Prediction Method of Bus Passenger Flow Based on RS-IPSOSVM
HUANG Yishao1,2, HAN Lei2,3
(1. Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety, Ministry of Education,
Changsha University of Science & Technology, Changsha 410114, Hunan, China; 2. School of Traffic and
Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China;
3. Key Laboratory of Road & Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China)
Abstract：In order to improve the accuracy of bus passenger flow forecasting, based on bus IC card data, the variation law and the influencing factors of bus passenger flow were explored, and a prediction method of bus passenger flow based on rough set (RS), improved particle swarm optimization (IPSO) and support vector machine (SVM) was proposed. Firstly, the influencing factors of bus passenger flow were determined by deep mining of passenger flow data. Secondly, rough set was used to reduce the attributes of 13 initial influencing factors and eliminate redundant information to get 8 core influencing factors.Thirdly, the adaptive inertia weight and asynchronous learning factors were introduced to optimize the PSO algorithm, and the IPSO algorithm was used to find the global optimal parameters of the SVM. The kernel function was used to map the core influencing factors of bus passenger flow into high-dimensional space, and the nonlinear mapping relationship between the core influence factors and the bus passenger flow was fitted to realize the passenger flow prediction. Finally, the proposed method was verified by the data of bus passenger flow in Guangzhou. The results show that the prediction accuracy of the proposed method is above 90%, which simplifies the training samples, overcomes the blindness of SVM parameter selection, and effectively improves the practicability and reliability.
黄益绍1,2，韩磊2,3. 基于RS-IPSOSVM的公交客流量预测方法[J]. 重庆交通大学学报（自然科学版）, 2020, 39(11): 11-19.
HUANG Yishao1,2, HAN Lei2,3. Prediction Method of Bus Passenger Flow Based on RS-IPSOSVM. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(11): 11-19.
［1］ LIU L J, CHEN R C. A novel passenger flow prediction model using deep learning methods［J］. Transportation Research Part C: Emerging Technologies, 2017, 84: 74 -91.
［2］ ZHANG J, SHEN D Y, TU L, et al. A real-time passenger flow estimation and prediction method for urban bus transit systems［J］. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3168 - 3178.
［3］ 张春辉, 宋瑞, 孙杨. 基于卡尔曼滤波的公交站点短时客流预测［J］. 交通运输系统工程与信息, 2011, 11(4): 154-159.
ZHANG Chunhui, SONG Rui, SUN Yang. Kalman filter-based short-term passenger flow forecasting on bus stop［J］. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(4): 154-159.
［4］ WILLIAMS B M, HOEL L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results［J］. Journal of Transportation Engineering, 2003, 129(6): 664-672.
［5］ GAN M, CHENG Y, LIU K, et al. Seasonal and trend time series forecasting based on a quasilinear autoregressive model［J］. Applied Soft Computing, 2014, 24: 13-18.
［6］ 张晓利, 陆化普. 非参数回归方法在短时交通流预测中的应用［J］. 清华大学学报(自然科学版), 2009, 49(9): 1471-1475.
ZHANG Xiaoli, LU Huapu. On-parametric regression for short-term traffic flow forecasting［J］.Journal of Tsinghua University (Science and Technology), 2009, 49(9): 1471-1475.
［7］ HOFLEITNER A, HERRING R, BAYEN A. Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning ［J］.Transportation Research Part B, 2012, 46(9): 1097-1122.
［8］ 高为, 陆百川, 贠天鹂, 等. 基于时空特性和RBF神经网络的短时交通流预测［J］. 交通信息与安全, 2011, 29(1):16-19.
GAO Wei, LU Baichuan, YUN Tianli, et al. Short-term traffic flow forecasting based on spatiotemporal characteristics of traffic flow and RBF neural network［J］. Journal of Transportation Information and Safety, 2011, 29(1): 16-19.
［9］ CASTRO-NETO M, JEONG Y S, JEONG M K, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions［J］. Expert Systems with Applications, 2009, 36(3): 6164-6173.
［10］ 刘晓玲, 贾鹏, 邬珊华, 等. 基于多维时空参数的道路短期交通状态预测［J］. 交通运输系统工程与信息, 2011, 11(4): 140-146.
LIU Xiaoling, JIA Peng, WU Shanhua, et al. Short-term traffic flow forecasting based on multi-dimensional parameters［J］.Journal of Transportation Systems Engineering and Information Technology, 2011, 11(4): 140-146.
［11］ 于滨, 邬珊华, 王明华,等. K近邻短时交通流预测模型［J］. 交通运输工程学报, 2012, 12(2): 105-115.
YU Bin, WU Shanhua, WANG Minghua，et al. K-nearest neighbor model of short-term traffic flow forecast［J］. Journal of Traffic and Transportation Engineering, 2012, 12(2): 109-115.
［12］ ZHENG W Z, LEE D H, SHI Q X, et al. Short-term freeway traffic flow prediction: Bayesian combined neural network approach［J］. Journal of Transportation Engineering, 2011, 132(2): 114-121.
［13］ HONG W C, DONG Y C, ZHENG F F, et al. Forecasting urban traffic flow by SVR with continuous ACO［J］. Applied Mathematical Modelling, 2011, 35(3): 1282- 1291.
［14］ WANG J S, CHIANG J C. A cluster validity measure with outlier detection for support vector clustering［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008, 38(1): 78-89.
［15］ KENNEDY J, EBERHART R C. Particle swarm optimization［C］∥ IEEE International Conference on Neural Networks. Piscataway: IEEE Service Center, 1995: 1942- 1948.
［16］ 刘晶晶. 粒子群优化算法的改进与应用［D］. 武汉: 武汉理工大学, 2007.
LIU Jingjing. The Improvement and Application upon the Particle Swarm Optimization［D］. Wuhan: Wuhan University of Technology, 2007.
［17］ 邓艳青. 粒子群优化算法的改进及其应用研究［D］. 长沙:中南大学, 2012.
DENG Yanqing.An Improvement of Particle Swarm Pptimization Algorithm and Its Application［D］. Changsha: Central South University, 2012.
［18］ ZHANG Fanping, DAI Huichao, TANG Deshan. A conjunction method of wavelet transform-particle swarm optimization-support vector machine for streamflow forecasting［J］. Journal of Applied Mathematics, 2014, 2014:1-10.
［19］ 徐文远, 邓春瑶, 刘宝义. 基于公交IC卡数据的公交客流统计方法［J］. 中国公路学报, 2013, 26(5): 158-163.
XU Wenyuan, DENG Chunyao, LIU Baoyi. Approach on public traffic passenger flow statistics based on IC data［J］. China Journal of Highway and Transport, 2013, 26 (5): 158-163.
［20］ PAWLAK Z, SKOWRON A. Rudiments of rough sets［J］. Information Sciences, 2007, 177(1): 3-27.
2020, Vol. 39 
