TY - GEN
T1 - Path Integral Control for Stochastic Dynamic Traffic Routing Problems
AU - Hibbard, Michael
AU - Wasa, Yasuaki
AU - Tanaka, Takashi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - In order to ensure customer satisfaction, ride-hailing providers typically base their routing strategies around minimizing the total traffic delay faced by their users. However, accurately modeling the provider's routing problem is difficult, as realistic traffic networks are non-linear, stochastic, and time-varying. We approach this problem by modelling the dynamic traffic network using the Lighthill, Whitham, and Richards model and use stochastic path integral control to construct the routing strategy of the ride-hailing provider. Differing from previous results, we allow for multiple input and output locations, as well as varying speed limits and maximum traffic densities. Furthermore, we allow the ride-hailing provider to additionally control its traffic flow at splits in the traffic network, which avoids an exponential blow-up in the state space. A numerical example for a representative traffic network is provided to demonstrate the efficacy of the proposed method.
AB - In order to ensure customer satisfaction, ride-hailing providers typically base their routing strategies around minimizing the total traffic delay faced by their users. However, accurately modeling the provider's routing problem is difficult, as realistic traffic networks are non-linear, stochastic, and time-varying. We approach this problem by modelling the dynamic traffic network using the Lighthill, Whitham, and Richards model and use stochastic path integral control to construct the routing strategy of the ride-hailing provider. Differing from previous results, we allow for multiple input and output locations, as well as varying speed limits and maximum traffic densities. Furthermore, we allow the ride-hailing provider to additionally control its traffic flow at splits in the traffic network, which avoids an exponential blow-up in the state space. A numerical example for a representative traffic network is provided to demonstrate the efficacy of the proposed method.
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U2 - 10.1109/CDC42340.2020.9304312
DO - 10.1109/CDC42340.2020.9304312
M3 - Conference contribution
AN - SCOPUS:85099886648
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 261
EP - 267
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -