Skip navigation

Conference Abstract: Assessing the Energy and Environmental Implications Due to the Emergence of Autonomous Vehicles

This is the abstract submitted for a presentation given at the International Symposium on Multimodal Transportation (ISMT), held December 6-7 2019 at the National University of Singapore.  The presentation was given by Zimo Zhang, based on his work on the CCAT project Behavioral Intention to Ride in an Autonomous Vehicle and Implications on Mode Choice Decisions, Energy Use, and Emissions.

Autonomous vehicles (AV) hold promise for the future of transportation with numerous benefits.  In addition to increased safety, driving experience, and traffic efficiency, AVs are also expected to impact greenhouse gas emissions, air quality and energy use.  Benefited from the advanced automation equipment and system-level assignment strategy, AVs offer unprecedented opportunities to smarter driving.  A large number of studies have already been done in terms of estimating AV's impact on mobility by conducting city level simulation.  However, studies evaluating the environmental impact of AVs and shared AVs (SAVs), which is considered to be an important benefit that AV might bring, based on macro-simulation have been scarce.  The objective of this study is twofold.  First, it is trying to fill the gap by designing a framework to estimate the environmental impact of AVs at a city-level simulation area using agent-based model (ABM). The proposed model could compare the environmental performance of AVs with that of traditional vehicles by designing different scenarios.  Second, it showcases the proposed framework using the case study of the Indianapolis metropolitan area.  An analysis has been done to estimate whether AVs could reduce greenhouse gas and air pollutant emissions and contribute to environmental improvement.

To achieve the research objectives, each individual's (individual is defined as a SAV) status is tracked during the simulation period and an overview for the whole fleet is generated.  Note that both individualized information and overall performance are of interest.  Different from traditional top-down simulation approaches, ABM is set up starting with agents (individuals in the system) and their interaction rules.  Complex systems such as urban AV systems have numerous decision makers (AV, passengers) behaving separately on the basis of different strategies (route searching, vehicle assignment, etc.). ABM approach enables setting specific behavior rules for each agent and as such, ABM is appropriate modeling technique to explore urban AV systems.

The proposed simulation model operates by generating personal-trips in each traffic analysis zone (TAZ) throughout the real road network across Indianapolis metropolitan area during the morning commuting period.  The network and traffic TAZ data for Indianapolis metropolitan area was collected from United States Census Bureau website.  Commuting origin-destination (OD) matrix data was collected from the Census Transportation Planning Products Program (CTPP) website. The model framework was built on two important agents (passengers and SAVs), and the simulation steps were grouped into three steps: 1) generating demand; 2) dispatching SAVs; and 3) monitoring fleet performance.  Different scenarios have been designed to test the impact of fleet size and fleet composition on greenhouse gas emissions, air pollutants, and energy consumption.