Abstract for presentation (Poster or Podium)
Infrastructure Systems
Shashi S. Nambisan, PhD, PE (he/him/his)
Director, Transportation Research Center &
Professor of Civil Engineering
University of Nevada, Las Vegas (UNLV) Transportation Research Center
Las Vegas, NV, United States
Shashi S. Nambisan, PhD, PE (he/him/his)
Director, Transportation Research Center &
Professor of Civil Engineering
University of Nevada, Las Vegas (UNLV) Transportation Research Center
Las Vegas, NV, United States
Juliana Byzyka, PhD (she/her/hers)
Assistant Research Engineer & Instructor
UNLV TRC
Las Vegas, NV, United States
Shreya Chindepalli, n/a
Undergraduate Student in Computer Science
University of Texas at Arlington
Las Vegas, Nevada, United States
Khandaker Arafin Islam, n/a
Graduate Student
University of Nevada, Las Vegas
Las Vegas, Nevada, United States
Juliana Byzyka, Ph.D.
UNLV TRC
Las Vegas, Nevada, United States
The presentation investigates the role of smart vehicles at intersections with high left-turn demand to increase traffic signal efficiency for left-turn vehicle maneuvers. The adoption of smart vehicles offers benefits to society in the form of enhanced safety, improved capacity of existing roadway infrastructure, and related economic, and environmental benefits. Capacity of a roadway and the saturation flow rate of an intersection is a reciprocal of minimum headway and gap. Signal timing is a process to “optimize” the operation of signalized intersections and respond to the demands of motor vehicles, bicycles, and pedestrians in an optimum or balanced manner. Signal timing is one of the most cost-effective ways to improve traffic flow, reduce overall delay time at an intersection, account for changes in the traffic characteristics due to growth or new developments, reduce response time for emergency vehicles, and postpone the need for costly road construction by improving traffic flow on existing facilities. Another way to improve traffic flow, safety, and capacity at intersections is left-turn lanes. However, one common issue with such traffic flow intervention is left-turn spillover. Recent developments in connected vehicles (CVs) technology could help to alleviate such a problem due to their capability to measure and communicate the temporal space between consecutive vehicles stopped at a left-turn bay. Such information could then be used to provide efficient use of left-turn lanes, improve capacity, reduce left-turn bay spillover, and design effective signal control strategies. Data from 35 controlled intersections will be presented for single, dual, and triple left-turn lanes across the Las Vegas Metropolitan Area in the state of Nevada. For each intersection, left-turn lane length and capacity are measured. An equation is then formulated, incorporating the mentioned variables to compute the gap between successive vehicles and assess left-turn lane capacity for both non-CVs and CVs. Preliminary calculations, considering roadways with posted speed limits from 25 to 55 mph; left turn lane lengths spanning from 240 to 720 ft; an assumed average vehicle length of 16 ft; and assumed average space headways of 10 ft for non-CVs and 5 ft for CVs; along with an estimated capacity on the left turn bay varying from 9 to 28 vehicles, demonstrate that for non-CVs, the average gap of vehicles at intersections with left-turn lanes ranges from 25 to 30 ft for non-peak hours and from 15 to 20 ft for peak hours. For CVs, the average gap is calculated to be within the range of 15-18 ft during non-peak hours and 9-12 ft during peak hours. Moreover, the capacity of a left-turn lane experiences an exponential increase at various percentages of CVs (0%, 5%, 10%, 20%, 30%, 50%, 80%, and 100%) at an intersection.