Research on Regional Rail Transit Travel Planning System Based on Passenger Flow Prediction*

2020 ◽  
Author(s):  
Yuxiang Ma ◽  
Wei Dong ◽  
Mengyu Zhang ◽  
Xinya Sun ◽  
Xiao Lu
Keyword(s):  
Planning System ◽  
Rail Transit ◽  
Travel Planning ◽  
Passenger Flow ◽  
Flow Prediction

scholarly journals Short-term passenger flow forecast for urban rail transit based on multi-source data

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Li ◽  
Liying Sui ◽  
Min Zhou ◽  
Hairong Dong
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Urban Rail ◽  
Svm Model

AbstractShort-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

scholarly journals The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method

2013 ◽  
Vol 05 (04) ◽  
pp. 227-231 ◽  
Author(s):  
Qian Li ◽  
Yong Qin ◽  
Ziyang Wang ◽  
Zhongxin Zhao ◽  
Minghui Zhan ◽  
...  
Keyword(s):  
Prediction Method ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Urban Rail

Passenger Flow Prediction Based on Particle Filter Optimization

2013 ◽  
Vol 373-375 ◽  
pp. 1256-1260
Author(s):  
Lei Cao ◽  
Shu Guang Liu ◽  
Xian Hua Zeng ◽  
Pan He ◽  
Yue Yuan
Keyword(s):  
Particle Filtering ◽  
Current Flow ◽  
Rail Transit ◽  
Genetic Optimization ◽  
Flow Volume ◽  
Ic Card ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Chongqing City

The article aims at making scientific and accurate prediction of the current flow of passagers based on its characteristics, which is nonlinear and is influenced by various factors. Clustering is used to make classification of IC card data.Then ARMA prediction model is installed and the model parameter is worked out through matrix method and revised by the method of particle filtering. Resampling is also conducted through genetic optimization. The data of rail transit of Chongqing City of June, 2012 is used to make verification.The result shows that the prediction of optimize parameters through the method of particle filtering is close to the real values. The MAE is 0.8154,MAPE is 1.755,so this method in our article can make the prediction of passenger flow volume more accurately.

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