Prediction of Travel Time Estimation Accuracy in Connected Vehicle Environments

Abstract

Effective management of transportation networks requires accurate travel time information that is largely determined by the quality of collected real-time traffic data. In connected vehicle (CV) environments, wherein equipped vehicles may be the primary source of reliable travel time data, accuracy and reliability of travel time estimates present a challenge due to the low market penetration at the early deployment stages of CV technology. The absence of ground truth data presents another challenge for quantifying the accuracy and reliability of travel time estimates. Therefore, CV infrastructure should be well planned in transportation networks to achieve acceptable and reliable estimates of travel times. Recent research shows that the accuracy of travel time estimates is influenced by traffic density, CV market penetration, and transmission range. These factors also impact the vehicle-to-vehicle and vehicle-to-infrastructure communication stability in a transportation network. This suggests correlation between the accuracy and reliability of travel time estimates and the communication stability. This study develops regression models to measure the accuracy and reliability of travel time estimates as a function of communication stability. Such models can help transportation planners assess the anticipated accuracy and reliability of travel time estimates in CV environments, as well as make better infrastructure investment decisions to ensure an acceptable level of accuracy and reliability of travel time estimates.