激励性和惩罚性交通拥堵治理政策的比较——基于动态演化博弈模型及仿真分析

随着城市化的快速发展，城市人口越来越多，私家车保有量急剧增长，这使得城市道路拥堵问题变得越来越严重，拥堵问题成为了城市健康可持续发展的重要阻碍。为了缓解交通拥堵，城市管理者基于不同的治理思路提出并实施了多项治理政策。本文从博弈论的角度阐述了交通拥堵的形成，并建立了一个出行者交通方式选择行为的演化博弈模型，揭示了不同治理措施对城市交通拥堵问题施加影响的过程，并对这些影响的结果做出了解释。该演化模型将出行者整体分为两类，一类表现为公交偏好，另一类为私家车偏好，我们分析在不同的交通拥堵治理政策下，出行者选择不同出行模式的成本和收益，得到随政策变化的收益矩阵，并分析两类出行者在不同政策下的演化稳定策略。为了更为直观地对演化过程及结果进行分析，我们对该模型进行了仿真模拟，最终发现惩罚和激励措施都能缓解交通拥堵，但在迅速性和持久性上，惩罚性措施比激励性措施更为有效。

Comparison of Incentive and Punitive Traffic Congestion Policies-Based on the Dynamic Evolutionary Game Model and Simulation Analysis

With the rapid development of urbanization, more and more people accommodate in urban areas and the number of private cars increases sharply, which make urban road congestion more and more serious. Traffic congestion concerns the public, researchers and authorities, for which a variety of measures from punishing to incentive perspectives have been conceived and carried out in practice. However the results of governance are quite unsatisfactory. An evolutionary congestion game model is built in this paper, lifting the veil on the process of different governance measures functioning on urban congestion problem and trying to give an explanation for observed results. The whole community is divided into two groups, one showing preference towards taking public transport and the other preferring private cars. The quantified government congestion measures are numerically examined which trigger trade-off matrix changes, and dynamic trends of two groups' ESS strategies in different situations. It is found that both the incentive and punitive traffic congestion policies can alleviate the congestion problem, but the punitive measures are more effective than the incentive measures in the rapidity and persistence.