Impact of Charging Infrastructure on Electric Vehicle Adoption: A Synthetic Population Approach

Cirillo, Cinzia; Burra, Lavan Teja; Al-khasawneh, Mohammad Bilal Mohammad

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Impact of Charging Infrastructure on Electric Vehicle Adoption: A Synthetic Population Approach

2026-03-01
By Cirillo, Cinzia ; Burra, Lavan Teja ; Al-khasawneh, Mohammad Bilal Mohammad

File Language:

English

Details

Creators:

Cirillo, Cinzia ; Burra, Lavan Teja ; Al-khasawneh, Mohammad Bilal Mohammad
Corporate Creators:

University of Maryland (College Park, Md.) ; Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC)
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Charging Infrastructure Data Collection DC Fast Charging Electric Vehicle Charging Electric Vehicles Infrastructure Surveys Synthetic Population Urban Areas
Publication/ Report Number:

SM25
Resource Type:

Tech Report
Geographical Coverage:

United States ; Maryland
Edition:

Final Report
Corporate Publisher:

Morgan State University.Safety and Mobility Advancement Regional Transportation and Economics Research Center (SMARTER) University Transportation Center (UTC)
Abstract:

Currently there is limited availability of travel survey data on households with electric vehicles (EVs) and fine geographic data on factors influencing EV ownership levels. To address this gap, we propose an integrated approach utilizing a discrete choice model and a Bayesian network-generated synthetic population. Applied to Maryland, the model analyzes the impact of public charging stations (level-2 and DC fast chargers) on EV ownership at the census tract level. Access to fast charging, workplace charging, and the possibility of teleworking are key factors influencing EV ownership. The model, applied to the synthetic population, predicts higher EV growth in suburban regions compared to urban areas. This demonstrates the application of this methodology in understanding micro-level EV adoption rates for informing targeted policies and infrastructure development.
Format:

PDF
Funding:

69A3552348303
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:da600e6e2862aff2c9afab4b8d39615a181fdcf91bd550f6327babac92a1c03479c1b0e462a3e9dd55c59d68796160b324051ec602f40f7dc517ef9fde02e168
Download URL:

https://rosap.ntl.bts.gov/view/dot/92853/dot_92853_DS1.pdf
File Type:

[PDF - 873.67 KB ]

File Language:

English

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