHAID)-based mixed effects decision tree is induced from multi-day activity diaries. Moreover, because the stochastic error of the micro-simulation approach brings about uncertainty, we adopted a two-stage stochastic mixed-integer programming (TSMIP) model, which measures uncertainty by means of a finite set of scenarios obtained from the derived decision rules underlying PEV charging. The proposed approach is demonstrated for the City of Eindhoven, The Netherlands, and benefits of the stochastic solution are discussed.

Multi-state supernetworks are capable of representing activity-travel patterns at a high level of detail and thus are a powerful tool for activity-travel scheduling (ATS) of multidimensional choice facets. To alleviate the limitations of the common deterministic network representation, travel time and activity duration uncertainty has been incorporated in multi-state supernetworks. However, the extension unrealistically assumed that all uncertain components are independent. This study suggests an approach of ATS considering spatially and temporally correlated travel times and activity durations in stochastic time-dependent (STD) multi-state supernetworks. Support points are used as a representation of the stochasticity of the STD multi-state supernetwork. ATS is formulated as a pathfinding problem subject to space–time constraints based on recursive formulations. A series of numerical experiments is implemented to demonstrate the applicability of the suggested approach.

This paper follows authors’ previous work, which measured social influence in travel behavior using a sequential stated adaptation experiment, and aims to investigate issues that are not discussed in the previous one. Specifically, this paper supplements the previous work in two aspects. On the one hand, the previous work tried to model respondents’ sequential choice in terms of a choice task with and without social network member’s choice, and estimated the model sequentially without any clear evidences to show the difference between sequential and simultaneous estimation. Therefore, this paper first provides the comparison of these two estimation approaches. On the other hand, the previous work did not clearly address two specific issues that come with the use of sequential choice experiments to measure social influence. The first issue is that respondents have a tendency to keep their choices consistent in sequential choice tasks, which may mask the effect of social influence; the second one is that the stochastic nature of utility implies that respondents may still reveal a shift in their choices even if no information about the choice of social network members is provided, which will be mixed with the effect of social influence, depending on the similarity of alternatives in the choice set. Therefore, this paper aims to take these two issues into consideration. The results of this study reveal that estimating the model simultaneously may lead to confounding bias and that taking the issues caused by the sequential choice experiment could improve the performance of the model and provide more insights about social influence.