TITLE:
Continuous Flow Intersection Performance Measures Using Connected Vehicle Data
AUTHORS:
Enrique Saldivar-Carranza, Howell Li, Mark Taylor, Darcy Michael Bullock
KEYWORDS:
Performance Measures, Connected Vehicles, Trajectories, Continuous Flow Intersection, Displaced Left Turn
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.12 No.4,
October
26,
2022
ABSTRACT: Continuous flow intersections (CFIs), also known as
displaced left turns (DLTs), are a type of alternative intersection
designed to improve operations at locations with heavy left-turn movements by
reallocating these vehicles to the left side of opposing traffic. Currently,
simulation is commonly used to evaluate operational performance of CFIs.
However, this approach requires significant on-site data collection and is
highly dependent on the analyst’s ability to correctly model the intersection
and driver behavior. Recently, connected vehicle (CV) trajectory data has become widely available and presents
opportunities for the direct measurement of traffic signal performance
measures. This study utilizes CV trajectory data to analyze the performance
of a CFI located in West Valley City, UT. Over 4500 trajectories and 105,000
GPS points are analyzed from August
2021 weekday data. Trajectories are linear-referenced to generate Purdue Probe
Diagrams (PPDs) and extended PPDs to estimate split failures (SF), arrivals on green (AOG), traditional Highway
Capacity Manual (HCM) level of
service (LOS), and the distribution of stops. The estimated operational performance showed effective
progression during the PM peak period
at all the critical internal storage areas with AOG levels at exit
traffic signals between 83% and 100%. In contrast, all external approaches with longer queue storage areas had AOG
values ranging from 2% to 81% during the same time period. The presented
analytical techniques and summary graphics provide practitioners with tools to
evaluate the performance of any CFI where CV trajectories are available without
the need for on-site data collection.