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Eco-Transportation and Alternative Technologies

Andy Alden in front of a Blacksburg transit bus

The Eco-Transportation and Alternative Technologies group (ETAT) is a partnership between VTTI, the Virginia Department of Transportation (VDOT), the Virginia Center for Transportation Innovation and Research (VCTIR), the Virginia Tech Institute for Critical Technology and Applied Science (ICTAS), the Virginia Tech College of Engineering, and the Virginia Tech Office of the Vice President for Research. ETAT is centered at VTTI with access to resources such as the Virginia Smart Road, VTTI researchers, and additional VTTI support staff.

Andy Alden, Group Leader

Andy Alden
Group Leader

Featured Projects

Blacksburg Transit Dynamic Bus Routing and Scheduling Study

Traditional transit operations use scheduled routes and sometimes on-demand or "tripper" buses in their daily operations to address normal and unexpected system loads. These strategies have long been the best approach to serving the needs of the commuting public while working within the constraints of limited resources. Recent technological advances in computing and communications along with the advent of connected vehicles have paved the way for the real-time assessment of bus capacity and rider demand. These capacities coupled with the increased usage of smart phone and web applications may enable a paradigm shift with respect to how transit operators can increase the dependability of their services while best utilizing their limited resources. Accurate, real-time measurement of bus passenger and bus stop queue counts along with the dynamic dispatch and re-routing of buses may allow transit operators to better serve the riding public while simultaneously decreasing operating expenses and adverse environmental impacts. Technology that allows the accurate real-time determination of bus loading is currently available and used. Magnetic card reading, near-field communication (NFC), radio frequency identification (RFID), and machine vision technologies may be used actively or passively (dependent upon technology) to assess demand in real time through measurement of bus queues. The information collected via these technologies can be used to develop innovative new models for bus routing that either do away with traditional scheduled routing altogether via dedicated, on-demand routing only or employ a hybrid model in which both traditional scheduled and on-demand routing are employed. Real-time data on bus occupancy can also be used to dynamically adjust traffic signal preemption priorities for transit precedence to further enhance system reliability.

Prediction of Roadway Surface Conditions Using On-Board Vehicle Sensors

This project proposes a method to predict compromised roadway conditions. The differential rotational displacement of driven versus free-rolling wheels of a vehicle traveling along a stretch of roadway is used to predict the relative coefficient of friction between tire and pavement. This method does not rely upon the initiation of onboard safety systems such as anti-lock brakes; instead, it provides a notification of diminished tire traction before safety system activation thresholds are attained. As roadway conditions change due to changes in factors including pavement properties and weather, this information can be shared across the connected-vehicle network to provide alerts to approaching drivers and supply modified operational parameters for use by their on-board safety systems. This real time data will also help optimize roadway maintenance operations such as salt application and cleaning to reduce costs and improve safety and sustainability.

Roadside Evaluation of a Buried Cable Animal Detection System

This project aims to evaluate an innovative roadside animal detection system in naturalistic and controlled conditions. The studied animal detection system is a buried cable system that detects the crossing of large animals and provides data on their locations along the length of the cable. The system will be installed and tested at a highly suitable site on the Virginia Smart Road where large wild animals are often observed. Researchers will use continuous, all-weather, and night-time capable video surveillance systems to monitor animal movement and gauge system detections along with potential non-detections. Recorded data will be analyzed to determine overall detection system performance and suitability for implementation in problem areas on Virginia public roads. If necessitated by the results, a second phase of evaluation on a public road will be proposed.

Animal-vehicle collisions (AVCs) are a common occurrence and a significant safety problem on America’s 3.9 million miles of roads. As of 2008, more than 300,000 AVCs have occurred every year in the U.S., resulting in many potentially preventable injuries, nearly 300 human fatalities, and property damages estimated at more than $2000 per occurrence. AVCs now account for more than 5% of all reported motor vehicle collisions, and the problem is only growing as both vehicle-miles-traveled and wildlife numbers near roads increase. Even as the rate of overall motor vehicle crashes has leveled off, the occurrence of AVCs has increased. Approximately 4-10% of AVCs involving large animals result in human injury, and the costs of related property damage, medical care, crash management, and animal carcass management exceed $8 billion/year. This number does not include secondary costs related to traffic delays, emergency management, litigation, infrastructure damage, etc. These issues are further exacerbated in western states where fences are uncommon and open range policies place liability for livestock-vehicle collisions on drivers.


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