RT Journal Article
A1 Tai-Jin Song
A1 Billy M. Williams
A1 Nagui M. Rouphail

PB iet
T1 Data-driven approach for identifying spatiotemporally recurrent bottlenecks
JN IET Intelligent Transport Systems
VO 12
IS 8
SP 756
OP 764
AB Identification of recurrent bottlenecks is an effective way to hone an appropriate investment in current facilities to relieve congestion. Furthermore, it would enable the ranking or prioritisation of bottlenecks since bottleneck removal and its associated impact alleviation are hampered by limited sources. It is imperative that transportation jurisdiction understand and identify the basis for ranking bottlenecks by exploring: how often they are active; how long it takes the congestion to disappear; and how many miles of road are affected. Previous bottleneck identification schemes have focused on identifying congestion with little attention to distinguishing the recurrent level at the same ‘bottleneck’ location. In contrast to traditional schemes, a data-driven approach for identifying recurrent bottlenecks is introduced, using probe vehicle speed reports. The historical spatiotemporal characteristics of bottlenecks are investigated through a comprehensive analysis of 2253 miles of all state-wide interstates in North Carolina. Using the characteristics determined the recurrent bottleneck locations with a historical time span of bottleneck activation are revealed and tested. The findings of the proposed identification schemes generate critical information in order to quantify and diagnose a bottleneck and its associated impact area.
K1 historical spatiotemporal characteristics
K1 associated impact alleviation
K1 bottleneck activation
K1 appropriate investment
K1 spatiotemporally recurrent bottlenecks
K1 probe vehicle speed reports
K1 associated impact area
K1 state-wide interstates
K1 data-driven approach
K1 transportation jurisdiction
K1 bottleneck identification schemes
K1 bottleneck removal
K1 North Carolina
DO https://doi.org/10.1049/iet-its.2017.0284
UL https://digital-library.theiet.org/;jsessionid=141dmnqosa1po.x-iet-live-01content/journals/10.1049/iet-its.2017.0284
LA English
SN 1751-956X
YR 2018
OL EN