Purpose: This prototype provides an approximate service area analysis for License Plate Agencies (LPAs) across North Carolina. The analysis assumes that travelers visit the nearest LPA based on drive time using the road network.
The study area is divided into hexagonal zones, each representing a geographic unit with aggregated population. Each hexagon is colored by the real drive time from its centroid to the nearest active LPA. You can filter LPAs by tier and rating, toggle locations on or off, and see how those changes affect access metrics in real time.
Hexagonal zones colored by drive time bands to the nearest LPA, plus facility markers sized by tier and colored by rating. The hexagonal aggregation approach provides a uniform spatial representation while maintaining population-weighted analysis.
Population Data: Population estimates are from the 2023 American Community Survey (ACS) at the census block group level. These are aggregated into hexagonal zones using areal weighting to distribute population proportionally. The population includes all residents (including children under 18 years and older adults). Future versions may refine this to include only the driving-age population.
Spatial Assumptions: For computational purposes, population is assumed to reside at the centroid of each hexagonal zone. Both hexagon centroids and LPA locations are snapped to the nearest node on the road network before computing travel times.
Travel Time Computation: Real-world drive times are computed using r5py, a Python interface to the R5 routing engine. Travel times are calculated from each LPA to hexagon centroids using the North Carolina Open Street Map road network. The analysis assumes travel times are similar in both to/from directions. Network congestion is not modeled; instead, free-flow travel times are used. Therefore, these travel times represent a lower bound on actual travel times that residents would experience, particularly during peak travel periods.
Fink, C., Klumpenhouwer, W., Saraiva, M., Pereira, R., & Tenkanen, H. (2022). R5py: Rapid Realistic Routing with R5 in Python. DOI:10.5281/ZENODO.7060437
Network Boundary Effects: The analysis uses only the North Carolina road network. Edge effects may occur where travelers could theoretically exit the state and re-enter to reach a destination, but these effects are considered relatively minor for this analysis.
Service Area Assumption: The analysis assumes travelers visit their nearest LPA by drive time. In reality, travelers may choose LPAs based on other factors such as familiarity, convenience along commute routes, or service quality.
This research is supported by the North Carolina Department of Transportation. The primary design for the tool was developed by Mohammad Ashraf Ali, and refined through contributions from the research team. The views and accuracy of the information presented belong to the authors alone. NCDOT assumes no liability for the contents or use thereof.
Dr. Venktesh Pandey (NCA&T), Dr. Ali Hajbabaie (NCSU), Mohd Ashraf Ali (NCA&T), and Fahim Kafashan (NCSU)