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Curb space is one of the busiest areas in urban road networks. Especially inrecent years, the rapid increase of ride-hailing trips and commercialdeliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy thelimited curb space that was designed and built decades ago. These PUDOs couldjam curbside utilization and disturb the mainline traffic flow, evidentlyleading to significant negative societal externalities. However, there is alack of an analytical framework that rigorously quantifies and mitigates thecongestion effect of PUDOs in the system view, particularly with little datasupport and involvement of confounding effects. To bridge this research gap,this paper develops a rigorous causal inference approach to estimate thecongestion effect of PUDOs on general regional networks. A causal graph is setto represent the spatio-temporal relationship between PUDOs and traffic speed,and a double and separated machine learning (DSML) method is proposed toquantify how PUDOs affect traffic congestion. Additionally, a re-routingformulation is developed and solved to encourage passenger walking and trafficflow re-routing to achieve system optimization. Numerical experiments areconducted using real-world data in the Manhattan area. On average, 100additional units of PUDOs in a region could reduce the traffic speed by 3.70and 4.54 mph on weekdays and weekends, respectively. Re-routing trips withPUDOs on curb space could respectively reduce the system-wide total travel timeby 2.44% and 2.12% in Midtown and Central Park on weekdays. Sensitivityanalysis is also conducted to demonstrate the effectiveness and robustness ofthe proposed framework.

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach