Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
路边上下客拥堵效应的估计与缓解：因果推断方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确提出因果效应估计，并研究道路网络中的时空联系和系统拥堵。
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路边上下客拥堵效应的估计与缓解：因果推断方法
- 研究问题
- 在数据有限且存在混杂的城市道路网络中，如何估计路边上下客对交通速度的影响，并通过步行和改道缓解系统拥堵？
- 方法与思路
- 作者构建上下客与车速的时空因果图，提出双重且分离的机器学习方法估计效应，再求解引导步行和交通改道的优化问题。
- 材料陈述的贡献
- 摘要报告曼哈顿数据中上下客增加与车速下降的效应估计，并称部分区域的改道方案可降低系统总旅行时间。
- 阅读关注点
- 应核对因果图的识别依据、时空混杂和网络相互影响的处理，以及估计效应如何用于改道反事实与敏感性分析。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要虽给出数值，但未说明识别假设、区间估计和外推范围。
原文摘要
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 curbside utilization and disturb the mainline traffic flow, evidently leading to significant negative 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. To bridge this research gap, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general regional 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 optimization. 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 curb space 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.