Characteristics Extraction and Increasing Block Fine Modeling for Repeated Speeding Behaviors

To efficiently deter the repeated speeders who are frequently fined but continue to commit the violation, this study attempts to investigate the characteristics of the repeated speeding behaviors and propose an increasing block fine modeling approach. Based on the off-site law enforcement data collected from the Deyang City, three speeding ranges (low (≤20%), medium (20%–50%), and high (≥50%)) were considered, and then the characteristics of repeated speeding behaviors were extracted. After that, the cost-benefit theory was introduced to develop an increasing block fine model by taking into account the speeding range and frequency. Considering the minimum total number of speeding as the goal and the economic cost as the constraint, an optimization model of increasing block fine was developed. The pattern search algorithm was used to solve the developed optimization model to determine the best number of blocks and corresponding fine within each speeding range. Finally, a case study was conducted to validate the performance of the developed model. The results show that for the repeated speeders, the percent of the low speeding behavior is the highest, and the speeding behaviors largely occur during the daytime. Furthermore, within the increasing block fine mechanism, the reduction rates of medium and high speeding behaviors are clearly higher than that of the low speeding behavior. The findings of this study offer a fresh viewpoint on the repeated speeding intervention and enable the speeding enforcement more equitable.