Modeling the curbside congestion effects of ride-hailing services for morning commute using bi-modal two-tandem bottlenecks

With the proliferation of ride-hailing services, curb space in urban areas has become highly congested due to the massive passenger pick-ups and drop-offs. Particularly during peak hours, the massive ride-hailing vehicles waiting to drop off obstruct curb spaces and even disrupt the flow of mainline traffic. However, there is a lack of an analytical model that formulates and mitigates the congestion effects of ride-hailing drop-offs in curb spaces. To address this issue, this paper proposes a novel bi-modal two-tandem bottleneck model to depict the commuting behaviors of private vehicles (PVs) and ride-hailing vehicles (RVs) during the morning peak in a linear city. In the model, the upstream bottleneck models the congestion on highways, and the downstream curbside bottlenecks depict the congestion caused by RV drop-offs in curb spaces, PV queue on main roads, and the spillover effects between them in the urban area. The proposed model can be solved in a closed form under eight different scenarios. A time-varying optimal congestion pricing scheme, combined curbside pricing and parking pricing, is proposed to achieve the social optimum. It is found that potential waste of road capacity could occur when there is a mismatch between the highway and curbside bottlenecks, and hence the optimal pricing should be determined in a coordinated manner. A real-world case from Hong Kong shows that the limited curb space and main road in the urban area could be the major congestion bottleneck. Expanding the capacity of the curb space or the main road in the urban area, rather than the highway bottleneck, can effectively reduce social costs. This paper highlights the critical role of curbside management and provides policy implications for the coordinated management of highways and curb spaces.