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A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 362;
Received: 2 September 2017 / Revised: 20 October 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies. View Full-Text
Keywords: spatial analysis; traffic incidents; dynamic prediction; road network spatial analysis; traffic incidents; dynamic prediction; road network

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Liu, C.; Zhang, S.; Wu, H.; Fu, Q. A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks. ISPRS Int. J. Geo-Inf. 2017, 6, 362.

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