An Evolutionary Game Model of Traffic Conflict Between Electric Bicycles and Pedestrians in Bus Stop Areas
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摘要:
为探究公交站台区域进出站台行人与电动自行车发生冲突时,双方的通行策略选择机制,建立行人与电动自行车通行时间延误模型,分析2类出行者选择是否通行或穿越策略的时间收益,利用复制动态方程刻画2类出行者通行策略的博弈学习行为,建立该区域2类出行者之间的冲突演化博弈模型,结合稳定性分析可以得到该区域2类出行者发生冲突时的稳定通行选择策略。利用桂林市6个公交站台区域的实测数据对演化博弈模型进行实证分析,结果表明:选择“通行”策略的行人群体比例越大,其过街安全感越高;发生冲突时,电动自行车做出决策的时间较行人更短,且2类出行者分别做出“通行”或“穿越”的决策时间短于做出“等待”或“减速”的决策时间,电动自行车平均用时减少约23%,行人平均用时减少约24%。
Abstract:The work studies the selection mechanism of travel strategies of pedestrians and electric bicycles when the two parties conflict in bus station areas. A time delay model of pedestrians and electric bicycles is established to analyze the time profit of two types of travelers choosing whether "passing" or "crossing" strategies. The replication dynamic equation is used to describe the game learning behavior of the two types of travellers' general strategy, and to establish the conflict evolution game model between the two types of travellers in this area. The stability analysis is used to obtain a stable behavior selection strategy of conflicts between two types of travelers in the region. The evolutionary game model is empirically analyzed by the measured data of six bus stop areas in Guilin. The results show that the proportion of pedestrians choosing to "passing" increases when the sense of security increases. When conflicts occur, electric bicycle riders make decisions faster than pedestrians. The decision-making time for both parties to choose "passing" or "crossing" is shorter than "waiting" or"deceleration". The average decision-making time of electric bicycles reduces by about 23%, and that of pedestrians reduces by about 24%.
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Key words:
- traffic safety /
- electric bicycle /
- pedestrians /
- traffic behaviors /
- evolutionary game /
- traffic conflicts
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表 1 过街行人和电动自行车收益矩阵
Table 1. Income matrix for pedestrians and electric bicycles
行人 电动自行车 穿越 减速 通行 (-tr,-tr - D) (D, 0) 等待 (0, T) (0, 0) 表 2 均衡点稳定性分析
Table 2. Stability analysis of equilibrium points
均衡点(p, q) det (J) tr (J) 结果 (0, 0) TD T+D 不稳疋 (0, 1) (tr + D) D -(tr + 2D) ESS (1, 0) trT -T - tr ESS (1, 1) (tr + D) tr D + 2tr 不稳疋 (p*, q*) 0 0 鞍点 表 3 电动车流速度-密度拟合函数
Table 3. Velocity density fitting function of the electric bicycle flow
地点 速度-密度关系 拟合优度 甲天下广场站 v=-11.82k+6.514 0.951 东环市场站 v = -11.63k + 6.668 0.946 七星路五里店站 v =-14.07k+8.218 0.857 穿山中路站 v =-13.25k+7.632 0.915 南门桥南站 v =- 13.35k + 6.881 0.897 乐群路站 v =-11.25k + 6.605 0.891 表 4 电动自行车和行人过街参数
Table 4. Traffic parameters of electric bicycles and pedestrians
地点 电动车到达量/ (veh/h) 行人平均过街时间/s 判断时间tr/s 甲天下广场站 2 232 4.17 2 东环市场站 1 780 4.25 2 七星路五里店站 1 116 4.38 2 穿山中路站 1 925 4.23 2 南门桥南站 2 584 4.04 2 乐群路口站 2 134 4.07 2 表 5 电动自行车和行人的演化结果
Table 5. Evolution of electric bikes and pedestrians
地点 电动午初始规1模 行人演化到“通行”的初始规模临界值 电动车演化到稳定时间/s 行人演化到稳定时间/s 穿越 减速 通行 等待 甲天下广场站 0.8 0.58 1.5 2.5 2.5 3.1 东环市场站 0.43 1.7 2.3 2.1 3.0 七星路五路店站 0.38 2.3 2.6 2.6 3.2 穿山中路站 0.54 1.7 2.5 2.5 3.0 南门桥南站 0.69 2.1 2.4 2.5 3.5 乐群路口站 0.55 2.1 2.3 2.4 3.3 -
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