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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 spatiotemporal 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 rerouting formulation is developed and solved to encourage passenger walking and traffic flow rerouting 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 miles/hour (mph) on weekdays and weekends, respectively. Rerouting trips with PUDOs on curb space could respectively reduce the system-wide total travel time (TTT) by 2.44% and 2.12% in Midtown and Central Park on weekdays. A sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework.

Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant 52102385], grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grants PolyU/25209221 and PolyU/15206322], a grant from the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) at the Hong Kong Polytechnic University [Grant P0043552], and a grant from Hong Kong Polytechnic University [Grant P0033933]. S. Qian was supported by a National Science Foundation Grant [Grant CMMI-1931827].

Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0195 .

 

Connected technologies have engendered a paradigm shift in mobility systems by enabling digital platforms to coordinate large sets of vehicles in real time. Recent research has investigated how a small number of connected vehicles may be coordinated to reduce total system cost. However, platforms may coordinate vehicles to optimize a fleet-wide objective which is neither user nor system optimal. We study the behavior of optimized fleets in mixed traffic and find that, at small penetrations, fleets may worsen system cost relative to user equilibrium, and provide a concrete example of this paradox. Past a critical penetration level, however, optimized fleets reduce system cost in the network, up to achieving system optimal traffic flow, without need for an external subsidy. We introduce two novel notions of fleet-optimal mixed equilibria: critical fleet size for user equilibrium (CFS-UE) and critical fleet size for system optimum (CFS-SO). We demonstrate on the Sioux Falls and Pittsburgh networks that 33% and 83% of vehicles, respectively, must participate in the fleet to achieve system optimum. In Pittsburgh, we find that, although fleets permeate the network, they accumulate on highways and major arterials; the majority of origin-destination pairs are either occupied exclusively by users or by the fleet. Critical fleet size offers regulators greater insight into where fleet and system interests align, transportation planners a novel metric to evaluate road improvements, and fleet coordinators a better understanding of their efforts to optimize their fleet.

History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics.

Funding: This work was supported by the U.S. Department of Transportation [Mobility21] and the National Science Foundation [CMMI-1931827].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1189 .

 

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.