In recent years many authorities have considered heavy vehicle restrictions, or complete bans, in certain parts of the city to alleviate both congestion and other externalities. But such restrictions are often implemented without regard for the true impact, both intended and unintended. One reason is because anticipating the effects, through modelling, is not easily achieved. As but one consequence, such bans are often implemented but not well-enforced. In this paper we show how an agent-based simulation is used to study the efficacy of truck bans. That is, studying if the combination of the probability of being caught, and the size of penalty if caught violating the restriction, has the intended effect of getting heavy vehicles to abide by the restriction(s). The results show that the behaviour of heavy vehicles is more sensitive to enforcement efficiency than the size of the penalty. In other words, using the proverbial stick as a motivator instead of a carrot, just using the stick has more effect than the size of the stick, no matter how much you wave it around.
The 8th International Workshop on Agent-based Mobility, Traffic and Transportation Models, April 29 - May 2, 2019, Leuven, Belgium