Related Works of ETA

Key Words: Estimated Time of Arrival (ETA)

Taxonomy

  • Route-based and Route-free
  • Applications: General Cars, Bus, Express
  • Problem: Delivery Time, Bus Delay, Travel Time
  • Solutions:

Comments

Papers

  • ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps, [KDD 2020 Baidu]
    • Taxonomy: General Cars
    • Input: Road network, Historical traffic conditions, Background information
    • Solutions: a spatial-temporal GNN with a novel graph attention mechanism. (first predict context and traffic)
    • Contributions:
      • Tehnical Novelty: (1) Exploite the correlation between spatial and temporal information; (2) Context information (?)
  • HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival, [KDD 2020 Didi]
    • Taxonomy: General Cars, Travel Time
    • Input: Road network, Traffic data
    • Solutions: Attention-based GNN
      • Tehnical Tricks: (1) Hierarchical convolution: recent periods, daily periods, weekly periods.
    • Comments:
      • Good taxonomy: Route-based and Route-free
  • BusTr: Predicting Bus Travel Times from Real-Time Traffic, [KDD 2020 Google]
    • Taxonomy: Bus, Bus Delay
    • Input: Road traffic
    • SoA Limitations: cannot meet the needs of a global-scale transit tracking produc due to cost (for agencies) and privacy (for crowdsourcing methods).
    • Comments:
      • To adopt different, heterogeneous, sparse data sources, training is conducted on a minimal set of features.
      • For reproducibility, they heavily strip down the training data format and data density.
  • Doing in One Go: Delivery Time Inference Based on Couriers’ Trajectories, [KDD 2020 JD]
    • Taxonomy: Express, Delivery Time,
    • Problem Importance: to “ease the burdens on the couriers”.
    • Input: GPS trajectory
    • Contributions:
      • Conceptual: present the first attempt to formalize the delivery time infer- ence problem.
      • Application: the system is deployed in JD Logistics and used internally.
    • SoA: extract staying point.
      • SoA Limitations: mismatch between noisy staying points and geocoded locations.
    • Proposed solutions: (1) Data Pre-processing (trajectory parsing); (2) Delivery Location Correction; (3) Delivery Event-based Matching;

KDD2020-JD-1

Ref.

[KDD 2020 Baidu] Fang, Xiaomin, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, and Haifeng Wang. “ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2697-2705. 2020.

[KDD 2020 Didi] Hong, Huiting, Yucheng Lin, Xiaoqing Yang, Zang Li, Kung Fu, Zheng Wang, Xiaohu Qie, and Jieping Ye. “HetETA: Heterogeneous information network embedding for estimating time of arrival.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2444-2454. 2020.

[KDD 2020 Google] Barnes, Richard, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, and Fangzhou Xu. “BusTr: Predicting Bus Travel Times from Real-Time Traffic.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3243-3251. 2020.

[KDD 2020 JD] Ruan, Sijie, Zi Xiong, Cheng Long, Yiheng Chen, Jie Bao, Tianfu He, Ruiyuan Li, Shengnan Wu, Zhongyuan Jiang, and Yu Zheng. “Doing in One Go: Delivery Time Inference Based on Couriers’ Trajectories.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2813-2821. 2020.