Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curb utilization and disturb the mainline traffic flow, evidently leading to significant societal externalities. However, there is a lack of an analytical framework that rigorously quantifies and mitigates the congestion effect of PUDOs in the system view, particularly with little data support and involvement of confounding effects. In view of this, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general networks. A causal graph is set to represent the spatio-temporal relationship between PUDOs and traffic speed, and a double and separated machine learning (DSML) method is proposed to quantify how PUDOs affect traffic congestion. Additionally, a re-routing formulation is developed and solved to encourage passenger walking and traffic flow re-routing to achieve system optimal. Numerical experiments are conducted using real-world data in the Manhattan area. On average, 100 additional units of PUDOs in a region could reduce the traffic speed by 3.70 and 4.54 mph on weekdays and weekends, respectively. Re-routing trips with PUDOs on curbs could respectively reduce the system-wide total travel time by 2.44\% and 2.12\% in Midtown and Central Park on weekdays. Sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework.

翻译：特别是近年来,乘车旅行和商业运输的迅速增加导致大量搭便车/卸货(PUDOs),占用了几十年前设计和建造的有限路段空间。这些PUDOs可能阻碍限制使用和干扰主要线路交通流量,明显导致重大的社会外差现象。然而,缺乏一个分析框架,严格量化和减轻PUDOs在系统视图中的拥堵效应,特别是几乎没有数据支持和混乱效应的参与。鉴于此,本文制定了一种严格的因果推断方法,以估计PUDOs在一般网络中的拥堵效应。一个因果图将显示PUDOs和交通速度之间的间间间间间间间间关系,并提议一种双轨和分离的机器学习(DSML)方法,以量化PUDOs对交通拥挤的影响。此外,正在开发并解决一条改道框架,以鼓励乘客行走和交通流量的回转,以达到系统总的最佳速度。在Omeralalalal-Orations 每周内分别进行一次通过实际数据实验,在MUDal-Seral-Servil 和Serviews Accreal-real_Serviews。