The final report for Connected Vehicle Applications for Adaptive Overhead Lighting (On-Demand Lighting), submitted by Dr. Ron Gibbons, Matthew Palmer, and Arash Jahangiri, has been released.

Report Abstract:

The Virginia Tech Transportation Institute (VTTI) has developed an on-demand roadway lighting system and has tested the system’s effect on driver visual performance. On-demand roadway lighting can dramatically reduce energy usage while maintaining or increasing vehicle and pedestrian safety. The system developed by VTTI uses connected vehicle technology (CVT), wireless lighting controls, LED luminaires, and a stand-alone processor on the Virginia Smart Road to sense vehicles and turn on roadway lighting only when needed.

During this research project, the use of on-demand, or just-in-time, lighting was investigated with respect to assessing driver distraction, and to human factors, including a driver’s ability to visually detect and recognize on-road objects and pedestrians. The developed on-demand lighting system described above utilized dedicated short range communication (DSRC), connected vehicle infrastructure (CVI), and centralized wireless lighting controls, and was used with VTTI-developed in-vehicle instrumentation and custom software. The software allowed the study of forward preview time in terms of forward lighting distance needed for drivers to detect roadside pedestrians and hazards.

Visual performance testing revealed a relationship between speed and the amount of forward lighting needed to detect pedestrians and hazards on the side of the roadway, and a small, but statistically insignificant, practical difference in visual performance between on-demand lighting and continuously-on lighting conditions. A survey of participant reactions indicated that the public generally accepts on-demand lighting and does not find it distracting as long as a minimum lighting condition is met. The survey also found that participants felt the system provided a safe driving environment. The main application for an on-demand lighting system would be on roadways with little traffic at night and higher accident rates, or higher conflict areas such as intersections, pedestrian crossings, and merge areas.

Click here to learn more about this project and read the final report.

The final report for Bicycle Naturalistic Data Collection, submitted by Mohammed Elhenawy, Arash Jahangiri, and Dr. Hesham Rakha, has been released.

Report Abstract:

Recently, bicycling has drawn more attention as a sustainable and eco-friendly mode of transportation. Between 2000 and 2011, bicycle commuting rates in the United States rose by 80% in large bicycle friendly cities (BFCs), by 32% in non-BFCs, and overall by 47%. On the other hand, about 700 cyclists are killed and nearly 50,000 are injured annually in bicycle–motor vehicle crashes in recent years in the United States.

More than 30% of cyclist fatalities in the United States from 2008 to 2012 occurred at intersections, and up to 16% of bicycle-related crashes were due to cyclists’ violations at intersections. In light of these statistics, this project focused on investigating factors that affect cyclist behavior and predicting cyclist violations at intersections. Naturalistic cycling data were used to assess the feasibility of developing cyclist violation prediction models. Mixed-effects generalized regression model is used to analyze the data and identify the significant factor affecting the probability of violations by cyclists. At signalized intersections, right turn, side traffic and opposing traffic are statistically significant factors affecting the probability of red light violation. At stop-controlled intersections, the presence of other road users, left turn, right turn and warm weather are statistically significant factors affecting the probability of violations.

Violation prediction models were developed for stop-controlled intersections based on kinetic data measured as cyclists approached the intersection. Prediction error rates were 0% to 10%, depending on how far from the intersection the prediction task was conducted. An error rate of 6% was obtained when the violating cyclist was at a time-to-intersection of about 2 seconds, which is sufficient for most motor vehicle drivers to respond.

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The final report for Field Testing of Connected Vehicle Virginia Test Bed System Performance, submitted by Reginald Viray, Abhijit Sarkar, and Dr. Zac Doerzaph, has been released.

Report Abstract:

This project identified vehicle-to-infrastructure (V2I) communication system limitations on the Northern Virginia Connected Vehicle Test Bed. Real-world historical data were analyzed to determine wireless Dedicated Short Range Communication (DSRC) coverage gaps and overlaps. In addition, a simulated scalability test was run to determine the effects of network congestion on the system. The results from the real-world historical data showed that significant loss of signal occurred due to obstructions commonly found in complex highway systems, including overpasses and underpasses, elevated concrete roadways, and foliage. Consequently, care must be taken to minimize loss of signal when selecting an installation site for roadside equipment (RSEs). The deployment of multiple RSEs or repeaters may be necessary to maximize coverage in localized dead zones. The results from the scalability test showed that the current network architecture is not able to handle a large deployment of connected vehicles (CV). If a large scale of CV were to be deployed, an assessment of the current network design needs to be investigated to account for the number of vehicles and subsequent flow of data expected in the operational area.

Click here to learn more about this project and read the final report.