TY - JOUR
T1 - Dynamic Interior Point Method for Vehicular Traffic Optimization
AU - Guo, Chang
AU - Li, Demin
AU - Zhang, Guanglin
AU - Ding, Xiaoning
AU - Curtmola, Reza
AU - Borcea, Cristian
N1 - Funding Information:
Manuscript received February 14, 2019; revised October 11, 2019 and January 1, 2020; accepted March 16, 2020. Date of publication March 31, 2020; date of current version May 14, 2020. This work was supported in part by the NSF of China under Grants 71171045 and 61772130, in part by the Fundamental Research Funds for the Central Universities No. 2232020A-12, in part by the Innovation Program of Shanghai Municipal Education Commission under Grant 14YZ130, in part by the International S&T Cooperation Program of Shanghai Science and Technology Commission under Grant 15220710600, in part by the Fundamental Research Funds for the Central Universities No.17D310404, and in part by the U.S. National Science Foundation under Grants DGE 1565478, SHF 1617749, and CNS 1801430. The review of this article was coordinated by Dr. A. Heydari. (Corresponding author: Guanglin Zhang.) Chang Guo, Demin Li, and Guanglin Zhang are with the College of Information Science and Technology, and Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai 201620, China (e-mail: guochang@mail.dhu.edu.cn; deminli@dhu. edu.cn; glzhang@dhu.edu.cn).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - The aim of this article is to improve vehicular traffic in terms of both travel time and load balance. To achieve this goal, we propose an optimization model that minimizes the sum of the total travel time in the road network and a time representation of the traffic imbalance effects in the network. This paper presents an analytic formulation of the optimization problem, and an algorithm, Dynamic Interior Point Method (DIPM), that solves this optimization through driver rerouting. Unlike user-optimum traffic optimizations, DIPM leads to better fairness for drivers and works well in case of congestion. Unlike other system-wide traffic optimizations, DIPM considers the effects of the driver behavior on traffic load. Together, these features allow our system to work well in a potential real-world deployment. DIPM benefits from a central server that computes driver routes, which is reachable via cellular networks or vehicular ad hoc networks. Theoretical analysis and simulation results demonstrate that DIPM is fast and can work in real-time. The results of extensive simulations with realistic urban maps and traffic scenarios show that DIPM outperforms other dynamic rerouting algorithms in terms of travel time. DIPM also improves fairness when compared with a user-optimum approach.
AB - The aim of this article is to improve vehicular traffic in terms of both travel time and load balance. To achieve this goal, we propose an optimization model that minimizes the sum of the total travel time in the road network and a time representation of the traffic imbalance effects in the network. This paper presents an analytic formulation of the optimization problem, and an algorithm, Dynamic Interior Point Method (DIPM), that solves this optimization through driver rerouting. Unlike user-optimum traffic optimizations, DIPM leads to better fairness for drivers and works well in case of congestion. Unlike other system-wide traffic optimizations, DIPM considers the effects of the driver behavior on traffic load. Together, these features allow our system to work well in a potential real-world deployment. DIPM benefits from a central server that computes driver routes, which is reachable via cellular networks or vehicular ad hoc networks. Theoretical analysis and simulation results demonstrate that DIPM is fast and can work in real-time. The results of extensive simulations with realistic urban maps and traffic scenarios show that DIPM outperforms other dynamic rerouting algorithms in terms of travel time. DIPM also improves fairness when compared with a user-optimum approach.
KW - Travel time optimization
KW - dynamic interior point method
KW - traffic load balance
KW - vehicular networking
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U2 - 10.1109/TVT.2020.2983434
DO - 10.1109/TVT.2020.2983434
M3 - Article
AN - SCOPUS:85085108911
SN - 0018-9545
VL - 69
SP - 4855
EP - 4868
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
M1 - 9051807
ER -