中文摘要
本文研究自动驾驶汽车(AV)可能如何重塑早高峰通勤出行模式并影响中央商务区(CBD)的停车。我们构建了一个连续时间博弈论交通模型，该模型考虑了驾车出行的关键经济抑制因素，如停车费和交通拥堵，并刻画了通勤者在均衡状态下的出发时间与停车位置（CBD停车区内或区外）模式。我们的分析表明，在均衡状态下，所有AV通勤者可能都选择在CBD外停车，与全部为人工驾驶车辆的情形相比，这同时增加了车辆行驶时间和车辆行驶里程。这一变化提高了系统总成本，并表明在AV大规模普及后CBD土地利用存在变化的可能（例如，将CBD停车位改作商业和住宅用途）。为降低系统总成本，城市规划者可以通过调整停车费和/或征收拥堵费作为短期措施来调节通勤者的决策，或调整基础设施，例如将CBD停车空间改造为AV下客点。我们的结果表明，这些措施能够大幅降低系统总成本（例如，在将我们的模型对匹兹堡数据进行校准时，降幅可达28.5%）。
Abstract
This paper investigates how autonomous vehicles (AVs) may reshape morning commute travel patterns and impact parking in a central business district (CBD). We develop a continuous-time game-theoretic traffic model that takes into account key economic deterrents to driving, such as parking fees and traffic congestion, and characterize the departure time and parking location (inside or outside the CBD parking area) patterns of commuters in equilibrium. Our analysis shows that all AV commuters may choose to park outside the CBD in equilibrium, increasing both vehicle hours and vehicle miles traveled as compared with the case with all human-driven vehicles. This change increases the total system cost and suggests a potential for CBD land use changes (e.g., repurposing CBD parking spots as commercial and residential areas) after the mass adoption of AVs. To reduce the total system cost, an urban planner may regulate commuters’ decisions by adjusting parking fees and/or imposing congestion tolls as a short-term measure, or adjusting infrastructure, for example, converting CBD parking spaces to drop-off spots for AVs. Our results indicate that these measures can reduce the total system cost substantially (e.g., up to 28.5% when calibrating our model to data from Pittsburgh).