Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method

LIANG Quan; WENG Jiancheng; HU Juanjuan; HAN Bing

doi:10.3963/j.jssn.1674-4861.2021.04.009

Volume 39 Issue 4

Aug. 2021

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LIANG Quan, WENG Jiancheng, HU Juanjuan, HAN Bing. Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method[J]. Journal of Transport Information and Safety, 2021, 39(4): 68-76. doi: 10.3963/j.jssn.1674-4861.2021.04.009

Citation:

LIANG Quan, WENG Jiancheng, HU Juanjuan, HAN Bing. Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method[J]. Journal of Transport Information and Safety, 2021, 39(4): 68-76. doi: 10.3963/j.jssn.1674-4861.2021.04.009

Citation:

LIANG Quan, WENG Jiancheng, HU Juanjuan, HAN Bing. Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method[J]. Journal of Transport Information and Safety, 2021, 39(4): 68-76. doi: 10.3963/j.jssn.1674-4861.2021.04.009

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Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method

doi: 10.3963/j.jssn.1674-4861.2021.04.009

1.
Road Teaching and Research Department, Transport Management Institute Ministry of Transport of the People's Republic of China, Beijing 101601, China
2.
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China

Received Date: 2020-07-04

Abstract

Abstract

Accurate grasp of the destinations of public transport commuters can clarify travel needs of passengers and improve public transport service. The data of public transport in one-month and the revealed preference(RP)survey in Beijing are collected. The travel chain of 563 public transport commuters is obtained through the association analysis of smart card numbers, transaction data, and network data. A total of 302 public transport commuters with high, medium, and low public travel stability are identified by association rules. The XGBoost integrated learning algorithm is introduced to develop a prediction model of the next travel destination for individual public transport commuters with different travel stabilities. The factors significantly influencing travel destinations are input variables. The following trip destination is the output variable. The prediction model is constructed by adjusting and optimizing parameters repeatedly. The destination prediction accuracy of passengers with high, medium, and low stability is 90%, 66.67%, and 50%, respectively. Besides, the transfer probability of the graph is utilized to revise the predicted results. The prediction accuracy is improved to 91.2%, 83.21%, and 69.5%. The transfer probability of the graph can improve the prediction accuracy of the passengers' travel destinations with medium and low stability. The destination data from the bus metropolitan system is used to compare and verify the aggregation results of destination prediction for the next trip.The absolute percentage error of the predicted value and the true value-changing gradient is less than 10%. Thus, the method of travel destination prediction by combining XGBoost and travel graph correction based on dividing public transport commuters' travel stability has high accuracy.
- urban transport,
- public transport commuter,
- destination prediction,
- XGBoost algorithm,
- travel graph

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References(24)

References

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Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method

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