Prediction model for drivers' tendency to perpetrate a double parking violation on urban trips

Abstract

Illegal parking during peak hours and on heavily traveled streets can significantly impact the severity of traffic congestion and related problems. A double parking violation is among the most significant instances of illegal parking. Double parking violations reduce road capacity and increase traffic congestion. Intensified traffic congestion also increases travel time and reduces road user satisfaction. Thus, the management of double parking violations is critical to managing traffic congestion in urban streets. In this study, the factors influencing the drivers' tendency to perpetrate a double parking violation were identified using the ordinal logistic regression model in the central area of Mashhad. Then, by redesigning the ordinal logistic regression model and utilizing the identified effective factors, a model was developed to predict the level of drivers' propensity to perpetrate a double parking violation during urban trips. Mashhad is one of the largest cities in Iran, and its central district experiences a high volume of daily traffic. The results indicated that parking duration is the main factor affecting drivers' propensity to perpetrate a double parking violation. Driver education level and driver presence in the vehicle ranked second and third, respectively. The P-value in the goodness-of-fit test was obtained at less than 0.05, indicating the designed model's acceptable accuracy.