Abstract
This study employs artificial intelligence technique, namely, Classification and Regression Trees (CART), with the concept of white spots to determine the factors, that contribute to the frequency and severity of road crashes. It was found that speed limit of more than 110km/hr increases the chance of a segment to be a blackspot, irrespective of any other parameter. Increasing number of lanes and lane width reduces the probability for a segment to be a blackspot. The results of this study provide valuable guidelines to reduce accidents for traffic management authorities. The application of CART proved to be helpful in achieving the objectives of this study.
| Original language | English |
|---|---|
| Title of host publication | IET Conference Proceedings |
| Publisher | Institution of Engineering and Technology |
| Pages | 49-54 |
| Number of pages | 6 |
| Volume | 2021 |
| Edition | 11 |
| ISBN (Electronic) | 9781839536588 |
| DOIs | |
| State | Published - 2021 |
| Event | 4th Smart Cities Symposium, SCS 2021 - Virtual, Online, Bahrain Duration: 21 Nov 2021 → 23 Nov 2021 |
Conference
| Conference | 4th Smart Cities Symposium, SCS 2021 |
|---|---|
| Country/Territory | Bahrain |
| City | Virtual, Online |
| Period | 21/11/21 → 23/11/21 |
Bibliographical note
Publisher Copyright:© 2021 IET Conference Proceedings. All rights reserved.
Keywords
- CART
- artificial intelligence
- blackspot segment
- geometric features
- traffic crashes
- white spot segments
ASJC Scopus subject areas
- General Engineering
