Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

Tianpei Tang, Senlai Zhu, Yuntao Guo, Xizhao Zhou and Yang Cao
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Tianpei Tang: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Senlai Zhu: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Yuntao Guo: Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Xizhao Zhou: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Yang Cao: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China

IJERPH, 2019, vol. 16, issue 7, 1-17

Abstract: Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

Keywords: traffic safety; Bayesian Network; run-off-road; roadside features; rural roads (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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