Road Network Safety Ranking Using Accident Prediction Models
Road network safety ranking procedure and its implementation is not a strictly regulated road safety activity. This is one of the most flexible ways to determine the most effective and beneficious road safety investments. This paper analyses possibilities for implementing road network safety ranking according to the accidents prediction. The application of road network safety ranking procedure enables to prevent road accidents, i.e. to implement a proactive road safety activity. Road accident predictions are made using the empirical Bayes method, which is based on the assumption that in a similar environment with the similar traffic conditions the risk of accidents is similar. In order to implement this method, all roads of national significance of Lithuania were divided into homogeneous road sections and junctions. The homogenous road groups were determined based on 2012‒2016 data of geometrical parameters of the road and traffic volume. Having estimated the predicted number of accidents for each homogenous road section, it is possible to calculate the predicted accident rate for each road. The authors of the paper, have predicted accident rate for the whole road, compiled a map of road safety levels for the trans-European roads in Lithuanian.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030223748
-
Supplemental Notes:
- © Springer Nature Switzerland AG 2020.
-
Authors:
- Jasiūnienė, Vilma
- Ratkevičiūtė, Kornelija
- Peltola, Harri
-
Conference:
- International Conference on Vision Zero for Sustainable Road Safety in Baltic Sea Region (VISZERO 2018)
- Location: Vilnius , Lithuania
- Date: 2018-12-5 to 2018-12-6
- Publication Date: 2019-6
Language
- English
Media Info
- Media Type: Web
- Edition: 1
- Features: References;
- Pagination: pp 166-176
- Monograph Title: Vision Zero for Sustainable Road Safety in Baltic Sea Region: Proceedings of the International Conference “Vision Zero for Sustainable Road Safety in Baltic Sea Region”, 5–6 December 2018, Vilnius, Lithuania
-
Serial:
- Lecture Notes in Intelligent Transportation and Infrastructure
- Publisher: Springer Cham
- ISSN: 2523-3440
- EISSN: 2523-3459
- Serial URL: https://www.springer.com/series/15991
Subject/Index Terms
- TRT Terms: Bayes' theorem; Crash risk forecasting; Highway safety; Ranking (Statistics)
- Geographic Terms: Lithuania
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01877898
- Record Type: Publication
- ISBN: 9783030223748
- Files: TRIS
- Created Date: Mar 28 2023 11:02AM