电动自行车骑行人交通违法行为演化博弈分析

Evolutionary Game Analysis for Traffic Violations of Electrical Bike Riders

为研究交通执法策略对电动自行车骑行人交通违法行为的影响,构建了电动自行车骑行人与交通执法者策略选择的演化博弈模型。通过模型求解、稳定性分析和仿真分析,分析处罚额度、感知收益差、执法成本和社会负面影响对博弈双方策略选择概率的影响。结果表明:当最大处罚额度小于感知收益差时,不利于对骑行人交通违法行为的控制;当最大处罚额度大于感知收益差,且执法成本较小时,采用静态处罚策略,博弈双方的策略选择没有均衡解。采用动态处罚策略,博弈双方的策略选择有均衡解。在上述条件下,通过提高最大处罚额度和社会负面影响,降低感知收益差和执法成本等措施,可有效减少电动自行车骑行人的交通违法行为。

To study the influence of traffic enforcement strategies on traffic violations of electric bike(e-bike) riders, this paper proposed an evolutionary game model of strategy choices of e-bike riders and traffic enforcers. Through the model solution, stability analysis, and numerical analysis, this study analyzed the impact of penalty, perceived relative benefit, enforcement cost, and negative social influence on the probabilities of strategic choices of both players. The results show that when the maximum penalty is less than the perceived relative benefit, it is not conducive to reduce the e-bike riders’ traffic violations;when the maximum penalty is greater than the perceived relative benefit and enforcement cost is less, the strategic choices of both players cannot reach an equilibrium solution if a fixed penalty strategy is adopted. If a dynamic penalty strategy is adopted, the strategic choices of both players can reach an equilibrium solution. Under such conditions, by implementing measures such as increasing maximum penalty and negative social influence, decreasing perceived relative benefit and enforcement cost, e-bike riders’ traffic violations can be effectively reduced.