Multi-source data-based optimal method and on-site verification for selecting dangerous goods transport enterprises

ObjectiveThe traffic safety assessment on dangerous goods transportation enterprises is easily affected by insufficient information sharing among regulatory authorities, monotonous assessment indicators, and subjective factors. This study proposes a scientific and effective optimal method for selecting dangerous goods transport enterprises to prevent and reduce serious traffic accidents involving dangerous goods transport.MethodFirst, the traffic safety assessment index system of dangerous goods transportation enterprises was constructed considering the operating mileage of vehicles. Subsequently, sliding window mechanism and data mining association rules were used to clean and integrate multi-source data, as well as extract effective assessment indicators. Furthermore, the weights of assessment indicators were determined through expert scoring and Delphi method. The improved-TOPSIS model was adopted to construct an optimal model for selecting hazardous goods transport enterprises regarding traffic safety. Finally, the combination of online and offline methods was adopted, and the on-site verification was carried out through symposium, questionnaires, and on-site inspections.ResultThe on-site verification results indicate that the proposed improved Delphi-TOPSIS model can optimize the selection of dangerous goods transportation enterprises regarding traffic safety.ConclusionThe proposed method could be used to integrate multi-source data and reduce the influence of subjective factors, providing scientific and technological support for optimizing traffic safety in hazardous goods transportation enterprises. The pilot projects would be expanded to perfect the model in the future.