Value of demand information in autonomous mobility-on-demand systems

Wen, Jian; Nassir, Neema; Zhao, Jinhua

doi:10.1016/j.tra.2019.01.018

Cited by 19 publications

(9 citation statements)

References 46 publications

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“…It offers a transport solution from customer's perspective to be made via multimodal trip planning feature. (Mulley, Nelson, and Wright 2018;Vazifeh et al 2018;Azevedo et al 2016;Zhang, Rossi, and Pavone 2016;Wen, Nassir, and Zhao 2019) 7 Customization Customization enables end users to modify the offered service option in according to their preferences. (Lyons, Hammond, and Mackay 2019;Ho et al 2018;Sochor, Karlsson, and Strömberg 2016)…”

Section: Ecosystemmentioning

confidence: 99%

Mobility as a Service: A Review on Recent Development and Future Envision

Xi¹

2021

Preprint

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Mobility as a Service (MaaS) is an innovative transport concept, anticipated to provide travelers with different kinds of travel services, more sustainable than a private car, in a simpler, packaged way. It combines different transport modes to offer a tailored mobility package, like a monthly mobile phone contract. The rapid development of intelligent transportation system and the shared economy has speeded up the development of MaaS in these years. In this paper, we aim at classifying the existing research on MaaS and the characteristics of MaaS into different categories, in order to answer the following questions after reviewing the existing literature: What is MaaS? Who are the main actors in MaaS? How can MaaS be implemented? Why should it be implemented? Where will MaaS end up in this wave of disruption? When we talk about MaaS, what are we focusing on? What is the future leading frequency of MaaS? Finally, based on the existing literature, we envision the leading future of MaaS.

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Section: Ecosystemmentioning

confidence: 99%

Mobility as a Service: A Review on Recent Development and Future Envision

Xi¹

2021

Preprint

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“…Ref. [32] proposed a dynamic request-vehicle assignment heuristic and a rebalancing policy for AMoD systems, and evaluated the value of demand information through simulation. On the other hand, the design and operations of MoD/AMoD systems need to account for diverse objectives of drivers, passengers and system operators, whose benefits may be conflicting.…”

Section: A Literature Reviewmentioning

confidence: 99%

An Integrated Decomposition and Approximate Dynamic Programming Approach for On-Demand Ride Pooling

Shen

2020

IEEE Trans. Intell. Transport. Syst.

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Through smartphone apps, drivers and passengers can dynamically enter and leave ride-hailing platforms. As a result, ride-pooling is challenging due to complex system dynamics and different objectives of multiple stakeholders. In this paper, we study ride-pooling with no more than two passenger groups who can share rides in the same vehicle. We dynamically match available drivers to randomly arriving passengers and also decide pick-up and drop-off routes. The goal is to minimize a weighted sum of passengers' waiting time and trip delay time. A spatial-and-temporal decomposition heuristic is applied and each subproblem is solved using Approximate Dynamic Programming (ADP), for which we show properties of the approximated value function at each stage. Our model is benchmarked with the one that optimizes vehicle dispatch without ride-pooling and the one that matches current drivers and passengers without demand forecasting. Using test instances generated based on the New York City taxi data during one peak hour, we conduct computational studies and sensitivity analysis to show (i) empirical convergence of ADP, (ii) benefit of ride-pooling, and (iii) value of future supply-demand information.

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“…Studies that have considered elastic demand for on-demand services include Hörl, Erath, and Axhausen (2016), Narayan et al (2019), Basu et al (2018), Wen, Nassir, and Zhao (2019). Hörl, Erath, and Axhausen (2016) presented a framework for simulation of autonomous vehicles in an integrated network and population-based traffic environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Basu et al (2018) presented a flexible automated mobility on-demand (AMoD) model developed within an agent-based simulation platform. Wen, Nassir, and Zhao (2019) studied the value of information on passenger demand in an on-demand mobility system at both individual and aggregate levels. Their results indicate that information on aggregate demand can lead to a better service (more requests served and shorter waiting time) while improving system performance and yielding a higher profit.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Narayan

Cats

Oort

et al. 2020

Transportmetrica A: Transport Science

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The recent emergence of innovative mobility solutions is changing the mobility landscape in urban areas. However, it remains unknown how the combined operation of private and pooled on-demand services affect service performance and the required dimensioning of the fleet size for such services. This study develops a model to determine the fleet size of an on-demand system offering private service and pooled service, where the demand for these services is an outcome of modal choices. We investigate the fleet size required when taking either the perspective of Transit Planning Authority (Agency) or Service Provider (Operator). The model is implemented for the network of Amsterdam North. Results show that the objectives of Agency and Operator yield different total fleet sizes with the Agency requiring a larger fleet than the Operator and that the optimal scenario for the Agency would be the one where only private on-demand service is offered.

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Value of demand information in autonomous mobility-on-demand systems

Cited by 19 publications

References 46 publications

Mobility as a Service: A Review on Recent Development and Future Envision

Mobility as a Service: A Review on Recent Development and Future Envision

An Integrated Decomposition and Approximate Dynamic Programming Approach for On-Demand Ride Pooling

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

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