1374181 - An Optimization Framework for Curbside Allocation

The emerging mobility solutions, i.e., ridesharing, ride-hailing services, bike-share services, and e-scooters, have resulted in the increased demand for curbside parking. Moreover, the growth of e-commerce and online shopping has added to the growing demand for curbside. These mobility services often occupy the travel lanes to complete their pickup and dropoff (PUDO) activities when the curbside is occupied by long-term parking by private vehicles. This disruption can cause undesirable impacts—congestion and emissions—for the traffic network. As a response, U.S. cities are shifting towards dynamic use of curbside management strategies to meet curbside demand with minimal disruption to through-traffic.
Demand for these newer mobility options varies in time and duration(e.g., ride-hailing services use curbside parking for a shorter duration than urban freight). The dynamic use strategy allows cities to allocate curbside space among various competing activities accounting for space and time heterogeneity. An optimization framework can boost and assist the adoption of curbside management strategies. Curbspaces are often assigned to various activities based on parking demand and spatial needs (e.g., urban centers, the proximity of schools, and parks). Optimal allocation can maximize the usage of the available spaces in the face of growing demand, especially in congested urban areas. Also, placing demand-based pricing for different activities can make spaces available during peak hours, distribute the demand over time and increase revenue for cities by increasing the overall curb productivity. Our study answers the following questions: a) What will be the share of different curbside activities to maximize benefits in a busy commercial street? b) How will their proportions change depending on the time of day? C) If demand-based pricing is implemented during peak hours, how will the pricing vary across different activities to maximize the revenue for cities? To address this, The study integrates a mathematical program with microsimulation tools (for both traffic and emissions) to find optimal curbside allocation strategies and measure the operational benefits (delay reduction) and energy simulation models to quantify the environmental benefits (changes in fuel consumption