Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach
- Authors:
- DOI:
- 10.1080/19439962.2011.624291
- Abstract:
- This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas. Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable road users such as pedestrians, cyclists, motorcyclists and young drivers. A “safe-system” integrating a system approach for the design of countermeasures and a monitoring process of performance indicators might address the priorities highlighted by the neural networks.
- Type:
- Journal article
- Language:
- English
- Published in:
- Journal of Transportation Safety and Security, 2011, Vol 3, Issue 4, p. 304-323
- Keywords:
- Kohonen networks; Accident factors; Feed-forward back-propagation neural networks; Cluster analysis; Accident patterns
- Main Research Area:
- Science/technology
- Publication Status:
- Published
- Review type:
- Peer Review
- Submission year:
- 2011
- Scientific Level:
- Scientific
- ID:
- 247968523
