A novel model of increasing block fine for red-light running recidivism

Abstract

Red-light running is one of the major causes for traffic crashes at signalized intersection. In addition to traffic engineering and management measures, enforcement countermeasures are employed to encourage drivers to comply with traffic signal through the threat of penalty points and fine. However, the fine associated with red-light running is fixed no matter how many times one vehicle violates red signal during a given period. Such issue results in little deterrence effectiveness of fine for red-light running recidivism. Therefore, the objective of this study is to explore a novel model of increasing block fine structure based on the number of one vehicle committing red-light running in 1 year so as to prevent red-light running recidivism. First, using optimal partition method, the number of one vehicle committing red-light running in 1 year is categorized into a few groups that are regarded as the blocks for an increasing block fine structure. Second, the price elasticity is introduced and discussed to determine the changed number of red-light running and corresponding fine at each block. Third, an optimization model is proposed to determine the varying fine at each block and solved via the simulated annealing algorithm. After that, a case study is conducted to verify the validity of the developed model. The results indicate that this novel fine structure can effectively deter red-light running recidivism from running red signal. In addition, the fine structure established in this research not only offsets the defect of the present fine structure but also reduces red-light running to some extent.

Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

“…It also suggested improving driver education, increasing traffic fines, and banning drivers with poor driving history to reduce RLR. Fu et al [22] proposed a step-by-step penalty strategy to prevent the re-offending of RLR vehicles. Despite the rigorous penalty strategy to reduce RLR, its effectiveness was limited.…”