Large-Scale Evaluation of Mobility, Technology and Demand Scenarios in the Chicago Region Using POLARIS

TL;DR

This study uses the POLARIS activity-based model to evaluate the combined impacts of technological and policy interventions on mobility, energy, emissions, and equity in the Chicago region, revealing synergistic effects and optimal strategies.

Contribution

It introduces a comprehensive end-to-end modeling workflow to assess multiple technology and policy scenarios simultaneously for the Chicago region.

Findings

Travel times reduced by up to 7% with combined strategies.

System efficiency increased by up to 53%.

Different intervention combinations show significant impacts on key metrics.

Abstract

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times. In this study, the use of an end-to-end modeling workflow centered around an activity-based agent-based travel demand forecasting tool called POLARIS is explored to provide insights on the effects of several different technology deployments and operational policies in combination for the Chicago region. The objective of the research was to explore the direct impacts and observe any interactions between the various policy and technology scenarios to help better characterize and evaluate their potential future benefits. We analyze system outcome metrics on mobility, energy and emissions, equity and environmental justice and overall efficiency for a scenario design of experiments that looks at combinations of supply interventions (congestion pricing, transit expansion, tnc policy, off-hours freight policy, connected signal optimization) for different potential demand scenarios defined by e-commerce and on-demand delivery engagement, and market penetration of electric vehicles. We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized. The results demonstrate the importance of considering various interventions jointly.