Cristian Poliziani | Lawrence Berkeley National Laboratory

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Presentation Description

Travel and traffic interventions, such as investments in active transportation and transit, and effective traffic management, offer opportunities to reduce the environmental impacts of the transportation systems. These interventions will redistribute the emission in time and space, altering the exposure levels to air pollution across population groups. While the overall emission changes have been investigated extensively, tracking the emission changes to the downwind populations at high resolution for equity assessment remains an active area of research. Additionally, potential tradeoffs and synergies of equity outcomes among a multitude of dimensions of mobility, accessibility, pollutant exposure, and health are not fully understood.

To address above research gaps, we present a newly developed integrated assessment framework based on a high-resolution, agent-based transportation model known as BEAM CORE (Behavior, Energy, Autonomy, Mobility [BEAM] Comprehensive Regional Evaluator) for large-scale traffic simulations, emissions, pollutant exposure and health assessment. BEAM CORE can simulate high-resolution activities for millions of passenger and freight agents, including their travel planning, mode choice, and vehicle movements at the roadway link level, and off-network parking/charging behavior at the zonal level. We integrated BEAM CORE with state-of-art emission modeling for both on-network and off-network emissions using the EMission FACtors (EMFAC) model developed by California Air Resources Board (CARB). We use InMAP, the Intervention Model for Air Pollution, to chemically disperse the spatially resolved changes in emissions of PM2.5 and its precursors, quantifying the resulting changes in exposure and mortality outcomes in different neighborhoods and population groups.

In this work, the integrated assessment framework is first calibrated and validated for the San Francisco Bay Area for key traffic and travel activities and emission outputs. Then we applied the calibrated model to four travel and traffic intervention scenarios: (1) Cordon pricing in San Francisco; (2) Recently adopted or planned improvements in the public transportation sector; (3) Post-pandemic telecommuting; and (4) Incentives for active modes. The highly granular travel activities, air quality, and health outcomes from these scenarios are aggregated by subpopulations of interests based on racial, socio-demographic, and location-based factors. The impact metrics derived include both air quality and health and other dimensions of equity concerns in transportation accessibility and mobility. Synergies and tradeoffs in these equity dimensions are quantified and compared across scenarios and subpopulations. The modeling framework developed here can be extended to assessing other types of transportation policies and evolving technologies in different regions to ensure equitable outcomes across diverse communities.

Speaker Biography

Cristian Poliziani is a Postdoctoral Scholar at Lawrence Berkeley National Laboratory, specializing in Transportation System Engineering, with a PhD earned from the University of Bologna, Italy.

Cristian Poliziani's research focuses on developing and evaluating large-scale and agent-based transportation system models. His research can support transport planning and optimization strategies with the aim to improve users' experience and minimize environmental, social, and economic impacts, while striving for more inclusive and equitable transportation solutions.

Co-presenters

Ling Jin
Lawrence Berkeley National Laboratory

Xiaodan Xu
Lawrence Berkeley National Laboratory

James Butler
University of California, Berkeley

Haitam Laarabi
Lawrence Berkeley National Laboratory

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