Kuwait in flow: a goal programming demand release schedule for the afternoon commuter

Abstract

Traffic congestion remains one of the most persistent challenges in urban environments, particularly in regions experiencing rapid population growth and limited availability of transportation data. In Kuwait, high automobile dependency, coupled with the absence of advanced traffic data and management systems, has intensified congestion and reduced network efficiency. Traditional Dynamic Traffic Assignment (DTA) models, while effective in capturing time-dependent traffic behavior, require detailed Origin–Destination data, making them impractical in contexts such as Kuwait. This research introduces a novel modeling framework that integrates the Cell Transmission Model (CTM) with Goal Programming (GP) to develop an optimization-based approach to traffic demand management. The GP–CTM model prescribes a demand release schedule that prevents road capacity from being exceeded, thereby mitigating congestion preemptively. The model was applied to a case study in the Sabah Al-Salem area of Kuwait, using school-related demand as the primary source of recurrent congestion. Results showed that the optimized demand release schedule achieved full demand clearance within the one-hour time horizon, while reducing average network occupancy by 64% compared to a baseline 'all-at-once' release policy. The findings demonstrate that the GP–CTM framework can deliver Dynamic Traffic Assignment–like insights without O-D data, providing aggregate-level understanding of system-wide congestion dynamics. This work offers policymakers a practical, computationally tractable tool for formulating demand management strategies and highlights a shift toward prescriptive and preemptive, system-level approaches in traffic management research.