Developing Vehicle Ownership and Activity-Based Travel Demand Models for Transportation Network and Emission Analysis

Abstract

Vehicle ownership significantly contributes to greenhouse gas (GHG) emissions, as the type of vehicle chosen for various activities directly affects fuel consumption and emission levels. This study presents an Integrated Transport Land-use and Energy (iTLE) modelling framework including the diversification of vehicle type to assess vehicle ownership level, travel patterns, traffic network performance, and vehicular emissions in the Halifax Regional Municipality (HRM). The study begins with the development of electric vehicle (EV) adoption models, exploring the socio-demographic factors influencing EV ownership through machine learning-based clustering and econometric models. The findings highlight the role of income, education, employment status, and household characteristics in shaping EV adoption patterns. Additionally, an electric vehicle type choice model integrates attitudinal and lifestyle factors, identifying distinct user classes with varying preferences for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and internal combustion engine (ICE) vehicles. The study also examines activity start-times, durations, and vehicle allocation using advanced statistical modeling. A nested Archimedean copula framework captures the dependencies between individuals’ activity start-time and duration, highlighting variations in travel pattern across different demographics. Further, a vehicle allocation model analyzes preferences for different fuel types across mandatory, maintenance, and discretionary activity-based tours, providing insights into vehicle type choice decisions based on accessibility measurements and household characteristics. This study advances a prototype of the iTLE model to forecast future electric vehicle (EV) adoption and evaluate its effects on greenhouse gas (GHG) emissions. The simulation outcomes reveal a notable decrease in emissions across various scenarios as EV adoption grows, emphasizing its potential to enhance sustainable transportation strategies. Additionally, the research employs an in-depth traffic simulation to estimate vehicle kilometers travel (VKT) and analyze emissions at a highly detailed level. It considers different vehicle types, including electric, gasoline, and diesel-powered vehicles, alongside various activity categories such as mandatory, maintenance, and discretionary trips. This comprehensive approach allows for more precise emission evaluations and facilitates the identification of urban pollution hotspots using spatial analysis. By integrating transportation modeling, behavioral analysis, and emissions forecasting, this study provides a comprehensive tool to support policy decisions for sustainable urban mobility. The findings offer valuable insights for urban planners and policymakers aiming to achieve net-zero GHG emissions by 2050 and promote cleaner, more efficient transportation systems.