Filling Traffic Count Gaps with Connected Vehicle Data

Traffic measures are conventionally evaluated in terms of vehicular traffic over time expressed by the annual average daily traffic (AADT). Estimation of vehicular traffic requires a significant effort and data availability. Connected vehicle data provides an alternative approach to associate journeys of a subset of vehicular traffic at roadway facilities with AADT estimates and expand this association to estimate traffic measures where traffic counts are not available. In this study, an AADT prediction model was developed as a function of connected vehicle journeys. The correlation analysis indicated a statistically significant correlation factor of 0.954 (p-value < 0.001). Assuming a normal distribution, linear regression was implemented, and the results indicated that AADT estimates were equal to the number of journeys multiplied by a factor of 0.315 (model R² = 0.941). Traffic estimates from connected vehicle data contribute to traffic analysis and safety evaluations as a function of measures of exposure at locations where regular traffic count data collection is not feasible.