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Copyright © 2014 Amir Falamarzi et al. Amir Falamarzi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.

Details

Title
Developing a Web-Based Advisory Expert System for Implementing Traffic Calming Strategies
Author
Falamarzi, Amir; Borhan, Muhamad Nazri; Riza Atiq O. K. Rahmat
Publication year
2014
Publication date
2014
Publisher
John Wiley & Sons, Inc.
ISSN
23566140
e-ISSN
1537744X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1563918775
Copyright
Copyright © 2014 Amir Falamarzi et al. Amir Falamarzi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.