Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

IEEE Access

Zhiwei Cheng¹  Xiaodan Wang²  Yun Bai²  Chuan Li² 

[1] Chongqing CPI Zineng Science and Technology Company, Ltd., Chongqing, China;National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China;

关键词: Affinity propagation;    passenger flow;    particle swarm optimization;    forecasting;    support vector regression;   

DOI  :  10.1109/ACCESS.2020.2967867

来源: DOAJ

【 摘 要 】

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.

【 授权许可】

Unknown