TY - JOUR
T1 - Solving Traffic Signal Scheduling Problems in Heterogeneous Traffic Network by Using Meta-Heuristics
AU - Gao, Kaizhou
AU - Zhang, Yicheng
AU - Su, Rong
AU - Yang, Fajun
AU - Suganthan, Ponnuthurai Nagaratnam
AU - Zhou, Meng Chu
N1 - Funding Information:
Manuscript received April 10, 2018; revised August 27, 2018 and September 17, 2018; accepted September 23, 2018. Date of publication November 8, 2018; date of current version August 27, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61603169 and in part by the Economic Development Board, Singapore, for the project of Development of NTU/NXP Smart Mobility Test-Bed under Grant S15-1105-RF-LLF URBAN. The Associate Editor for this paper was F. Chu. (Corresponding author: MengChu Zhou.) K. Gao is with the Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China, and also with the School of Computer, Liaocheng University, Liaocheng 252000, China (e-mail: gaokaizh@aliyun.com).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.
AB - This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.
KW - Jaya
KW - Traffic signal scheduling
KW - artificial bee colony
KW - genetic algorithm
KW - harmony search
KW - water cycle algorithm
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U2 - 10.1109/TITS.2018.2873790
DO - 10.1109/TITS.2018.2873790
M3 - Article
AN - SCOPUS:85056297804
SN - 1524-9050
VL - 20
SP - 3272
EP - 3282
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 9
M1 - 8527673
ER -