|Table of Contents|

Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年04期
Page:
210-222
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics
Author(s):
HUI Yang1; 2;  WANG Yong-gang1;  PENG Hui1;  HOU Shu-qian3
(1. Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Transportation Soft Science Research Center, Chang'an University, Xi'an 710064, Shaanxi, China; 3. Xi'an Rail Transit Group Company Limited, Xi'an 710018, Shaanxi, China)
Keywords:
urban rail transit;  passenger flow prediction;  coupled spatial-temporal characteristic;  back propagation neural network;  particle swarm optimization algorithm;  adaptive mutation;  inertia weight
PACS:
U293.13
DOI:
10.19818/j.cnki.1671-1637.2021.04.016
Abstract:
To improve the accuracy of subway passenger flow prediction, by considering the Xi'an Metro Line 1 as an example, five main factors affecting subway passenger flow variations, such as festival, non-festival, time period, station, and weather, were extracted to analyze the coupled spatial-temporal characteristics of subway passenger flow. A back propagation(BP)neural network was constructed to predict the subway passenger flow. The proposed BP neural network was further optimized by using a particle swarm optimization(PSO)algorithm that introduced adaptive mutation and balanced inertia weights to form a subway passenger flow prediction system that could consider complex influence factors. Transfer stations and non-transfer stations including a first and an intermediate station were selected, the weather, festival, and non-festival factors were considered, and the BP neural network models for different time periods were compared. Then, the prediction errors of the PSO-BP neural network model were optimized. Research results show that by considering the weather, festival and non-festival factors, the mean absolute error(MAE), root mean square error(RMSE), and mean absolute percentage error(MAPE)of the optimized PSO-BP neural network model predictions at transfer stations within the optimized time periods decrease by 40.13%, 31.46% and 23.89%, respectively, compared with the optimized PSO-BP neural network models prediction errors without the time periods, decrease by 17.50%, 17.86% and 17.32% compared with the BP neural network models prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions in the non-transfer stations within the optimized time periods decrease by 16.50%, 20.99% and 32.59%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 11.48%, 12.10% and 17.73%, respectively, compared with the BP neural network model prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions at each station within the optimized time periods decrease by 24.37%, 24.48% and 29.69%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 13.49%, 14.02% and 17.59%, respectively, compared with the BP neural network model prediction errors within the given time periods. Therefore, using the optimized PSO-BP neural network model and considering the influencing factors can improve the accuracy of subway passenger flow prediction. 8 tabs, 12 figs, 30 refs.

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Last Update: 2021-09-01