Travel demand management through congestion pricing – case of Bengaluru central business district (CBD)

Abstract

Bengaluru, recognized as India’s information technology epicentre, encounters significant traffic obstruction, particularly within its Central Business District (CBD). With a vehicular population surpassing 1.3 crore and an annual growth rate of approximately 7% (RTO Bengaluru, 2023), the city’s roadway infrastructure grapples to adapt to the escalating demand. Unregulated vehicle proliferation, constrained roadway capacity, and ineffective traffic administration contribute to excessive delays and diminished mobility. Average peak hour velocities in the CBD have plummeted to as low as 10-12 km/h, with congestion levels ascending by nearly 25% over the preceding decade (TomTom Traffic Index, 2023; BMRCL & DULT Reports, 2023). This investigation examines congestion pricing as a tactical instrument to regulate traffic flow and enhance network efficacy within the CBD. This study critically evaluates congestion pricing strategies implemented worldwide and assesses their applicability to Bengaluru’s Central Business District (CBD). Successful models from cities such as London, Singapore, and Stockholm are analyzed for their policy frameworks, technology adoption and impact on urban mobility. By comparing these global benchmarks, the study identifies best practices that can be adapted to Bengaluru’s unique traffic conditions. Additionally, the research examines the effects of congestion pricing on key traffic parameters, including vehicle volume, travel duration, and overall network efficiency. A detailed impact assessment is conducted to determine how pricing mechanisms influence traffic flow, reduce delays, and optimize road space utilization. To achieve this, the study employs traffic simulation models to replicate real-world traffic conditions in Bengaluru’s CBD. Macroscopic model Visum used to analyse network-wide congestion trends. The simulation incorporates real-time traffic volume data, road network characteristics, and travel demand patterns to evaluate different congestion pricing scenarios. By comparing these models, the study identifies the most effective strategy for reducing peak-hour congestion and improving traffic flow. Furthermore, a demand elasticity analysis is conducted to measure how travellers respond to congestion pricing. This analysis assesses behavioural changes, such as shifts from private vehicles to public transport, route alterations, or modifications in travel time preferences. Data from travel surveys and historical congestion pricing studies are used to estimate elasticity values, helping to determine the optimal pricing level that balances congestion reduction with commuter affordability. By integrating traffic simulation results with demand elasticity analysis, this Travel demand management through congestion pricing – case of Bengaluru central business district (CBD) research provides a data-driven approach to formulating an effective congestion pricing strategy for Bengaluru. The findings offer valuable policy recommendations, including the need for robust enforcement mechanisms, technological infrastructure, and complementary transport measures such as improved public transit and last-mile connectivity. Congestion pricing model can significantly improve urban mobility, reduce congestion, and create a more efficient and sustainable traffic management system. Keyword: Congestion Pricing, Traffic Flow Management, Bengaluru CBD, Urban Mobility, Traffic Simulation