Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Shapes based trajectory queries for moving objects
Proceedings of the 13th annual ACM international workshop on Geographic information systems
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The VLDB Journal — The International Journal on Very Large Data Bases
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Estimating Beijing's travel delays at intersections with floating car data
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science
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In this paper, we aim to mine turn delay at different times and turn types in city road network based on personal GPS collected trajectories. We provide a method to effectively solve the problem for computing turn delay. By using this method, we can rapidly process massive trajectory data, to explore and predict turn delay in city road network. Through map-matching and pre-processing work for trajectory data, we firstly extract turn delay records from the time that people pass across a road intersection. Limited by the range of trajectory collection, these turn delay records cannot cover all road intersection and all different times. Therefore, we secondly propose a prediction model based on Neural Networks to handle these records. In this prediction model we have considered both geography neighborhood effect and topological relationship of road intersections. Finally, we tested the efficiency of this method through cross-validation by using 8986 trajectories derived from 165 pedestrians in a time period of three years. It demonstrates that the proposed method can obtain a higher accuracy of turn delay prediction than traditional methods which usually ignore topological characteristics of road intersections.