| Author Name | Affiliation | | Chao Yang | Key Laboratory of Road and Traffic Engineering of the Ministry of Education,School of Transportation Engineering, Tongji University, Shanghai 201804, China | | Xiaolei Ru | Key Laboratory of Road and Traffic Engineering of the Ministry of Education,School of Transportation Engineering, Tongji University, Shanghai 201804, China | | Bin Hu | Key Laboratory of Road and Traffic Engineering of the Ministry of Education,School of Transportation Engineering, Tongji University, Shanghai 201804, China |
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| Abstract: |
| Bus arrival time prediction contributes to the quality improvement 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: |
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| Descriptions in Chinese: |
| 基于线路时空信息的残差神经网络预测公交到达时间 #$TAB 杨超，茹小磊，胡斌* （同济大学 交通工程学院 道路与交通工程教育部重点实验室，上海201804） 中文说明：公交到站时间预测有助于提高公共交通的服务质量。如果能提前知道准确的公交到达时间，乘客就可以有效地安排出发时间。本文提出一种机器学习方法，RTSI-ResNet模型，来预测公交到达目标车站的时间。利用残差神经网络框架对公交线路时空信息进行建模。研究发现，两站点之间路段的公交行程时间不仅与同路段前一班次公交行程时间存在相关性，而且与临近的上下游路段的公交出行时间也存在共同的变化趋势。从包含目标路段的正反方向公交路线中提取公交出行时间和车头时距两个特征，构成公交路线时空信息，综合反映道路交通状况。对深圳公共交通系统10号公交线路的轨迹数据进行实验，结果表明本文提出的RTSI-ResNet模型优于其他著名的方法(如RNN/LSTM、SVM等)。特别地，当公交车与目标车站的距离越远时，预测精度的优势越明显。 关键词：公交到达时间预测; 线路时空信息; 残差神经网络; 递归神经网络; 公交轨迹数据 |
