About

Corey Harper is an assistant professor in the Department of Civil and Environmental Engineering and Heinz School of Information Systems and Public Policy at Carnegie Mellon University. He directs the Future Mobility Systems Lab at CMU, where his research focuses on applying modeling and simulation tools, such as agent-based models and regional traffic models, along with multi-source data analytics to evaluate the equity, environmental, congestion, and policy implications of emerging transportation technologies. Harper's work has included studying climate-resilient transportation systems and automation in transportation, utilizing methods like cost-benefit analysis, machine learning, and simulation to address questions related to weather impacts on congestion and the effects of autonomous vehicles on parking revenues. He has received recognition for his research, including the Elsevier ATLAS Best Paper Award for his work on the equity impacts of automation. Harper holds a B.S. in Civil Engineering from Morgan State University and both an M.S. and Ph.D. in Civil and Environmental Engineering from Carnegie Mellon University. Prior to his faculty position, he worked as a consultant at Booz Allen Hamilton, supporting clients on cyber-physical systems and assisting the U.S. Department of Transportation with connected and automated vehicle integration.

Research topics

• Business
• Engineering
• Transport engineering
• Geography
• Environmental planning
• Advertising

Selected publications

• A unified framework for siting Level 2 electric vehicle charging infrastructure

  Transportation Research Part D Transport and Environment · 2026-04-08

  article
  PublisherDOI

• Grid-ECO: Grid Aware Electric Vehicle Charging Stations Placement Optimizer

  arXiv (Cornell University) · 2026-02-12

  preprintOpen access

  The paper develops a methodology, Grid-ECO, to optimally allocate electric vehicle charging stations (EVCS) within a distribution feeder, while considering EV charging demand at census-level granularity. The underlying problem is NP-hard and requires satisfying nonlinear, nonconvex, three-phase unbalanced AC network constraints while including integer decision variables. Existing works cannot guarantee AC feasibility nor optimality of this problem without either i) relaxing the integer decision variable space or ii) convexifying AC constraints. Proposed Grid-ECO exactly solves the underlying mixed-integer nonlinear program (MINLP) to near-zero optimality gap while prioritizing candidate locations based on grid voltage and current sensitivities. To solve the MINLP exactly, Grid-ECO exactly reformulates it into mixed-integer bilinear program (MIBLP), enabling global optimization using the spatial branch-and-bound algorithm (sBnB). To ensure computational tractability for large-scale feeders, we develop and include a novel presolving strategy based on Sequential Bound Tightening (SBT) with variable filtering and decomposition. Case studies demonstrate that Grid-ECO outperforms the off-the-shelf Gurobi sBnB solver by solving cases where no feasible solution is found within 167 hours. When feasible solution is found by off-the-shelf solver, Grid-ECO reduces solution time by up to 73\% and sBnB node exploration by up to 97\%, while achieving a 0\% optimality gap and guaranteed AC feasibility.

  PublisherDOI

• Examining the role of ridesourcing services during rain: A Chicago case study

  Transportation Research Interdisciplinary Perspectives · 2026-01-06

  articleOpen access

  • Estimated effects of rain on TNC ridership in Chicago with valid confidence intervals using non-parametric methods. • Rain effects are larger in densely populated, high income areas. • Rain effects relative to base ridership are larger in peripheral areas. • Areas with low transit access and car ownership show higher rain effects, suggesting TNC dependency during rain. Transportation network companies (TNCs) are an established transportation mode. Yet, uncertainty remains on the level to which rain affects TNC ridership and how this relates to socioeconomic factors. Leveraging TNC trip and weather data from Chicago we estimate rain effects on ridership using non-parametric methods and use OLS regression to reveal their associations with underlying demographics. We find rain causes ridership fluctuations between −46 % and + 140 %, with highest percentage changes observed in the periphery of Chicago. Ridership tends to decrease in areas near the Chicago Transit Authority rail lines, suggesting a possible alleviating effect of transit. OLS regression reveals areas with higher population tend to experience higher changes in ridership during rain (p < 0.001), and the same is true in areas with higher shares of high-income households (p < 0.05). In addition, higher transit access (p < 0.001) and lower shares of households with no vehicles (p < 0.05) are associated with lower rain effects on ridership.

  PublisherDOI

• Grid-ECO: Grid Aware Electric Vehicle Charging Stations Placement Optimizer

  arXiv (Cornell University) · 2026-02-12

  articleOpen access

  The paper develops a methodology, Grid-ECO, to optimally allocate electric vehicle charging stations (EVCS) within a distribution feeder, while considering EV charging demand at census-level granularity. The underlying problem is NP-hard and requires satisfying nonlinear, nonconvex, three-phase unbalanced AC network constraints while including integer decision variables. Existing works cannot guarantee AC feasibility nor optimality of this problem without either i) relaxing the integer decision variable space or ii) convexifying AC constraints. Proposed Grid-ECO exactly solves the underlying mixed-integer nonlinear program (MINLP) to near-zero optimality gap while prioritizing candidate locations based on grid voltage and current sensitivities. To solve the MINLP exactly, Grid-ECO exactly reformulates it into mixed-integer bilinear program (MIBLP), enabling global optimization using the spatial branch-and-bound algorithm (sBnB). To ensure computational tractability for large-scale feeders, we develop and include a novel presolving strategy based on Sequential Bound Tightening (SBT) with variable filtering and decomposition. Case studies demonstrate that Grid-ECO outperforms the off-the-shelf Gurobi sBnB solver by solving cases where no feasible solution is found within 167 hours. When feasible solution is found by off-the-shelf solver, Grid-ECO reduces solution time by up to 73\% and sBnB node exploration by up to 97\%, while achieving a 0\% optimality gap and guaranteed AC feasibility.

  PublisherOA PDF

• Finding gaps in the national electric vehicle charging station coverage of the United States

  Nature Communications · 2025-01-27 · 31 citations

  articleOpen access

  The United States federal government has invested $7.5 billion into charging infrastructure, including the National Electric Vehicle Infrastructure Program, to build fast charging stations along designated highways for long-distance car travel. We develop a consecutive coverage metric to compute the percent of United States roads (traffic-weighted) that are consecutively accessible within 500 miles of each county. We answer (1) what the state of consecutive coverage is in each county and (2) what the increase in coverage is when designated highways receive fast chargers. In 2023, 10% of counties had at least 75% minimum viable coverage. We find that if all designated highways receive fast-charging stations, 94% of United States counties will reach at least 75% fast charger coverage. However, the remaining counties are rural. This demonstrates that federal funding for fast chargers will help connect most-but not all-counties to the national network of continuously accessible charging stations.

  PublisherOA PDFDOI

• Driving the grid forward: How electric vehicle adoption shapes power system infrastructure and emissions

  Proceedings of the National Academy of Sciences · 2025-09-08 · 5 citations

  articleOpen access

  We model the effect of plug-in electric vehicle (EV) adoption on U.S. power system generator capacity investment, operations, and emissions through 2050 by estimating power systems outcomes under a range of EV adoption trajectory scenarios. Our EV adoption scenarios are informed by 1) an Energy Information Administration scenario with no policy intervention, 2) EV growth expected under the Inflation Reduction Act (IRA), 3) a Biden Administration 50% EV sales target by 2030, 4) the Environmental Protection Agency’s projections under vehicle emissions standards, and 5) the International Energy Agency’s roadmap to Net Zero by 2050. We find across these scenarios that increasing EV adoption induces investment in new wind, solar, storage, and natural gas capacity, affecting power generation mix and emissions. The net effect of increasing EV adoption beyond our IRA base case is to increase power sector emissions by about 5 mtCO 2 eq per EV-year in 2026 (comparable to displaced gasoline vehicle combustion emissions), but this effect rapidly drops to annual levels below 1 mtCO 2 eq per EV-year by 2032 and continues below this level through 2050. Consequential effects of EV adoption vary regionally, with most regions primarily increasing wind or solar capacity and some regions primarily increasing natural gas capacity, even in 2050. Our national emissions estimates per EV-year are relatively robust to the level of EV adoption beyond our baseline and to variation in assumptions about power systems, EV behavior, and policy.

  PublisherDOI

• Siting for demand and equity: Optimizing level 2 electric vehicle charger placement

  Journal of Transport Geography · 2025-08-05 · 4 citations

  article
  PublisherDOI

• Battery Electric Vehicle Safety Issues and Policy: A Review

  World Electric Vehicle Journal · 2025-07-01 · 5 citations

  reviewOpen access

  Battery electric vehicles (BEVs) are seeing widespread adoption globally due to technological improvements, lower manufacturing costs, and supportive policies aimed at reducing greenhouse gas emissions. Governments have introduced incentives such as purchase subsidies and investments in charging infrastructure, while automakers continue to broaden their electric vehicle portfolios. Although BEVs show high overall safety performance comparable to internal combustion engine vehicles (ICEVs), they also raise distinct safety challenges that merit policy attention. This review synthesizes the current literature on safety concerns associated with BEVs, with particular attention to fire risks, vehicle weight, low-speed noise levels, and unique driving characteristics. Fire safety remains a significant issue, as lithium-ion battery fires, although less frequent than those in ICEVs, tend to be more severe and difficult to manage. Strategies such as improved thermal management, fire enclosures, and standardized response protocols are essential. BEVs are typically heavier than ICEVs, affecting crash outcomes and braking performance. These risks are especially important for interactions with pedestrians and smaller vehicles. Quiet operation at low speeds can also reduce pedestrian awareness, prompting regulations for vehicle sound alerts. Together, these issues highlight the need for policies that address both emerging safety risks and the evolving nature of BEV technology.

  PublisherOA PDFDOI

• Corrigendum to “Siting for demand and equity: Optimizing level 2 electric vehicle charger placement” [Journal of Transport Geography, Volume 128, October 2025, 104369]

  Journal of Transport Geography · 2025-12-02

  article
  PublisherDOI

• Analyzing disparities in app-hailed travel during extreme heat in New York City

  Transportation Research Part D Transport and Environment · 2025-03-04 · 5 citations

  articleOpen accessSenior author

  • Ridership increased by six to nine percent compared to normal-weather days. • Fixed effects were used to analyze variation across low- and high-income areas. • Afternoon peak higher by 1.7 to 3.2 rides per 1,000 people in median income areas. • Ridership increased by three percent for every $10,000 increase in income. • Baseline per cap trips were lower in low-income areas compared to high-income areas. To understand extreme weather effects on travel behavior, we examine changes in New York City app-hailed travel during extreme-heat days and estimate whether such changes vary across low- and high-income neighborhoods. We use trip records for July 2019, when several heat-related messages were issued by the National Weather Services, and find that daily ridership was six to nine percent higher on days when heat messages were issued than on matching weekdays in the same month. Our fixed effects regression models estimate that for median income neighborhoods afternoon peak-hour ridership was 1.7 to 3.2 rides higher per 1,000 people during the heat events than on matching weekdays in the same month. This effect increased by an additional 0.5 to 0.6 rides per 1,000 people for every $10,000 increase in average neighborhood per capita income, suggesting that higher income travelers increased trip frequency at a higher rate than lower-income travelers.

  PublisherDOI

Frequent coauthors

• Chris Hendrickson

  Carnegie Mellon University

  16 shared

• Constantine Samaras

  University of Patras

  14 shared

• Sean Qian

  Carnegie Mellon University

  8 shared

• Destenie Nock

  6 shared

• Rick Grahn

  National Renewable Energy Laboratory

  6 shared

• H. Scott Matthews

  ORCID

  5 shared

• Lily Hanig

  Carnegie Mellon University

  4 shared

• Zachary B. Rubinstein

  Carnegie Mellon University

  3 shared

Education

• B.S., Civil Engineering

  Morgan State University

  2013

• M.S., Civil and Environmental Engineering

  Carnegie Mellon University

  2014

• Ph.D., Civil and Environmental Engineering

  Carnegie Mellon University

  2017

Awards & honors

• Elsevier ATLAS Best Paper Award