Next Article in Journal
Reduction of Bias and Light Instability of Mixed Oxide Thin-Film Transistors
Next Article in Special Issue
Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural Network Classification
Previous Article in Journal
Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
Previous Article in Special Issue
A New Damage Assessment Method by Means of Neural Network and Multi-Sensor Satellite Data
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(9), 886; doi:10.3390/app7090886

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

1
Transportation Research Institute (IMOB), Hasselt University, Diepenbeek 3590, Belgium
2
Taxila Institute of Transportation Engineering, Department of Civil Engineering, University of Engineering & Technology, Taxila 47050, Pakistan
3
School of Transportation, Southeast University, Nanjing 210096, China
4
Faculty of Engineering Technology, Hasselt University, Diepenbeek 3590, Belgium
*
Author to whom correspondence should be addressed.
Received: 31 July 2017 / Revised: 20 August 2017 / Accepted: 22 August 2017 / Published: 29 August 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
View Full-Text   |   Download PDF [23512 KB, uploaded 30 August 2017]   |  

Abstract

Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis. View Full-Text
Keywords: road safety; risk evaluation; data envelopment analysis; artificial neural networks; crash data analysis road safety; risk evaluation; data envelopment analysis; artificial neural networks; crash data analysis
Figures

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Shah, S.A.R.; Brijs, T.; Ahmad, N.; Pirdavani, A.; Shen, Y.; Basheer, M.A. Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach. Appl. Sci. 2017, 7, 886.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top