Enhancing Real-Time Crash Risk Prediction Performance Considering Spatial and Temporal Correlations in Support Vector Machine

Unobserved heterogeneity in crash data could affect the predicting accuracy of crash risks. Such effects can be considered within the spatial and temporal correlation to improve the model prediction performance. This study aims at proposing an enhanced support vector machine (SVM) model that involves the spatial and temporal weight features in the model structure to address the spatial and temporal proximity in the real-time crash risk predictions. A total of 254 crash data on the Interstate 80 were obtained. Traffic flow data 5 min before the occurrence of each crash were extracted to be the case database. Non-crash traffic flow data were randomly extracted from the collision free periods to be the control database. The Receiver Operating Characteristics (ROC) curves were drawn to evaluate and compare the prediction performance of different models. The results showed that by incorporating the spatial and temporal correlations in the SVM, the model fitness was improved: the predicting accuracy was increased from 79.8% to 86.5% as compared to the basic SVM model. Two weight matrixes of spatial and temporal correlation in the SVM were tested, and the models with the 0-1 first order weight feature had the highest predicting accuracy. The authors also tested the modeling accuracy for different ratios of training and testing sample sizes. Findings of this study suggest that the proposed SVM model with the spatial and temporal correlation can effectively improve the predicting accuracy of real-time crash risks based on the traffic variables from loop detector stations.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Liu, Tong
  • Li, Zhibin
  • Pu, Ziyuan
  • Xu, Chengcheng
  • Li, Meng
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Tables;
• Pagination: 7p

Subject/Index Terms

• TRT Terms: Crash risk forecasting; Mathematical prediction; Real time information; Traffic data; Traffic flow
• Uncontrolled Terms: Support vector machines
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01698140
• Record Type: Publication
• Report/Paper Numbers: 19-03963
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM