TITLE:
A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures
AUTHORS:
Enrique D. Saldivar-Carranza, Darcy M. Bullock
KEYWORDS:
Connected Vehicle, Trajectory, Traffic Signal, Performance, Map, Geometry
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.3,
July
21,
2023
ABSTRACT: Connected vehicle (CV) trajectory data provides practitioners with opportunities
to assess traffic signal performance with no investment in detection or
communication infrastructure. With over 500 billion trajectory records
generated each month in the United States, operations can be evaluated
virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate
accurate movement-level trajectory-based performance estimations is the
most time-consuming aspect of using CV data to evaluate traffic signal
operations. Various studies have utilized vehicle location data to update and
create maps; however, most proposed mapping techniques focus on the
identification of roadway characteristics that facilitate vehicle navigation
and not on the scaling of traffic signal performance measures. This paper
presents a technique that uses commercial CV trajectory and open-source
OpenStreetMap (OSM) data to automatically map intersection centers and approach
areas of interest to estimate signal performance. OSM traffic signal tags are
processed to obtain intersection centers. CV data is then used to extract
intersection geometry characteristics surrounding the intersection. To
demonstrate the proposed technique, intersection geometry is mapped at 500
locations from which trajectory-based traffic signal performance measures are
estimated. The results are compared to those obtained from manual geometry
definitions. Statistical tests found that at a 99% confidence level,
upstream-focused performance estimations
are strongly correlated between both methodologies. The presented
technique will aid agencies in scaling traffic signal assessment as it
significantly reduces the amount of manual labor required.