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Summary This paper presents novel vehicle classification technology by utilizing a single‐element piezoelectric sensor placed diagonally on a traffic lane to accurately identify vehicles. Novelty of this technique originates from using diagonally placed piezoelectric strip sensor and machine learning technology to provide a highly accurate and cost‐effective alternative to current vehicle classification systems. Diagonal placements of the piezoelectric strip sensor ensure detection of passing vehicle tires by facilitating vehicle classification process. Presented technology is capable of accurately classifying vehicles into a relatively large number of classifications, including motorcycle, which has proven to be a challenging category in present‐day commercial vehicle classifiers. Vehicle classification is a vital intelligent transportation systems application. Accurate data reporting aids suitable roadway design for safety and capacity and can also support other purposes, such as reporting highway congestion to the general public or providing area denseness data to interested businesses. To make a classification decision, a vehicle's signal is acquired from diagonal piezoelectric strip sensor, processed, and then applied to a machine learning algorithm. A speed estimation technique using the same single‐element piezoelectric sensor was also developed, tested, and compared with an embedded vehicle classifier currently used by the Oklahoma Department of Transportation. Testing on several highway sites indicated up to 97% classification accuracy. This paper presents a complete description of the developed system, including sensor installation, data acquisition and processing, and classification algorithm. Overall, the system offers a high‐performance cost‐effective solution for vehicle classification that minimizes roadwork typically required for loop and sensor installations of current systems. Copyright © 2016 John Wiley & Sons, Ltd.

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Connected vehicle (CV) technology has shown great potential in enhancing safety, mobility, and environmental sustainability of roadway transportation systems by enabling vehicles and infrastructure to exchange real-time information through wireless communications. The information available through CV technology has been used to develop a variety of onboard driver assistance tools and applications. This paper presents an innovative, safety-focused CV application called the high-speed differential warning (HSDW). The application identifies potential hazards resulting from high-speed differentials between HSDW-equipped vehicles (ego-vehicles) and surrounding remote vehicles and then provides alerts to drivers of ego-vehicles to help them take appropriate actions. In this research, real-world scenarios and parameters of the HSDW application are first identified. A driver response strategy for each scenario is then developed in consonance with findings from a public survey on the HSDW application. Next, a comprehensive evaluation of various scenarios is conducted with a well-calibrated Paramics traffic microscopic simulation tool set up for a real-world traffic network. Finally, the safety performance of HSDW-equipped vehicles, unequipped vehicles, and overall traffic is analyzed with the surrogate safety assessment model. Results demonstrate that the proposed HSDW application improves the safety performance of ego-vehicles without compromising the mobility and environmental sustainability performance of the overall traffic.