TY - GEN
T1 - Enhanced Delta-tolling
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
AU - Mirzaei, Hamid
AU - Sharon, Guni
AU - Boyles, Stephen
AU - Givargis, Tony
AU - Stone, Peter
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - In the micro-tolling paradigm, a centralized system manager sets different toll values for each link in a given traffic network with the objective of optimizing the system's performance. A recently proposed micro-tolling scheme, denoted \Delta-tolling, was shown to yield up to 32% reduction in total travel time when compared to a no-toll scheme. \Delta-tolling, computes a toll value for each link in a given network based on two global parameters: \beta which is a proportional parameter and R which controls the rate of toll change over time. In this paper, we propose to generalize \Delta-tolling such that it would consider different R and \beta parameters for each link. a policy gradient reinforcement learning algorithm is used in order to tune this high-dimensional optimization problem. The results show that such a variant of \Delta-tolling far surpasses the original \Delta-tolling scheme, yielding up to 38% reduced system travel time compared to the original \Delta-tolling scheme.
AB - In the micro-tolling paradigm, a centralized system manager sets different toll values for each link in a given traffic network with the objective of optimizing the system's performance. A recently proposed micro-tolling scheme, denoted \Delta-tolling, was shown to yield up to 32% reduction in total travel time when compared to a no-toll scheme. \Delta-tolling, computes a toll value for each link in a given network based on two global parameters: \beta which is a proportional parameter and R which controls the rate of toll change over time. In this paper, we propose to generalize \Delta-tolling such that it would consider different R and \beta parameters for each link. a policy gradient reinforcement learning algorithm is used in order to tune this high-dimensional optimization problem. The results show that such a variant of \Delta-tolling far surpasses the original \Delta-tolling scheme, yielding up to 38% reduced system travel time compared to the original \Delta-tolling scheme.
UR - http://www.scopus.com/inward/record.url?scp=85060470251&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569737
DO - 10.1109/ITSC.2018.8569737
M3 - Conference contribution
AN - SCOPUS:85060470251
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 47
EP - 52
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers
Y2 - 4 November 2018 through 7 November 2018
ER -