基于元胞自动机模型的路侧停车行为对交通流的影响研究

针对路侧停车带来的进出停车位排队延误、低速巡游降低通行效率、过量停车加剧交通负荷等问题，研究了路侧停车对路段动态交通流的影响分析方法。基于视频识别算法，提取路侧停车车辆在驶入车位过程中的运行轨迹和速度波动数据，解析路侧停车过程中的驶入行为特性，并按照行为差异将停车车辆停车全过程细分为进入路段、寻找车位、找到车位、驶入车位、静止停放、驶离车位、汇入路段和错失车位8类状态；分别依据停车车辆和通行车辆的实际驾驶行为，从跟驰特征、速度矫正、换道规则和位置更新等方面对路侧停车元胞自动机模型进行了改进；在选择目标车位时综合考虑了步行至目的地时间和驶入车位耗时2个要素。与常规通行车辆相比，深入分析了停车车辆提前换道和停车完后汇入路段行为对后车的影响。基于实际交通流数据对仿真模型进行参数标定，经验证，模型拟合度为77.6%；仿真分析了在差异化的停车需求强度下，巡游速度对道路通行能力和延误时间的影响规律。结果表明:固定的巡游速度和停车需求强度下，道路延误时间随道路交通量先增加后减少；在低停车需求强度下，巡游速度对道路通行能力影响微弱，在高停车需求强度下，当巡游速度从30 km/h降低至20 km/h，外侧车道饱和流量降低500 veh/h，最高延误时间增加105 s。 

Accessing the Impacts of Curb Parking Behavior on Traffic Flows Through Cellular Automata Models

Curb parking may lead to several traffic issues, such as queue delay, slow traffic due to low-speed cruising, and reduced road capacity because of excessive parking spaces. In order to mitigate these issues, the impacts of curb parking on traffic flows are studied. Data of vehicle trajectory and speed is collected based on video recognition technique. Then, the characteristics of driving behaviors of the vehicles which use curb parking are analyzed. According to differences of driving behaviors, the process of curb parking is divided into eight steps: driving into the road, cruising for a parking space, finding a parking space, entering the space, parking, leaving the space, merging into traffic, and missing a parking space. Based on extracted data of parking and cruising behaviors of curb parking vehicles, a cellular automata model is proposed by taking multiple features into consideration, including their characteristics of car following, speed correcting, lane changing, and position updating. Time costs of both parking a vehicle and walking to destination are also considered for searching a target parking space. Compared with other vehicles, the impacts of behaviors of curb parking on the following vehicles, i.e., lane changing and lane merging, are analyzed. Besides, parameters of a simulation model are calibrated based on observed data of traffic flow, and the result shows that the degree of fit is 77.6%. Moreover, the influences of cruising speed on road capacity and delay time are analyzed by a simulation under differentiated parking intensities. The results show that delay time first increases, then decreases with the rise of traffic volume at a fixed cruising speed and parking intensity. At a low parking intensity, the impact of cruising speed on road capacity is small. In a scenario of high-volume traffic, when cruising speed declines from 30 km/h to 20 km/h, the saturation flow of outer lanes decreases by 500 veh/h, and the maximum delay time increases by 105 s.