TITLE:
Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data
AUTHORS:
Enrique D. Saldivar-Carranza, Howell Li, Darcy M. Bullock
KEYWORDS:
Diverging Diamond Interchange, Performance Measures, Connected Vehicle, Big Data
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.11 No.4,
September
14,
2021
ABSTRACT: Since the first Diverging Diamond Interchange (DDI) implementation in
2009, most of the performance studies developed for this type of interchange
have been based on simulations and historical crash data, with a small number of studies using Automated Traffic Signal
Performance Measures (ATSPM). Simulation models require considerable
effort to collect volumes and to model actual controller operations. Safety
studies based on historical crashes usually require from 3 to 5 years of data
collection. ATSPMs rely on sensing equipment. This study describes the use of
connected vehicle trajectory data to analyze the performance of a DDI located
in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe
Diagram (PPD) is proposed to assess the levels of delay, progression, and
saturation. Further, an additional PPD variation is presented that provides a
convenient visualization to qualitatively understand progression patterns and
to evaluate queue length for spillback in the critical interior crossover. Over
7000 trajectories and 130,000 GPS points were analyzed between the 7th and the 11th of June 2021 from 5:00 AM to 10:00 PM to estimate the
DDI’s arrivals on green, level of service, split failures, and downstream
blockage. Although this technique was demonstrated for weekdays, the ubiquity
of connected vehicle data makes it very easy
to adapt these techniques to analysis during special events, winter storms,
and weekends. Furthermore, the methodologies presented in this paper can be
applied by any agency wanting to assess the performance of any DDI in their
jurisdiction.