Multi-View Spatial-Temporal Model for Travel Time Estimation

Taxi arrival time prediction is essential for building intelligent transportation systems. Traditional prediction methods mainly rely on extracting features from traffic maps, which cannot model complex situations and nonlinear spatial and temporal relationships. Therefore, we propose Multi-View Spatial-Temporal Model (MVSTM) to capture the mutual dependence of spatial-temporal relations and trajectory features. Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics. Experiments on large-scale taxi trajectory data have shown that our approach is more effective than the existing novel methods. The source code can be found at https://github.com/775269512/SIGSPATIAL-2021-GISCUP-4th-Solution.

[1]  Wei Cao,et al.  When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks , 2018, AAAI.

[2]  Sanjay Chawla,et al.  Inferring the Root Cause in Road Traffic Anomalies , 2012, 2012 IEEE 12th International Conference on Data Mining.

[3]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[4]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[5]  Yu Zheng,et al.  Travel time estimation of a path using sparse trajectories , 2014, KDD.

[6]  Ugur Demiryurek,et al.  Probabilistic estimation of link travel times in dynamic road networks , 2015, SIGSPATIAL/GIS.

[7]  Akbar Siami Namin,et al.  A Comparison of ARIMA and LSTM in Forecasting Time Series , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[8]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[9]  Yu Zheng,et al.  T-share: A large-scale dynamic taxi ridesharing service , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[10]  Xing Xie,et al.  xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems , 2018, KDD.

[11]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[12]  Haifeng Wang,et al.  ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps , 2020, KDD.

[13]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[14]  Yang Liu,et al.  graph2vec: Learning Distributed Representations of Graphs , 2017, ArXiv.

[15]  Wang-Chien Lee,et al.  DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation , 2019, CIKM.

[16]  Justin Dauwels,et al.  Estimating Travel Time Distributions by Bayesian Network Inference , 2020, IEEE Transactions on Intelligent Transportation Systems.

[17]  Zheng Wang,et al.  HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival , 2020, KDD.

[18]  Nicholas Jing Yuan,et al.  T-Finder: A Recommender System for Finding Passengers and Vacant Taxis , 2013, IEEE Transactions on Knowledge and Data Engineering.

[19]  Zheng Wang,et al.  Learning to Estimate the Travel Time , 2018, KDD.

[20]  Gao Cong,et al.  Learning Travel Time Distributions with Deep Generative Model , 2019, WWW.

[21]  Tony Z. Qiu,et al.  Link travel time and delay estimation using transit AVL data , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).