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  1. Free, publicly-accessible full text available January 1, 2025
  2. Free, publicly-accessible full text available January 1, 2025
  3. 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 .

     
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    Free, publicly-accessible full text available December 27, 2024
  4. Free, publicly-accessible full text available October 1, 2024
  5. 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 .

     
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    Free, publicly-accessible full text available July 1, 2024
  6. The rate at which sea level is rising in recent years due to global warming has become a growing concern, most especially as it affects coastal areas of the world. The devastating impact of sea level rise (SLR) on coastal communities, ranging from coastal beach erosion, nuisance high tide flooding, and saltwater pollution of low-lying farmlands to loss of tidal wetlands is leading to a decline in social and economic activities especially in coastal areas. According to the National Oceanic and Atmospheric Administration (NOAA), 40% of the US population living on the coast is inevitably vulnerable to SLR. Therefore, the objective of this study is to project relative sea level rise (RSLR) for Anne Arundel County and to estimate the contribution of land subsidence to RSLR at this location. To project RSLR for Anne Arundel County, this study combines global mean sea level rise (GMSLR) scenarios with local land subsidence measured at GPS LOYF station in Annapolis, Anne Arundel County, Maryland. Current quadratic trend of RSLR in Anne Arundel County projects that by 2100, RSLR for the county will be approximately 1.2 m forecasting from 1992, which is 86% and 174% of the GMSLR intermediate-high and intermediate-low scenarios, respectively. Land subsidence significantly contributed to RSLR in the 20th century; however, since 2001 absolute sea level rise (ASLR) driven by climate change has significantly contributed to RSLR in this location. The results in this paper suggest considering the intermediate-high RSLR scenario for planning and decision-making in Anne Arundel County, Maryland, in relation to SLR. 
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