Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach

Liu, Zhiyong; Li, Ruimin; Dai, Jingchen

doi:10.1016/j.tra.2022.01.001

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…The results show consistency with previous research that parking demand was reduced the most in residence-dominant zones in terms of quantity and office-dominant zones in terms of proportion, at the cost of a dramatic increase in empty VMT generated because of SAVs' relocation behavior which would therefore result in road congestion. Reference [24] explored the effects of SAVs based on a data-driven modeling approach using the dataset from Langfang, China. The simulation experiments contain two kinds of sharing schemes called "ride-sharing" and "car-sharing", respectively.…”

Section: Previous Studiesmentioning

confidence: 99%

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

Zhu,

Ye,

Yan

et al. 2024

Systems

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Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%.

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Section: Previous Studiesmentioning

confidence: 99%

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

Zhu,

Ye,

Yan

et al. 2024

Systems

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“…Among several advantages, their ability of parking themselves may lead to an overall decrease in parking demand. A benefit that may become more significant especially if AVs are integrated into shared mobility services, e.g., car/ride‐sharing systems (Liu et al., 2022). Moreover, users are usually likely to ask for a CS service with AVs.…”

Section: New Trends In the Optimization Of Car‐sharing Systemsmentioning

confidence: 99%

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

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This paper reviews the most recent literature on the optimization of car‐sharing systems. Unlike other surveys, we do not focus on a single aspect of car‐sharing systems, but we consider a wide range of optimization problems with a global view. Our aim is threefold. First, we revise the classic decision problems arising in car‐sharing systems with conventional and electric vehicles, such as the fleet size, the station location, and the vehicle relocation. Then, we discuss some of the most recent decision problems arising in more innovative car‐sharing systems, like those with autonomous vehicles, with mixed fleets, and the multi‐modal and the time‐dependent ones. Finally, we focus on new perspectives arising in the optimization of some innovative car‐sharing services that are not yet (or little) investigated in the literature and that are worthy of attention from the operations research point of view. For each of them, we also highlight new and challenging open decision problems that deserve academic attention.

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“…Shared Autonomous Vehicles (SAV) systems refer to shared services making use of fully Autonomous Vehicles (AV). SAV might include various configurations: from ride-sharing services [24], [25], [57], [62], [130], [138] to vehicle-sharing services [27], [58], [139] such as autonomous taxis [77], [112], [139], [140]. These can be summoned by the user who plans a trip in advance (i.e., Autonomous Mobility-on-Demand).…”

Section: B Shared Autonomous Vehiclesmentioning

confidence: 99%

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction

et al. 2022

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Shared Mobility Systems (SMS) facilitate on-demand journeys using one or more transportation modes such as car-sharing, bike-sharing, or ride-sharing. As a result, SMS often face challenges such as finding suitable facility locations, efficient routing of shared vehicles, matching and re-distributing available resources with dynamic demands. Most existing surveys study how a particular challenge is addressed using artificial intelligence, machine learning, and optimisation techniques. However, these surveys fail to address the crucial ''Whole System Design'' point of view, which includes the ''whole system'' of interconnected stakeholders, entities, and subsystems that participate in, impact, and influence the success of each other and system a whole. Such a survey is highly required with the growing demand for flexible SMS that supports autonomous decision-making and offers multi-modal and inter-operable transportation services catered for highly dynamic traffic conditions in urban areas. This paper attempts to fill this gap by categorising the SMS' interconnected challenges in different transportation modes and reviewing how offered solutions across all modes address these challenges as a unified system.

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Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach

Cited by 16 publications

References 38 publications

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

A survey on emergent trends in the optimization of car‐sharing systems

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction

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