TY - GEN
T1 - Optimization of Public Bus Scheduling using Real-Time Online Information
AU - Ho, Teng Hei
AU - Wang, Ke
AU - Xu, Man
AU - Lam, Chan Tong
AU - Ng, Benjamin K.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Due to increasing populations and congested traffics, public transportation arrangements are crucial for smart cities. In this paper, a joint method is proposed to optimize public bus scheduling, using real-time online bus information. Specifically, we first introduce three parameters, i.e., number of buses, number of stops, and dwell time, to be the inputs of the optimization process. Then, K-means clustering and genetic algorithms are used to optimize collaboratively. Different from traditional genetic algorithms, the proposed method can effectively avoid local optimal results. Besides, we propose two metrics to evaluate the system performance. Real-time online data-based experiments show the effectiveness and robustness of our scheme. As a result, the number of buses and operation efficiency can decrease by 28% to 47% and enhance by 3% to 12%, respectively.
AB - Due to increasing populations and congested traffics, public transportation arrangements are crucial for smart cities. In this paper, a joint method is proposed to optimize public bus scheduling, using real-time online bus information. Specifically, we first introduce three parameters, i.e., number of buses, number of stops, and dwell time, to be the inputs of the optimization process. Then, K-means clustering and genetic algorithms are used to optimize collaboratively. Different from traditional genetic algorithms, the proposed method can effectively avoid local optimal results. Besides, we propose two metrics to evaluate the system performance. Real-time online data-based experiments show the effectiveness and robustness of our scheme. As a result, the number of buses and operation efficiency can decrease by 28% to 47% and enhance by 3% to 12%, respectively.
KW - bus scheduling
KW - genetic algorithm
KW - jointly optimization
KW - means clustering
UR - http://www.scopus.com/inward/record.url?scp=85152223379&partnerID=8YFLogxK
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00280
DO - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00280
M3 - Conference contribution
AN - SCOPUS:85152223379
T3 - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
SP - 1863
EP - 1868
BT - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
Y2 - 18 December 2022 through 20 December 2022
ER -
