A Methodology to Assess the Quality of Travel Time Estimation and Incident Detection Based on Connected Vehicle Data

Abstract

Connected vehicle (CV) technologies are expected to have a significant influence on the investment decisions of transportation system management and operations (TSMO) in the near future. One of the potential applications is the use of CV data to support various TSMO processes. This study investigates the use of CV data as an alternative to existing data acquisition techniques in providing two critical functions to support TSMO: travel time estimation and incident detection. In support of this investigation, the study develops regression models to estimate the accuracy and reliability of travel time measurement and latency of incident detection as functions of the traffic demand level and the proportion of CV in the traffic stream. The developed regression models are used in conjunction with a prediction of CV proportions in future years to determine when the CV technology can provide sufficient data quality to replace existing data acquisition systems. The results can be used by TSMO programs and agencies to plan their investment in data acquisition alternatives in future years.