Corridor-Level Congestion Analysis and Critical Path Network Optimization for Urban Mobility Improvement: The Case of Nagpur

Rapid urbanization and rising private vehicle ownership have intensified traffic congestion in Tier-2 Indian cities such as Nagpur. The Comprehensive Mobility Plan (CMP-2025) identifies major Origin–Destination (OD) corridors with high Passenger Car Unit (PCU) volumes experiencing recurrent congestion, indicating the need for systematic assessment and planning interventions. This study evaluates traffic congestion levels, traffic flow behaviour, and the key causes of congestion across selected priority corridors in the Nagpur Metropolitan Region, with a focus on route reallocation as a decongestion strategy.

The methodology integrates primary data from classified traffic volume counts, speed–delay studies, and route-choice surveys with secondary data from CMP reports, RTO records, and GIS-based road network layers. Congestion severity is assessed using Volume–Capacity (V/C) ratio, Level of Service (LOS), and Travel Time Index (TTI). Spatial analysis using GIS is employed to identify bottleneck locations, while traffic flow behaviour is examined through fundamental traffic flow relationships and peak-hour variability.

Results show that one of the critical corridors generates approximately 76196 peak-hour passenger trips, with the corridor Humpyard Road carrying the highest demand. Congestion is primarily attributed to the absence of service lanes, constrained right-of-way, dominance of two-wheelers in work trips, and poorly integrated land-use patterns. Peak-hour travel speeds across most corridors remain below 30 km/h, with signal delays contributing significantly to travel time losses, indicating scope for signal optimization and route reallocation.

The study reveals a substantial mismatch between road capacity and traffic demand, exacerbated by roadside parking, geometric deficiencies, encroachments, and inefficient traffic management. Route reallocation, combined with targeted traffic management and signal improvement measures, is proposed as an effective short-term strategy for reducing congestion. The research offers a structured framework for congestion assessment and provides evidence-based planning recommendations applicable to similar urban contexts.