Congestion Hot Spot Identification using Automated Pattern Recognition

Lisa Kessler, Barbara Karl, Klaus Bogenberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper introduces a methodology which identifies congestion hot spots for individual congestion types. The proposed algorithm first isolates coherent congested clusters out of a spatio-temporally discretized speed matrix. Then, virtually driven trajectories which pass through the respective congestion area are calculated and their speed profiles are analyzed. A congestion type is assigned to each trajectory and thereafter, a congestion type for the overall cluster is determined. Considering the spatial and temporal start and end points of each cluster along with its assigned congestion type, accumulated occurrences of congestion are determined. The methodology is applied to data derived from speed sensors along the Bavarian freeway A9 in Germany. The results show a high share of Stop and Go traffic in the Greater Munich Area. All over the considered stretch, Jam Waves occur frequently, limited to a few locations but widely spread in time.

Original languageEnglish
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141497
DOIs
StatePublished - 20 Sep 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: 20 Sep 202023 Sep 2020

Publication series

Name2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
Country/TerritoryGreece
CityRhodes
Period20/09/2023/09/20

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