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To mitigate carbon emissions and exploit underutilized lanes (e.g., bus lanes), a credit-based reservation scheme (CRS) is proposed to optimize road resources, allowing travelers to switch from the high-emission mode (e.g., driving in normal lanes) to the low-emission mode (e.g., taking buses) and gain carbon credits. These credits can then be used to access the reward mode (e.g., using priority lanes). This CRS design problem is formulated as a leader–follower game, where the transport authority determines the reward ratio based on the mode choices of travelers. We analyze how the CRS affects emissions and traffic congestion, and conduct two numerical studies on Beijing and London to verify our findings. Results demonstrate that when the reward ratio is set at a moderate level and the generalized cost of the reward mode is higher than the other two modes, the CRS can effectively reduce system-wide carbon emissions and traffic congestion.

In the rapidly transforming landscape of urban transportation, Mobility-as-a-Service (MaaS) has emerged as a potentially transformative solution to adapt to fluctuating travel demand. However, despite arguments in the literature suggesting that uncertainty in travel plans may influence travelers’ preferences for MaaS, the extent and mechanisms of this impact remain relatively underexplored. This study aims to bridge this gap by evaluating travelers’ preferences toward MaaS subscriptions, mode choices, and their reactions to uncertainty within a risky-choice modeling framework. We incorporate a linear-specified benchmark model and two nonlinear models (one based on Expected Utility Theory with Constant Absolute Risk Aversion adjustments, and the other on Prospect Theory), to estimate travelers’ risk perceptions and their valuation of gains and losses associated with travel time and costs caused by occasional activities during commute trips. The data utilized in our study originates from a specifically designed stated preference survey carried out in Hong Kong. Our findings present insightful observations into MaaS bundle configurations, socio-demographic patterns, and notably, behaviors under uncertain circumstances. In the face of uncertainty, we observe a noticeable increase in reluctance to adopt MaaS, and a heightened preference for taxis. The models also underscore significant risk aversion and diminishing sensitivity concerning travel time and cost, with these tendencies proving more pronounced for travel time compared to cost. These insights are valuable for policymakers and MaaS operators, offering an enhanced understanding of users’ risk attitudes and the intricate interplay between travelers’ preferences for MaaS and the uncertainties inherent in daily travel.

Mobility-as-a-Service (MaaS) is an emerging transport model which provides access to a combination of travel modes through a single platform. A MaaS operator sits between travelers and transport service providers (TSPs), acting as a broker who purchases mobility resources from individual TSPs, constructs seamless transport services, and then sells them to travelers in response to their demand. To ensure the sustainability of such platforms, the key challenge lies in matching travelers to TSPs so that travelers’ individual needs are satisfied, TSPs gain nonnegative profits and system efficiency is achieved. To solve this matching and pricing problem, travelers’ truthful valuations and travel requirements are needed, while such information is usually unknown to operators beforehand. In this study, we develop an auction-based online mobility resource allocation and pricing mechanism to solve this problem, taking into account travelers’ strategic behavior. We first propose an offline (static) mechanism using a Vickrey–Clarke–Groves (VCG) based pricing scheme to ensure incentive compatibility, individual rationality, and system efficiency. We then develop an online mechanism based on the dynamic learning algorithm to obtain the near-optimal solution and compare it to a customized greedy based algorithm. We compare both online mechanisms to the offline VCG based mechanism and theoretically prove the competitive ratios. The efficiency and performance of the proposed mechanisms are demonstrated through a numerical study.

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to discharge unused power to the power grid, allowing EVs to serve as flexible energy resources. Although this technology promises benefits to power systems, its impact on transport systems is unknown. We investigate the interaction between transport and power systems with V2G during co-existing peak periods. Commuters choosing to discharge earn rewards while encountering additional delay costs and altering departure time. A bottleneck model is used to characterize drivers’ departure time and discharge choices. Possible queue patterns and occurrence conditions are derived. Traffic congestion is proved to be reduced compared to the without V2G case when commuters choosing to discharge arrive at the bottleneck earlier than those choosing not to discharge. We also derive the sufficient condition of the “win–win–win” situation for EV drivers, power and transport systems and develop a decision tree for the power system to set the discharge reward optimally.

As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in carfollowing behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-followingMV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocityaggressive. These findings highlight the vital importance of taking into account the heterogeneity of MVfollowing-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.