Document Type

Thesis

Date of Award

11-2025

School/College

College of Science, Engineering, and Technology (COSET)

Degree Name

MS in Transportation Planning & Management

Committee Chairperson

Mehdi Azimi

Committee Member 1

Yi Qi

Committee Member 2

Fengxiang George Qiao

Committee Member 3

Yachi Wanyan

Committee Member 4

Krishna Murthy Gurumurthy

Keywords

Agent-Based Modeling, Activity-Based Modeling, POLARIS, Vehicle Emissions, Energy Consumption, Shared Mobility, Autonomie, SVTrip, Electric Vehicles (EVs), Rideshare Systems.

Abstract

The transportation sector remains a major source of criteria pollutants globally, with passenger vehicles contributing significantly to energy demand. While electric vehicles (EVs) offer a promising path toward energy efficiency, few studies have evaluated their impact at the metropolitan scale using integrated, high-fidelity modeling frameworks. This study investigates the energy implications of rideshare electrification in the Houston metropolitan area using a multi-stage simulation workflow. Travel demand is modeled using POLARIS, an agent-based, activity-based framework; vehicle trajectories are translated into second-by-second speed profiles using SVTrip; and energy consumption and criteria pollutant emissions are estimated using the Autonomie vehicle simulation platform. The study evaluates six distinct powertrain configurations, including internal combustion engines (ICE), hybrids, plug-in hybrids (PHEVs), battery electric vehicles (BEVs), and fuel cell vehicles (FCEVs), across multiple scenarios: baseline, TNC-only electrification, and full fleet electrification. Results reveal that BEVs exhibit the highest energy efficiency and lowest emissions per kilometer, with fleet-wide electrification reducing overall pollutants by up to 49.5%. The findings highlight the effectiveness of electrifying high-utilization fleets such as rideshare vehicles and offer evidence-based insights to guide strategies in auto-oriented urban regions. This study contributes a replicable framework for modeling the energy and environmental impacts of emerging mobility systems under realistic demand conditions.

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