A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems

We study route-planning for Autonomous Mobility-on-Demand (AMoD) systems that accounts for the impact of road traffic on travel time. Specifically, we develop a congestion-aware routing scheme (CARS) that captures road-utilization-dependent travel times at a mesoscopic level via a piecewise affine a...

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Bibliographic Details
Published in2019 18th European Control Conference (ECC) pp. 3040 - 3046
Main Authors Salazar, Mauro, Tsao, Matthew, Aguiar, Izabel, Schiffer, Maximilian, Pavone, Marco
Format Conference Proceeding
LanguageEnglish
Published EUCA 01.06.2019
Subjects
Automobiles
Computational modeling
Numerical models
Prediction algorithms
Real-time systems
Road traffic
Roads
Routing
Stochastic processes
System performance
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DOI10.23919/ECC.2019.8795897

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Summary:We study route-planning for Autonomous Mobility-on-Demand (AMoD) systems that accounts for the impact of road traffic on travel time. Specifically, we develop a congestion-aware routing scheme (CARS) that captures road-utilization-dependent travel times at a mesoscopic level via a piecewise affine approximation of the Bureau of Public Roads (BPR) model. This approximation largely retains the key features of the BPR model, while allowing the design of a real-time, convex quadratic optimization algorithm to determine congestion-aware routes for an AMoD fleet. Through a real-world case study of Manhattan, we compare CARS to existing routing approaches, namely a congestion-unaware and a threshold congestion model. Numerical results show that CARS significantly outperforms the other two approaches, with improvements in terms of travel time and global cost in the order of 20%.
DOI:10.23919/ECC.2019.8795897