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Title: Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach
Authors: Raheel Shah, Syyed Adnan
Ahmad, Naveed
Shen, Yongjun
Basheer, Muhammad Aamir
Issue Date: 2017
Source: Applied Sciences-Basel, 7(9), p. 1-19 (Art N° 886)
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.
Notes: Shah, SAR (reprint author), Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium.;;;;;
Keywords: road safety; risk evaluation; data envelopment analysis; artificial neural networks; crash data analysis
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e-ISSN: 2076-3417
DOI: 10.3390/app7090886
ISI #: 000414453600022
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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