Evolutionary Game Theoretic Approach to Rear-End Events on Congested Freeway

Abstract

Rear-end crashes on freeways contribute significantly to nonrecurring congestion. Reducing these events would significantly improve freeway capacity, particularly during peak hours. Although promising countermeasures, such as variable speed limits, changeable message signs, and vehicle-based improvements, are under consideration, currently there is a shortage of demonstrably proven countermeasures targeted at freeway rear-end crashes. Liability rules, in which the direct cost associated with a crash is divided between the drivers, their insurance companies, or both, are a primary mechanism for influencing the occurrence of freeway rear-end crashes. An exploratory effort uses concepts from evolutionary game theory to predict the effects of liability rules on rear-end crashes. In a typical two-vehicle car-following scenario, driving behavior can be associated with a utility that each driver expects to achieve depending on his or her and the opponent's actions. Such interactions between leader and follower are modeled as the outcome of an evolutionary process in which drivers with different driving behaviors are randomly and repeatedly matched against each other to play a two-player game. The outcome of these games determines the fraction of drivers pursuing a particular driving strategy for the next phase of the game. The stable long-run distribution of driving strategies is then used to predict the proportion of drivers who are more likely to be involved in a rear-end accident. It turns out that when direct crash costs are allocated evenly to the involved drivers, a population in which all drivers act to avoid crashes is not evolutionarily stable.