Leveraging Connected Vehicle Data to Assess Interstate Exit Utilization and Identify Charging Infrastructure Investment Allocation Opportunities

Abstract

With an influx of public and private sector investment in the electric vehicle (EV) domain, public agencies and stakeholders need objective, equitable and systematic processes for identifying candidate sites for siting charging stations. This paper reports on a case study examining the Indiana Interstate network using connected vehicle data (CV). The Indiana Interstate network analyzed by this study is composed of 1247 centerline miles along nine routes. Each month, approximately 13 billion CV records representing more than 44 million unique trips are generated along all roads in Indiana. For this study 3.02 billion records comprising 4.78 million trips on and around Indiana Interstates and Exits were analyzed for usage patterns. The CV data was predominantly from internal combustion engine vehicle (ICEV) passenger cars, but provides insight into exit utilization and dwell times at 544 exits on 9 interstate roadways to evaluate how their current usage would align with building out Indiana’s Alternative Fuel Corridors. A pareto sorted graphic for the top 50 busiest exits in the state shows that all but two are not well served by fast charging infrastructure. The paper suggests this pareto sorted list as a good starting point for further analysis and identified 15 exits on Indiana interstates, if chosen for deploying charging infrastructure, would ensure full compliance. The results provide a systemwide look at present dwell patterns among ICEVs and help identify locations of interest that would most benefit from addition of charging infrastructure as the current fleet of ICEVs gradually transitions to EVs.

Using Connected Vehicle Data to Evaluate National Trip Trends

“…Emerging connected vehicle (CV) data can act as an alternative data source to potentially deliver a more scalable method to measure temporal and spatial usage of passenger vehicles in near real-time for a variety of applications, including trip origin-destinations, quantifying traffic congestion, travel mode choice, traffic safety, and infrastructure planning [16][17][18][19][20]. Early studies have shown that these CV datasets capture around 500 billion vehicle records in a month nationwide [21] for the United States, with a rough market penetration of 5-7% [22].…”

“…Combining all of these emerging CV data attributes can provide stakeholders and planning commissions with a holistic view of the transportation network and present opportunities to invest in infrastructure improvements, including electric vehicle charging. Previous studies have looked at the use of CV data to assess interstate exit utilization to aid in charging infrastructure site selection and to monitor EV and hybrid vehicle (HV) usage patterns, including operational performance measures such as vehicle miles traveled and charger utilization [17,18]. Thus, as outlined in the preceding text, current survey practices for collecting trip data face multiple concerns in terms of the cost and time of acquisition, low response rates, accuracy of self-reported trip data, and data representativeness, among others.…”

Applications of using connected vehicle data for pavement quality analysis

“…As vehicles become increasingly connected and equipped with advanced sensors/ communication technologies, they can generate and transmit real-time data on traffic conditions, weather, road quality, and much more (Hadi et al, 2017;Intelligent Transportation Systems, 2023;Sakhare et al, 2023). Some of the use cases for connected vehicle data include impact of technology (digital alerts and automated speed enforcement), traffic signal analytics (impacts of timing changes), and infrastructure assessment (exit utilization, electric vehicle charging placement, and pavement marking evaluation) (Day et al, 2017;Mahlberg et al, 2021a;Sakhare et al, 2021;Desai et al, 2022;Mathew et al, 2022;Saldivar-Carranza et al, 2022). There are no established best practices for the use of connected vehicle data in transportation asset management (TAM) systems, but substantial opportunities are being created to use CV data for TAM systems as the penetration and technology continues to grow (Dennis et al, 2014a).…”