Fleet operational policies for automated mobility: A simulation assessment for Zurich

Hörl, Sebastian; Ruch, Claudio; Bécker, Félix; Frazzoli, Emilio; Axhausen, Kay W.

doi:10.1016/j.trc.2019.02.020

Cited by 131 publications

(90 citation statements)

References 25 publications

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“…It is noteworthy that the selected dispatch and rebalancing algorithm may have a strong impact on service performance indicators (Hörl et al, 2019;Hyland and Mahmassani, 2018). Particularly, when the demand for SAV service is relatively high, applying simplified assignments (e.g.…”

Section: Ridesharing and Rebalancingmentioning

confidence: 99%

“…Second, the population (about 500,000 inhabitants) and the metropolitan area network sizes allow us to perform the simulation with an acceptable downscaling rate (10%), which results in quite accurate outputs compared to the full-scale model (Bischoff and Maciejewski, 2016). Actually, in some studies relying on agent-based simulation and utility scoring, the population of case study areas is highly downscaled (1%) due to the high computational time (Hörl et al, 2019;Kamel et al, 2019). This extensive downscaling may potentially affect the service performance evaluations considering the spatiotemporal interaction of supply and demand in large study areas.…”

Section: Simulated Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

108

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Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multi-modal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated.Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.

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Section: Ridesharing and Rebalancingmentioning

confidence: 99%

Section: Simulated Scenariosmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

108

View full text Add to dashboard Cite

show abstract

“…Reactive, time-invariant methods span from simple bipartite matching, to control methods based on fluidic frameworks. A good comparison of different reactive controllers can be found in [3] and [4], where, notably, both studies show that the controller first proposed in [5] performs competitively across tests. However, these controllers do not provide a natural way to leverage travel demand forecasts.…”

Section: Introductionmentioning

confidence: 91%

Stochastic Model Predictive Control for Autonomous Mobility on Demand

Tsao

Iglesias

Pavone

2018

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

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This paper presents a stochastic, model predictive control (MPC) algorithm that leverages short-term probabilistic forecasts for dispatching and rebalancing Autonomous Mobility-on-Demand systems (AMoD), i.e. fleets of self-driving vehicles. We first present the core stochastic optimization problem in terms of a time-expanded network flow model. Then, to ameliorate its tractability, we present two key relaxations. First, we replace the original stochastic problem with a Sample Average Approximation, and provide its performance guarantees. Second, we divide the controller into two submodules. The first submodule assigns vehicles to existing customers and the second redistributes vacant vehicles throughout the city. This enables the problem to be solved as two totally unimodular linear programs, allowing the controller to scale to large problem sizes. Finally, we test the proposed algorithm in two scenarios based on real data and show that it outperforms prior state-of-the-art algorithms. In particular, in a simulation using customer data from the ridesharing company DiDi Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in customer waiting time compared to state of the art nonstochastic algorithms.

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“…In conjunction with society's interest in AMoD technologies, there is extensive literature emerging that studies the different aspects of AMoD systems. The potential impact of shared mobility services on daily urban mobility [2], the analysis of AMoD systems with realistic demand [3], autonomous vehicle behavior in existing traffic models [4], and rebalancing algorithms [5] have been investigated using simulation frameworks. The interplay between AMoD and public transport has been studied in [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

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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Fleet operational policies for automated mobility: A simulation assessment for Zurich

Cited by 131 publications

References 25 publications

Shared autonomous vehicle simulation and service design

Shared autonomous vehicle simulation and service design

Stochastic Model Predictive Control for Autonomous Mobility on Demand

Smart Charging Benefits in Autonomous Mobility on Demand Systems

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