Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms

Rossi, Federico; Zhang, Rick; Hindy, Yousef; Pavone, Marco

doi:10.1007/s10514-018-9750-5

Cited by 131 publications

(98 citation statements)

References 38 publications

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“…To prove Proposition 2, we first state the following lemmas: . The lemma simply follows from the balance condition (11). We exclude the proof for brevity.…”

Section: B Proof Of Propositionmentioning

confidence: 99%

“…On the modeling side, queueing theoretical models capture the stochasticity of the customers [8], while network flow models efficiently optimize the fleet control in a static setting [9]. Owing to their simplicity, network flow based formulations are commonly used for algorithmic control of routing and rebalancing in a receding-horizon fashion [10], and to control congestion effects [11]. In addition to AMoD technology, the transportation sector is looking to increase utilization of electric vehicles (EVs) for consumers, companies, and fleet operations.…”

Section: Introductionmentioning

confidence: 99%

“…When we have the proposed characteristic of the stationary distribution, the recursive relation between d(v, p) and d ( v − 1, p) can be written as follows based on (11):…”

mentioning

confidence: 99%

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Smart Charging Benefits in Autonomous Mobility on Demand Systems

Turan

Tucker

Alizadeh

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.

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“…To prove Proposition 2, we first state the following lemmas: . The lemma simply follows from the balance condition (11). We exclude the proof for brevity.…”

Section: B Proof Of Propositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Turan

Tucker

Alizadeh

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…Topic Reference Optimization Fleet sizing [38], [73], [92], [93], [98], [115], [130] Vehicle routing / trip assignment [81], [98], [106], [120], [123], [125], [127], [131]- [136] Vehicle rebalancing / relocation…”

Section: Approachmentioning

confidence: 99%

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

Zhao

Malikopoulos

2022

IEEE Intell. Transport. Syst. Mag.

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Shared mobility can provide access to transportation on a custom basis without vehicle ownership. The advent of connected and automated vehicle technologies can further enhance the potential benefits of shared mobility systems. Although the implications of a system with shared autonomous vehicles have been investigated, the research reported in the literature has exhibited contradictory outcomes. In this paper, we present a summary of the research efforts in shared autonomous vehicle systems that have been reported in the literature to date and discuss potential future research directions.

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“…The impact on congestion arising from unallocated vehicle trips in MoD system has been numerically explored in [5] and [4]. One of the proposed mitigation is strategies is congestion-aware fleet routing [6].…”

Section: Related Workmentioning

confidence: 99%

The Impact of Ridesharing in Mobility-on-Demand Systems: Simulation Case Study in Prague

Fiedler

Čertický

Alonso–Mora

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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In densely populated-cities, the use of private cars for personal transportation is unsustainable, due to high parking and road capacity requirements. The mobility-ondemand systems have been proposed as an alternative to a private car. Such systems consist of a fleet of vehicles that the user of the system can hail for one-way point-to-point trips. These systems employ large-scale vehicle sharing, i.e., one vehicle can be used by several people during one day and consequently the fleet size and the parking space requirements can be reduced, but, at the cost of a non-negligible increase in vehicles miles driven in the system. The miles driven in the system can be reduced by ridesharing, where several people traveling in a similar direction are matched and travel in one vehicle. We quantify the potential of ridesharing in a hypothetical mobility-on-demand system designed to serve all trips that are currently realized by private car in the city of Prague. Our results show that by employing a ridesharing strategy that guarantees travel time prolongation of no more than 10 minutes, the average occupancy of a vehicle will increase to 2.7 passengers. Consequently, the number of vehicle miles traveled will decrease to 35 % of the amount in the MoD system without ridesharing and to 60% of the amount in the present state.

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Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms

Cited by 131 publications

References 38 publications

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

The Impact of Ridesharing in Mobility-on-Demand Systems: Simulation Case Study in Prague

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