A Data-Driven Spatial Decision Framework for Assessing the Relationship Between Logistics Activities and Heavy Vehicle Accident Risks

Abstract

The increase in consumption habits of societies causes individual and collective mobility. This situation gradually increases the importance of logistics centers (LCs), which are the key parts of the developed supply chain. However, heavy vehicles, which are frequently preferred for customer satisfaction and supply, have recently been involved in many traffic accidents. A balance must be achieved in order to carry out logistics activities safely. In this study, the interaction between the site selection of LCs and the severity of heavy vehicle accidents was investigated. To do so, a four-stage hybrid solution approach was proposed. (i) First, the criteria affecting the site selection and logistics activities of LCs were determined. Then, the risk factors causing heavy vehicle traffic accidents were determined. (ii) The weights of these criteria and risk factors were calculated with the fuzzy SIWEC method. (iii) The availability maps were obtained in the spatial decision-making process via GIS. (iv) Finally, the relationship between logistics activities and heavy vehicle accidents was defined by combining spatial outputs and weight values. The concrete relationship between logistics activities and heavy vehicle accident severity was carried out by Pearson coefficient analysis. The proposed approach was applied to T & uuml;rkiye as a case study. The correlation coefficient was determined as 0.611229 and the relationship between them was found to be moderate and strong correlation. The first step of safe supply can be achieved by reducing the occurrence of heavy vehicle accidents by opening new LCs in some critical areas.