Abstract
Green logistics and environmentally-friendly logistics necessitates transport system to be reliable for delivery. The reliability of transport system is usually measured by travel time reliability (TTR). Compared with the TTR on a single road or path, the TTR of trip (delivery) seems more important for managers and logistics operators in decision making. To estimate the trip-based reliability, this paper firstly defines the trip-based reliability as ‘the arriving late risk between an OD pair’. In the trip-based reliability, OD pair rather than path or road is chosen as the object, which is different from the existing TTR. Aggregating the trips with the same origin in a specific time interval, we then introduce network travel risk (NTR) to evaluate the reliability of zone. Further, this paper develops a temporal graph neural network with heterogeneous features (TGCNHF) to provide the real-time NTR. In this model, features are divided into tendency-based and periodicity-based and handled respectively by two 1-D convolution layers on time axis. After stacking the length of time intervals to 1, a graph convolution is employed to extract the spatial correlation. Then, a fully connected layer with a SoftMax function accomplishes the NTR prediction. To test the proposed TGCNHF, a real-world travel time dataset collected in Beijing main urban area is used in comparison. The results show that our TGCNHF model can extract the spatio-temporal correlation from traffic data and the predictions overperform the state-of-art baselines on real-world traffic datasets.
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This research was supported in National Natural Science Foundation of China (71961137008).
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Appendix A: Abbreviations
Appendix A: Abbreviations
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Fang, K., Fan, J. & Yu, B. A trip-based network travel risk: definition and prediction. Ann Oper Res 343, 1069–1094 (2024). https://doi.org/10.1007/s10479-022-04630-6
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DOI: https://doi.org/10.1007/s10479-022-04630-6