无人驾驶停车共乘缓解瓶颈拥堵研究

Autonomous Vehicles Parking and Ridesharing to Alleviate Bottleneck Congestion

利用无人驾驶车辆利于共乘和自行停车的特点,提出了一种新的高效的通勤模式——无人驾驶停车共乘。首先,基于扩展的瓶颈模型,解析了该出行模式下瓶颈处通勤行为的均衡特征,建立了共乘率、停车收费与出行成本(包括瓶颈排队、工作延误、共乘隐私损失及共乘下车延误)之间的定量关系,给出了可诱导至系统最优的拥堵收费方案。然后,实施了数值实验,证明了模型的有效性,探究了模型中关键参数对出行费用的影响。最后,提供了该通勤模式可以应用的实际场景。解析和数值实验结果均表明,该通勤模式可缩短早高峰出行的持续时长,简化出行过程,从而大幅减少出行费用和换乘停车场规模,可为未来解决城市交通拥堵和市中心停车位缺乏等难题提供借鉴。

Owing to the self-parking and ridesharing capabilities of autonomous vehicles,a new effective commuting mode,i.e.,autonomous vehicles parking and ridesharing is proposed.Firstly,based on the extended bottleneck model,the equilibrium characteristics of commuting behavior at bottlenecks was analyzed,the quantitative relationships between average ridesharing passengers per vehicle,parking charge,and the travel cost(including queue cost,travel delay cost,drop-off delay cost and privacy loss)were established,and the congestion charge scheme to induce the traffic corridor to evolve to the system optimum was given.Then,numerical experiments were carried out to verify the validity of the proposed model and to explore the impact of key parameters involved in the model on the travel cost.Finally,practical implementation of the proposed commuting mode was provided.The results from both the analysis and numerical experiments indicate that the proposed commuting mode can significantly shorten the duration of morning rush hour,simplify commuting processes,and reduce travel costs and transfer parking spaces.This study could provide a reference for solving urban traffic issues such as congestion and lack of parking spaces in the era of autonomous vehicles.