How can we evacuate with alternative fuel vehicles? PhD student, Denissa Purba, shows that conventional evacuation plans are infeasible for electric vehicles, particularly those with low driving range. Purba and Drs. Kontou and Vogiatzis develop an optimization model that designates evacuation routes for gasoline, electric, and other alternative fuel vehicles, which can be concurrently followed during preemptive evacuations. We recommend that evacuation coordinators and emergency planners design routes that minimize the system’s evacuation time, are seamless in that they eliminate forking and evacuees’ divergence, apply contraflow principles so that each road in the network can be used at maximum capacity, and provide reliable access to charging and refueling infrastructure. Our research paper is openly published at Transportation Research Part C: Emerging Technologies: https://doi.org/10.1016/j.trc.2022.103837.
MSc student Wu and Dr. Kontou publish electric vehicle incentives design paper!
Masters of Science student at CEE UIUC, Yen-Chu Wu, is the leading author of a paper that was recently published in Transportation Research Part D: Transport and Environment focusing on designing and allocating rebates and charging infrastructure investments to induce electric vehicle adoption and achieve emission reduction targets. The analysis indicates that rebates should be provided earlier than chargers due to neighborhood effects of electric vehicle adoption and the minimization of expenditure; availability of home charging influences consumers’ choice and the drivers electrified travel distance; rebates are more effective for modest drivers while charging stations should be prioritized for frequent drivers; network externalities should be further investigated because of their impact on electric vehicle demand. Find the publication, openly accessible, here: https://doi.org/10.1016/j.trd.2022.103320.
PhD student Liu and Dr. Kontou publish transportation energy vulnerability paper!
Transportation energy vulnerability is amplified as gas prices rise. PhD student Shanshan (Shirley) Liu and Dr. Kontou measure exposure, sensitivity, and adaptive capacity to transportation energy burden and provide composite scores of transportation energy vulnerability in the US in our new Sustainable Cities and Society open-access paper https://doi.org/10.1016/j.scs.2022.103805.
A greater share of electric vehicle adoption and use can lower census tracts’ transportation energy vulnerability scores and reduce spatial disparities. Due to unavailable or underfunded transit systems, adaptive capacity cannot discount exposure and sensitivity to transportation energy burden.
Energy Policy paper on disparities in electric vehicle rebates allocation is published!
Our research examining equity issues of electric vehicle rebate allocation was published open access in Energy Policy. The paper was coauthored by Shuocheng Guo and Dr. Kontou; Shuocheng completed this research as part of an independent study under Ria’s advisement before completing his MS degree at UIUC! A brief overview and major findings of this research are presented below:
Overview: Incentives such as electric vehicle rebates assist with alleviating high capital costs of alternative fuel cars. We uncover distributional effects of plug-in electric vehicle rebates, focusing on a program in the State of California. We use economic attributes representative of populations of census tracts as well as data on rebates distributed to plug-in electric vehicle buyers through the Clean Vehicle Rebate Project from 2010 to 2018. Horizontal and vertical equity coefficients are computed, while measurement of spatial association characterizes spatial patterns of rebates allocation across the State. We evaluate the distributional fairness of rebates allocation between income groups and disadvantaged communities.
Major findings: We find that rebates have been predominantly given to high income electric vehicle buyers. However, the share of rebates distributed to low-income groups and disadvantaged communities increased over time and after an income-cap policy was put into effect. Spatial analysis shows high spatial clustering effects and rebates concentration in major metropolitan regions. We reveal neighborhood effects: communities with lower median income or disadvantaged receive higher rebate amounts when these are geographic neighbors to clusters characterized as high rebate amount receivers.
By assessing electric vehicles rebate allocation equity, we highlight potential criteria and directions for future policy design. Equitable incentive design could make a difference in the uptake of electric vehicles in disadvantaged and low-income communities!
New publication on ridesourcing safety externalities
Dr. Kontou’s paper with Prof. Noreen McDonald from the Department of City and Regional Planning of the University of North Carolina at Chapel Hill is now accepted at PLOS ONE. Their paper answers the question “is ridesourcing related to road crashes, injuries, and DUI offenses?” providing insights from Travis county, Texas. They find that increases in ridesourcing trips (Uber and Lyft-like services) were not associated with worsening road safety. While studying the operations of RideAustin, Profs. Kontou and McDonald uncovered that for every 10% increase in ridesourcing trips, there was an expected 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in driving while impaired offenses in Travis County. The magnitude of road safety improvement is smaller compared to the expected safety effectiveness of other interventions, such as seatbelt laws, reduced speeds, and traffic calming design. The paper can also be found in arxiv.
Research on reducing ridesourcing empty vehicle miles with travel demand prediction is published
Our paper on reducing ridesourcing empty vehicle travel with future travel demand prediction appeared at the Transportation Research Part C: Emerging Technologies journal. This paper was motivated by the potential to reduce deadhead mileage of ridesourcing trips by providing drivers with information on future ridesourcing trip demand. Future demand information enables the driver to wait in place for the next rider’s request without cruising around and contributing to congestion. A machine learning model was employed to predict hourly and 10-minute future interval travel demand for ridesourcing at a given location. Using future demand information, we developed algorithms to (i) assign drivers to act on received demand information by waiting in place for the next rider, and (ii) match these drivers with riders to minimize deadheading distance. Real-world data from ridesourcing providers in Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing) were leveraged. Results show that this process achieves 68%–82% and 53%–60% reduction of trip-level deadheading miles for the RideAustin and DiDi Chuxing sample operations respectively, under the assumption of unconstrained availability of short-term parking. It was a great opportunity to collaborate with colleagues from the Center for Integrated Mobility Sciences of the National Renewable Energy Laboratory! More info here.
New publication on willingness to pay for public EV charging
Our paper on willingness to pay (WTP) for electric vehicle public charging infrastructure appeared at the Transportation Research Part D: Transport and Environment journal. We construct three WTP functions that describe the maximum amount that potential users of charging infrastructure are willing to pay to access the current level of charging availability. We determine functions that describe WTP for plug-in hybrid electric vehicle drivers, and battery electric vehicle drivers for inter-regional & intra-regional travel. A numerical experiment leveraging data from the State of California demonstrates that existing public charging infrastructure worths $1,500 for the average intra-regional battery electric vehicle driver and is valued at over $6,500 along intercity routes! More info here.