VMT, and demand for this travel continues to grow (especially in megaregions). Much past work recognizes that the out-of-town, intercity, and long distance travel market offers some of the biggest opportunities for automated vehicles at all levels, as it allows travelers to do other things during long trips (e.g. work, relaxing, spending time with family and friends). Past work also hypothesizes that automated vehicles (AV) will likely attract some of the current airline market share, increasing congestion on the interstate system. However, it is still unclear both how widespread these trends will be and where these trends are most likely to occur. This study focuses on the second objective by developing a weighted metric, referred to as an ‘AV-Interstate Congestion Score’, to evaluate how likely each county in the US was to see an increase in interstate congestion due to additional long distance AV traffic. Based on the long distance travel and AV literature, four different factors based on national data sources were selected to describe potential increases in AV use:
- ‘Employment Opportunities’ characterize employment at the Metropolitan Statistical Area (MSA) level based on the number of establishments, total employees, annual payroll and a national location quotient.
- ‘Airport Connectivity’ characterize airport enplanements and was quantified based on the number of passengers reported at each medium and large airport.
- ‘Transit Quality’ characterize transit service based on service area, trips per week and ridership for each US urban area.
- ‘Recreational Choices’ characterize choices at the MSA level based on the total number of recreational establishments, number of recreational industries, annual payroll and a national location.
Each county received a value for each metric using a modified gravity-access model, summing the scores to all the destinations weighted by the travel distance needed to reach each destination. These scores were standardized so counties could receive a score for each metric from 0 (low influence) to 1 (high influence). Each county had 13 scores describing factors influencing individuals’ likelihood of traveling long distance with an AV.
Finally, 7 metrics were weighted in a single equation (with weights determined based on the literature) to generate a single ‘AV-Interstate Congestion Score’ describing how likely each county in the US was to see an increase in interstate congestion due to additional long distance automated vehicle traffic. Maps generated based on individual metrics and the final ‘AV-Interstate Congestion Score’ were generated and highlight that the Northeast and South has the highest potential for additional AV interstate traffic. Planners and policy makers will be able to use these maps (and be able to do their own weighting schemes based on the metrics) to determine a) how much a potential demand AVs will have on their roadway system for long distance travel and b) where those trips will likely be going. Additionally, states may be able to use the results to prioritize projects and funds to support anticipated AV demand and proactively address congestion.