An Integrated Policy Framework for Urban Traffic Congestion Mitigation: A Comparative Simulation Insights from Amsterdam and Delhi

Abstract

Urban traffic congestion is a global concern due to rapid urbanization, increased vehicle ownership, and limited transport infrastructure. This paper proposes a comprehensive framework for evaluating and mitigating traffic congestion using both conventional approaches and emerging smart mobility technologies. Through literature synthesis and simulation-based analysis, we compare strategies such as infrastructure expansion, traffic demand management (TDM), and intelligent transportation systems (ITS) under multiple urban contexts. Using case studies from Amsterdam and Delhi, the study employs traffic simulation tools (SUMO/VISSIM) and AI models (LSTM/GRU) to evaluate scenarios against key performance indicators, including travel time, emissions, and delay indices. Findings demonstrate that hybrid strategies that incorporate AI-driven control and policy interventions yield superior, sustainable results. The paper concludes with a set of policy recommendations and future research directions aimed at urban planners and policymakers.