【摘要】 为提高路外停车场停车效率，同时保障停车过程的交通安全，研究了路外停车场内部车行交通组织优化方法。利用有向加权图表征停车场出入口和车行通道布局及内部车行交通组织，将车行交通组织优化问题转化为邻接矩阵优化问题；以停车过程中的安全与效率为优化目标，构建潜在冲突风险、泊车行程时间和节点均衡系数3个评价指标，考虑停车场内部泊位数量和通道通行能力的约束，建立停车场内部车行交通组织优化模型，并采用遗传算法求解。为比较优化前后交通组织效果，基于实际案例数据进行VISSIM仿真，选取出入口排队长度、单车泊车时间、冲突点分布及车位利用率进行对比研究，并进行模型参数和交通流量的敏感性分析。结果表明：(1)模型能够弥补定性研究的不足和主观经验判断的缺陷，实现路外停车场内部车行交通组织定量优化。(2)优化后出入口排队长度平均降低了25.8%，车位利用率在[0, 1.8]范围内的停车单元数下降了5.89%；冲突点核密度降低。(3)模型结果对潜在冲突风险参数在±0.1～±0.3范围内的变化不敏感，模型较为稳定；在-20%～+20%的流量变化范围内，优化方案单车泊车时间及平均排队长度变化范围均维持在10%以内，能够适应实际应用场景下的流量波动。更多还原

【Abstract】 To improve the efficiency of off-street parking lots and to ensure road traffic, this study proposes an optimization method for internal vehicle-traffic organization in off-street parking lots. A directed weighted graph is utilized to represent the layout of exit/entrance and passages of a parking lot and the traffic organization within the parking lot, thereby transforming the optimization problem of traffic organization into the optimization problem of adjacency matrix. With safety and efficiency as the optimization objectives, three evaluation indicators, i.e., potential conflict risk, parking travel time and node equilibrium coefficient are used. Thereby the optimization model for internal traffic organization of parking lots is established, considering the constraints of the number of parking spots and passage capacity. The optimization problem is solved using genetic algorithm. To compare the effects of traffic organization before and after optimization, VISSIM-simulation is adopted based on data from an empirical case.The parameters such as queue length at entrances/exits, individual parking time, distribution of conflict points, and use ratio of parking spots are selected for comparison, along with sensitivity analysis of model parameters and traffic flow. The results show that:(1) the model can compensate for the limitations of qualitative research and subjective empirical judgements, achieving quantitative optimization of the internal traffic organization in off-street parking lots.(2) The queue lengths at entrances/exits reduces by 25.8% on average; the number of parking spots with use ratio in the range of 0 to 1.8 decreases by 5.89%; and the kernel density of conflict points also reduces.(3) The model is relatively stable as it is insensitive to the variations of parameters of potential conflict risks within the range of ±0.1 to ±0.3. 4) Within a range of-20% to +20% regarding the variation of traffic volume, the optimized solution ensures the corresponding variations of individual parking time and the average queue length remain within 10%,which shows that the solution is adaptable to fluctuating traffic volumes in real-world scenarios.更多还原