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http://hdl.handle.net/1942/24307
Title: | Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach | Authors: | Raheel Shah, Syyed Adnan BRIJS, Tom Ahmad, Naveed PIRDAVANI, Ali SHEN, Yongjun BASHEER, Muhammad |
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. syyed.adnanraheelshah@uhasselt.be; tom.brijs@uhasselt.be; n.ahmad@uettaxila.edu.pk; ali.pirdavani@uhasselt.be; yongjunshen@outlook.com; muhammadaamir.basheer@student.uhasselt.be | Keywords: | road safety; risk evaluation; data envelopment analysis; artificial neural networks; crash data analysis | Document URI: | http://hdl.handle.net/1942/24307 | Link to publication/dataset: | http://www.mdpi.com/2076-3417/7/9/886 | 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 (http://creativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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applsci-07-00886.pdf | Published version | 2.5 MB | Adobe PDF | View/Open |
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