2018 journal article
Data-driven approach for identifying spatiotemporally recurrent bottlenecks
IET INTELLIGENT TRANSPORT SYSTEMS, 12(8), 756–764.
TL;DR:
A data-driven approach for identifying recurrent bottlenecks is introduced, using probe vehicle speed reports, and the characteristics determined the recurrent bottleneck locations with a historical time span of bottleneck activation are revealed and tested.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: October 16, 2018
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.