Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps
Abstract In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods t...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2017-10-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-017-14237-8 |
id |
doaj-0a830179f3ff4ff08f3720637172b061 |
---|---|
record_format |
Article |
spelling |
doaj-0a830179f3ff4ff08f3720637172b0612020-12-08T02:06:26ZengNature Publishing GroupScientific Reports2045-23222017-10-017111110.1038/s41598-017-14237-8Revealing the day-to-day regularity of urban congestion patterns with 3D speed mapsClélia Lopez0Ludovic Leclercq1Panchamy Krishnakumari2Nicolas Chiabaut3Hans van Lint4Univ. Lyon, IFSTTAR, ENTPE, LICITUniv. Lyon, IFSTTAR, ENTPE, LICITDelft University of Technology, CITGUniv. Lyon, IFSTTAR, ENTPE, LICITDelft University of Technology, CITGAbstract In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.https://doi.org/10.1038/s41598-017-14237-8 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Clélia Lopez Ludovic Leclercq Panchamy Krishnakumari Nicolas Chiabaut Hans van Lint |
spellingShingle |
Clélia Lopez Ludovic Leclercq Panchamy Krishnakumari Nicolas Chiabaut Hans van Lint Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps Scientific Reports |
author_facet |
Clélia Lopez Ludovic Leclercq Panchamy Krishnakumari Nicolas Chiabaut Hans van Lint |
author_sort |
Clélia Lopez |
title |
Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps |
title_short |
Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps |
title_full |
Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps |
title_fullStr |
Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps |
title_full_unstemmed |
Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps |
title_sort |
revealing the day-to-day regularity of urban congestion patterns with 3d speed maps |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-10-01 |
description |
Abstract In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected. |
url |
https://doi.org/10.1038/s41598-017-14237-8 |
work_keys_str_mv |
AT clelialopez revealingthedaytodayregularityofurbancongestionpatternswith3dspeedmaps AT ludovicleclercq revealingthedaytodayregularityofurbancongestionpatternswith3dspeedmaps AT panchamykrishnakumari revealingthedaytodayregularityofurbancongestionpatternswith3dspeedmaps AT nicolaschiabaut revealingthedaytodayregularityofurbancongestionpatternswith3dspeedmaps AT hansvanlint revealingthedaytodayregularityofurbancongestionpatternswith3dspeedmaps |
_version_ |
1724394109883383808 |