Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods
The current risk assessment methods for dangerous goods roads have the problem of being unable to cope with complex road conditions and the influence of multiple factors. This study extends 9 tertiary indicators from three secondary indicators: personnel factors, vehicle factors, and road factors, t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111225000032 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850201973698068480 |
|---|---|
| author | Qiankun Jiang Haiyan Wang |
| author_facet | Qiankun Jiang Haiyan Wang |
| author_sort | Qiankun Jiang |
| collection | DOAJ |
| description | The current risk assessment methods for dangerous goods roads have the problem of being unable to cope with complex road conditions and the influence of multiple factors. This study extends 9 tertiary indicators from three secondary indicators: personnel factors, vehicle factors, and road factors, to evaluate the transportation risk of dangerous goods. After calculating the weights of each indicator, this study improves the parameters of the particle swarm algorithm using the aggregation and foraging behavior of artificial fish, and uses the improved algorithm to solve the optimal solution for the cost of dangerous goods road transportation. After experimental verification, the improved hybrid algorithm has optimized the path transportation time by 13.9 % compared to a single algorithm model. The total risk of simultaneously improving the algorithm was 0.8863, and the total transportation distance was 861 km, both lower than other algorithms. The comprehensive analysis shows that the established model is reasonable, and the designed improved hybrid algorithm can improve the efficiency of the transportation industry, while also contributing to the improvement of the current cost status of dangerous goods road transportation. |
| format | Article |
| id | doaj-art-2ea8c5eccd354033a8f4d6c2b41a41a9 |
| institution | OA Journals |
| issn | 0386-1112 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj-art-2ea8c5eccd354033a8f4d6c2b41a41a92025-08-20T02:11:54ZengElsevierIATSS Research0386-11122025-04-01491728010.1016/j.iatssr.2025.01.003Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goodsQiankun Jiang0Haiyan Wang1School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; Department of Management, Hubei University of Technology Engineering and Technology College, Wuhan 430068, China; Corresponding author at: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaThe current risk assessment methods for dangerous goods roads have the problem of being unable to cope with complex road conditions and the influence of multiple factors. This study extends 9 tertiary indicators from three secondary indicators: personnel factors, vehicle factors, and road factors, to evaluate the transportation risk of dangerous goods. After calculating the weights of each indicator, this study improves the parameters of the particle swarm algorithm using the aggregation and foraging behavior of artificial fish, and uses the improved algorithm to solve the optimal solution for the cost of dangerous goods road transportation. After experimental verification, the improved hybrid algorithm has optimized the path transportation time by 13.9 % compared to a single algorithm model. The total risk of simultaneously improving the algorithm was 0.8863, and the total transportation distance was 861 km, both lower than other algorithms. The comprehensive analysis shows that the established model is reasonable, and the designed improved hybrid algorithm can improve the efficiency of the transportation industry, while also contributing to the improvement of the current cost status of dangerous goods road transportation.http://www.sciencedirect.com/science/article/pii/S0386111225000032Dangerous goodsRisk assessmentTransportation routePSOArtificial fish swarm optimization |
| spellingShingle | Qiankun Jiang Haiyan Wang Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods IATSS Research Dangerous goods Risk assessment Transportation route PSO Artificial fish swarm optimization |
| title | Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| title_full | Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| title_fullStr | Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| title_full_unstemmed | Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| title_short | Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| title_sort | risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods |
| topic | Dangerous goods Risk assessment Transportation route PSO Artificial fish swarm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S0386111225000032 |
| work_keys_str_mv | AT qiankunjiang riskassessmentandhybridalgorithmtransportationpathoptimizationmodelforroadtransportofdangerousgoods AT haiyanwang riskassessmentandhybridalgorithmtransportationpathoptimizationmodelforroadtransportofdangerousgoods |