Effects of drivers evasive behavior on the placement of automated enforcement equipment in highway systems

Franceschi, Lucas; Otto, Gustavo Garcia; Kaesemodel, Luciano; Valente, Amir Mattar · 2020 · DOAJ

DOI: 10.14295/transportes.v28i5.2233

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Summary

This paper investigates the optimal placement of automated enforcement equipment on highway networks, specifically addressing the challenge of drivers actively evading monitoring to continue illegal transportation practices, such as overloading. The authors argue that traditional facility location models, which assume static traffic flows, are ineffective because violators use route planning software to avoid detection. Consequently, the study focuses on the Evasive Flow-Capturing Problem (EFCP), a bilevel optimization framework where enforcement agencies (leaders) locate stations to minimize uncaptured illegal traffic, while carriers (followers) choose the shortest unmonitored routes to minimize their costs. The methodology employs a deterministic binary integer programming model proposed by Marković et al. (2015), which simplifies the bilevel problem into a single-level program by assuming that minimizing travel distance for evasive drivers aligns with minimizing the cost of illegal traffic for planners. The researchers conducted numerical experiments using real data from the highway network of Espírito Santo, Brazil, comprising 368 nodes, 902 links, and 451 origin-destination pairs. They utilized a modified Yen’s algorithm to preprocess shortest paths within a defined maximum allowed detour, representing the drivers' willingness to deviate from the shortest path to avoid enforcement. Three experiments were performed: assessing damage reduction relative to the number of stations, analyzing the impact of increasing evasion tendencies on required station counts, and evaluating the consequences of planning without accounting for evasive behavior. The results demonstrate that accounting for evasive behavior does not significantly increase the number of monitoring stations required but rather optimizes their location to cover all viable paths within the allowed detour margin. In Experiment 1, installing 20 stations achieved over 95% damage reduction, while 78 stations were needed for 100% coverage. Experiment 2 revealed that as the maximum allowed detour increased, the number of stations required to capture all flows also increased, but not prohibitively. Crucially, Experiment 3 showed that if planning ignores evasive behavior (assuming 0% detour) while drivers actually evade (up to 50% detour), the enforcement system becomes ineffective. Conversely, overestimating the evasion tendency in planning ensures full coverage without excessive cost increases. The significance of this work lies in its practical guidance for transportation planners. It confirms that ignoring evasive behavior leads to ineffective enforcement systems, whereas incorporating it into mathematical models ensures robust coverage. The study highlights that while preprocessing all viable paths can be computationally intensive, the resulting optimization provides a cost-effective strategy for locating enforcement equipment. The findings suggest that planners should err on the side of overestimating driver evasion tendencies to guarantee successful interception of illegal vehicles, thereby protecting infrastructure and ensuring safety without incurring disproportionate infrastructure costs.

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• automated enforcement cameras