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.

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

“…An important input to the calculation of delay in the above equations is the reduction in the incident duration due to the reduction in the detection time resulting from applying the alternative detection technology. The incident detection time based on CV data and traffic detector were derived based on simulation results by the authors of this study, as shown in Equations 4 and 5, respectively ( 25 , 26 ):…”

“…where l = Arrival rate (demand) (vph) m = Arrival rate (demand) (vph) m R = Reduced capacity rate under metering for ramps or under non-recurrent conditions for the mainline (vph) t R = Event duration (hr) t Q = Time in queue duration (hr) An important input to the calculation of delay in the above equations is the reduction in the incident duration due to the reduction in the detection time resulting from applying the alternative detection technology. The incident detection time based on CV data and traffic detector were derived based on simulation results by the authors of this study, as shown in Equations 4 and 5, respectively (25,26):…”

An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri

“…Iqbal et al [ 30 ] evaluated the application of basic safety messages (BSMs) obtained from the vehicle-to-vehicle (V2V) communication environments of connected vehicles (CVs) for travel time estimations and incident detection. Regression models were used to estimate vehicle travel times.…”

“…The review of the literature indicated that the application of V2I probe data for automatic incident detection had not been previously investigated. Iqbal et al [ 30 ], and Wolfgram et al [ 31 ] utilized basic safety messages (BSMs) obtained from the vehicle-to-vehicle (V2V) communication environments for automatic incident detection. However, to the best of the authors’ knowledge, this paper is the first study to propose an automatic incident detection algorithm on the basis of V2I probe data.…”