The use of microscopic traffic simulation model for the analysis of vehicle emission

Abstract

Road transport is a significant contributor to greenhouse gas emissions and urban air pollution in New Zealand, particularly in the rapidly growing Bay of Plenty region. The high vehicle density during peak hours and the reliance on petrol and diesel fuels in most fleets contribute significantly to vehicle emissions, which contribute to climate change and air quality degradation. This research investigates the use of a microscopic traffic simulation model to analyse vehicle emissions in order to identify practical measures to reduce transport-related emissions. SUMO (Simulation of Urban Mobility), an open-source software was used to construct the road network, generate realistic vehicle demand, and execute the simulation, while the HBEFA (Handbook Emission Factors for Road Transport) emission model was used to estimate vehicle emissions. This simulation utilised vehicle count data provided by the Tauranga City Council, and MongoDB was employed as the main database to facilitate effective storage, retrieval, and real-time querying of the simulation output. Three targeted emission reduction scenarios were developed and tested based on the simulation results: increasing electric vehicle ownership by 20%, introducing trackless trams on high-demand routes, and encouraging carpooling to reduce single-occupancy vehicle usage. As a result of the simulation, each measure resulted in significant reductions in vehicle emissions, and the effectiveness of each scenario was examined and compared. The findings of this study provide practical, data-driven insights for local councils, transport planners, and policymakers in implementing effective strategies to reduce vehicle emissions and contribute to New Zealand’s emission reduction targets.