Who gets the curb? A systematic review of responsive curb management research

Abstract

Urban curb space is a capacity-constrained resource that serves different user groups, including freight deliveries, ride-hailing, transit access, paratransit, and short-term parking, yet in most cities it is still governed by static rules and fixed signage. Emerging data sources, including sensors, cameras, mobile applications, and integrated payment systems, make responsive curb management (RCM) increasingly feasible by supporting the definition, implementation, and enforcement of curb regulations that adapt to real-time conditions. This paper provides a systematic review of RCM research and classifies the literature into five groups: qualitative studies, empirical behavioural and causal inference studies, predictive analytics and machine learning, optimisation and control, and network-level and multimodal system models that capture interactions across curb operations, traffic flows, and transportation modes. For each group, we explain the motivation, methodological approaches, core findings, and how these research streams relate to one another within the broader RCM landscape. We conclude by outlining cross-cutting limitations and research needs in demand and duration forecasting, optimisation under stochastic arrivals and service durations, network integration, and equity-oriented governance for responsive curb systems.