Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.

Decisions to invest in alternative intelligent transportation system (ITS) technologies are expected to increase in complexity, particularly with the introduction of connected vehicles (CV) and automated vehicles (AV) in the coming years. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. In addition, these methods cannot account for agency preferences and constraints that cannot be converted to dollar values. This study utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied in a case study of the selection between using CV data and point detector data to support the freeway traffic data collection and monitoring service. The four objectives specified in the AHP analysis are providing the required functions, providing the required performance, minimizing the risks and constraints, and maximizing the return on investment. A stochastic return-on-investment analysis using a Monte Carlo simulation was used to calculate the return on investment values for input to the AHP method.

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.

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