Abstract

International audience; —Technologic advancements have contributed to the spread of sharing economy concepts, a developing phenomenon that favors the shift from private mobility to service-use (shared mobility). One-way carsharing is a most recent and popular kind of shared mobility, that is growing and developing rapidly in various forms. These systems are considered to have a transformative impact on future urban transportation. Despite all of the benefits that have been reported from the use of new one-way carsharing (e.g. Autonomous Mobility-on-Demand systems), their impacts on the mobility are not certain yet. This comes from the fact that in such services supply and demand influence each other in a significant way in short-, mid-, and long-term. Also service characteristics at the level of each vehicle strongly affect the demand. In this paper methods, paradigms, toolkits and platforms used in the literature for the demand estimation of the new one-way carsharing systems, as well as their potential drawbacks are discussed. A review of the literature reveals that despite the considerable number of studies related to balancing vehicle stocks across stations in one-way systems, the investigation about demand estimation of such services for which the complex relationship between supply and demand is considered, remain very limited. The majority of current platforms and toolkits used for demand estimation of new one-way carsharing systems are based on activity-based multi-agent simulations. In these simulations several main components are not yet taken into account, which could dramatically change the results. Data detail, accessibility and reliability, high computational time, calibration and validation still remain major challenges for travel demand estimation for one-way carsharing systems.

Abstract

International audience; Shared autonomous vehicles are considered to have a transformative impact on future urban transportation and especially Shared Mobility. In order to assess the transport-related impact of this new mode, various models and simulations are under development. The majority of recently proposed simulations are based on activity-based multi-agent approaches. Thanks to the disaggregated level of data in multi-agent simulation, the traveler decision making mechanisms might be individualized according the attributes. In this paper we try to take in advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles. To illustrate the proposed methodology the transport system of Paris is simulated by using an activity-based multi-agent tool called MATSim. The traveler preferences toward shared autonomous vehicles use are also summarized based on the literature review. Document type: Conference object

Abstract

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.

Abstract

Urban mobility options have substantially increased in recent years, enabled by the widespread availability of smart device software Apps, geo-positioning technology, and the ease of electronic financial transactions. These options are likely to be supplemented soon by the rapidly advancing development of autonomous vehicles. Commercial sharing services,such as share cars and share bicycles, are expecting to complement fixed route public transit systems to support the first/last mile challenge of public transit services [a part of Mobility asa Service (MaaS)], as well as stimulate additional demand for short distance mobility. This paper reviews the development of commercial bike and car sharing schemes, then examines the technologies and policies that support MaaS. This research draws upon the experiences learnt and indicates the potential difficulties encountered in the successful planning of shared mobility services. The results provide an in-depth discussion of the characteristics and needs of shared mobility services and investigates the barriers of applying the Internet of Things (IoT), cybersecurity and blockchain in first/last mile mobility challenge. The findings will assist the community, business providers and government policymakers who are keen to promote shared mobility as a pathway towards more efficient, environmentally sustainable and socially responsive mobility solutions.