| Author Name | Affiliation | Postcode | | Chao Yang | Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, China | 201804 | | Xiaolei Ru | Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, China | 201804 | | Bin Hu* | Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, China | 201804 |
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| Abstract: |
| Bus arrival time prediction contributes to improving the quality of public transport services. Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance. We proposed a machine-learning approach, RTSI-ResNet, to forecast the bus arrival time at target stations. The residual neural network framework was employed to model the bus route temporal-spatial information. It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses, but also had common change trends with nearby downstream/upstream segments. Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal-spatial information, which reflects the road traffic conditions comprehensively. Experiments on the bus trajectory data of route NO. 10 in Shenzhen public transport system demonstrated that the proposed RTSI-ResNet outperformed other well-known methods (e.g., RNN/LSTM, SVM). Specifically, the advantage was more significant when the distance between bus and the target station was farther. |
| Key words: bus arrival time prediction route temporal-spatial information residual neural network recurrent neural network bus trajectory data |
| DOI：10.11916/j.issn.1005-9113.2018007 |
| Clc Number:U121 |
| Fund: |
