Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data

Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.

[1]  Hang Li,et al.  Spatial–temporal travel pattern mining using massive taxi trajectory data , 2018, Physica A: Statistical Mechanics and its Applications.

[2]  Yi Li,et al.  Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries , 2020, IEEE Transactions on Industrial Informatics.

[3]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Hui Liu,et al.  Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles , 2018, Energy.

[5]  Song Gao,et al.  Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling , 2019, Comput. Environ. Urban Syst..

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.

[9]  Wei Guo,et al.  Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale , 2016, ISPRS Int. J. Geo Inf..

[10]  Qingchao Liu,et al.  Short‐Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials , 2018, Comput. Aided Civ. Infrastructure Eng..

[11]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[12]  Wanjing Ma,et al.  Trajectory analysis for on-demand services: A survey focusing on spatial-temporal demand and supply patterns , 2019, Transportation Research Part C: Emerging Technologies.

[13]  Xiao Qin,et al.  Understanding the effects of trip patterns on spatially aggregated crashes with large-scale taxi GPS data. , 2018, Accident; analysis and prevention.

[14]  Ning Feng,et al.  Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting , 2019, AAAI.

[15]  Yunlong Shang,et al.  A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery , 2021, IEEE Transactions on Industrial Electronics.

[16]  Wanli Min,et al.  Real-time road traffic prediction with spatio-temporal correlations , 2011 .

[17]  G.A. Putri Saptawati,et al.  Traffic speed prediction from GPS data of taxi trip using support vector regression , 2017, 2017 International Conference on Data and Software Engineering (ICoDSE).

[18]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[19]  Ying Ding,et al.  Community detection: Topological vs. topical , 2011, J. Informetrics.

[20]  Meng Han,et al.  Discovering Travel Community for POI Recommendation on Location-Based Social Networks , 2019, Complex..

[21]  Yinhai Wang,et al.  Uncovering urban human mobility from large scale taxi GPS data , 2015 .

[22]  Su Yang,et al.  On feature selection for traffic congestion prediction , 2013 .

[23]  Xi Liu,et al.  Revealing daily travel patterns and city structure with taxi trip data , 2013, ArXiv.

[24]  Chao Chen,et al.  Short‐Term Traffic Speed Prediction for an Urban Corridor , 2017, Comput. Aided Civ. Infrastructure Eng..

[25]  Hua Cai,et al.  Understanding taxi travel patterns , 2016 .

[26]  Filipe Moura,et al.  Spatiotemporal Variation of Taxi Demand , 2020, Transportation Research Procedia.

[27]  Yi Li,et al.  Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries , 2019, IEEE Transactions on Transportation Electrification.

[28]  Weifeng Lv,et al.  LSTM variants meet graph neural networks for road speed prediction , 2020, Neurocomputing.

[29]  Haiwei Chen,et al.  Finding Community Structure and Evaluating Hub Road Section in Urban Traffic Network , 2013 .

[30]  Yongjian Yang,et al.  Sparse Data-Based Urban Road Travel Speed Prediction Using Probabilistic Principal Component Analysis , 2018, IEEE Access.

[31]  Yinhai Wang,et al.  Revealing intra-urban travel patterns and service ranges from taxi trajectories , 2017 .