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Speeding has been and continues to be a major contributing factor to trafficfatalities. Various transportation agencies have proposed speed managementstrategies to reduce the amount of speeding on arterials. While there have beenvarious studies done on the analysis of speeding proportions above the speedlimit, few studies have considered the effect on the individual's journey. Manystudies utilized speed data from detectors, which is limited in that there isno information of the route that the driver took. This study aims to explorethe effects of various roadway features an individual experiences for a givenjourney on speeding proportions. Connected vehicle trajectory data was utilizedto identify the path that a driver took, along with the vehicle relatedvariables. The level of speeding proportion is predicted using multiplelearning models. The model with the best performance, Extreme GradientBoosting, achieved an accuracy of 0.756. The proposed model can be used tounderstand how the environment and vehicle's path effects the drivers' speedingbehavior, as well as predict the areas with high levels of speedingproportions. The results suggested that features related to an individualdriver's trip, i.e., total travel time, has a significant contribution towardsspeeding. Features that are related to the environment of the individualdriver's trip, i.e., proportion of residential area, also had a significanteffect on reducing speeding proportions. It is expected that the findings couldhelp inform transportation agencies more on the factors related to speeding foran individual driver's trip.

Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding