A Data mining approach on significant variables affecting lorry drivers overloading in Tehran urban roads

The aim of this study is to identify the important factors influencing lorry drivers overloading on Tehran's highways and to investigate the effect of each variables in committing this violation. To this end, the required information was collected through field surveys at 10 stations on the Tehran urban highways. The tonnage data of freight vehicles were collected using a pair of portable scales as well as other information needed including driver information, vehicle, load and travel by completing the questionnaire. After correcting or deleting incomplete data, 856 data records were used for statistical analysis. The results of statistical models and binary logistic analyzes showed that the highest probability of overloading in the inner-city axes was obtained for construction loads. Also, the results of modeling in traffic type section showed that the highest likelihood of overload for internal loads (origin and destination inside Tehran) and the least probability of overburden (origin and destination outside of Tehran) were obtained. Finally, it was concluded that drivers are more likely to commit overloading on weekends.