Demand uncertainty aware curbside space allocation planning in shared-use transportation networks

Abstract

Efficient management of curbside space is gaining more attention as cities confront increasing traffic, curbside requirements, and mobility patterns. Given their increasing significance in meeting diverse shared-use mobility requirements, the absence of optimal planning on curbside areas can lead to networkwide negative impacts. This study examines demand uncertainty for planning at several temporal resolutions. Our developed approach identifies the optimal curbside space allocation planning strategies to enhance passenger-level services, considering the Curb Productivity Index and the uncertain arrival distribution of Shared-Use Mobility (SUM) service units throughout the curbside networks. We integrated a core optimization module to adjust capacity over various time scales and find the optimal allocation plan. Further, we integrated a sample-based heuristic to allow decision-making at multiple levels of granularity (allocating space hourly versus adjustments occurring every five minutes due to interconnected infrastructure technologies or analogous factors). The proposed solution methodology is demonstrated for a network of curbsides with known demand distribution parameters (truncated Normal with mean and standard deviation for hourly demand). The results suggest that the allocation plans are highly sensitive to the decision interval (minutes vs. one-hour), and the coarse-resolution decision-making may overestimate the performance of a curbside allocation plan, underscoring the need for fine-resolution allocation plans in cities.