Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method
Bibliographic Data
| ID | 15504566 |
|---|---|
| Authors | Tianpei Tang (0000-0003-2222-4745, University of Shanghai for Science and Technology, corresponding author), Senlai Zhu (0000-0002-6138-5130, Nantong University, corresponding author), Yuntao Guo (0000-0001-9649-5121, Purdue University West Lafayette), Xizhao Zhou (0000-0002-1335-3804, University of Shanghai for Science and Technology), Yang Cao (0000-0003-1795-6474, Nantong University) |
| Year | 2019 |
| Volume | 16 |
| Issue | 7 |
| Pages | 1166-1166 |
| Publication date | 2019-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16071166 |
| PMID | 30939766 |
| OpenAlex | W2927039151 |
| Language | EN |
| Citations received | 4 |
| References cited | 26 |
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
Bayesian network · Business · Crash · Environmental health · Geography · Risk analysis (engineering · Rural area · Statistics · Transport engineering · Computer Science · Engineering · Infrastructure Maintenance and Monitoring · Injury Epidemiology and Prevention · Mathematics · Medicine · Traffic and Road Safety
| Unique citing works | 4 |
|---|---|
| Citations per year | 0.57 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |