Cargando…
A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692405/ https://www.ncbi.nlm.nih.gov/pubmed/26710073 http://dx.doi.org/10.1371/journal.pone.0145348 |
_version_ | 1782407252665171968 |
---|---|
author | Wang, Xiaomeng Peng, Ling Chi, Tianhe Li, Mengzhu Yao, Xiaojing Shao, Jing |
author_facet | Wang, Xiaomeng Peng, Ling Chi, Tianhe Li, Mengzhu Yao, Xiaojing Shao, Jing |
author_sort | Wang, Xiaomeng |
collection | PubMed |
description | Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing. |
format | Online Article Text |
id | pubmed-4692405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46924052016-01-12 A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data Wang, Xiaomeng Peng, Ling Chi, Tianhe Li, Mengzhu Yao, Xiaojing Shao, Jing PLoS One Research Article Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing. Public Library of Science 2015-12-28 /pmc/articles/PMC4692405/ /pubmed/26710073 http://dx.doi.org/10.1371/journal.pone.0145348 Text en © 2015 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Wang, Xiaomeng Peng, Ling Chi, Tianhe Li, Mengzhu Yao, Xiaojing Shao, Jing A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title | A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title_full | A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title_fullStr | A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title_full_unstemmed | A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title_short | A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data |
title_sort | hidden markov model for urban-scale traffic estimation using floating car data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692405/ https://www.ncbi.nlm.nih.gov/pubmed/26710073 http://dx.doi.org/10.1371/journal.pone.0145348 |
work_keys_str_mv | AT wangxiaomeng ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT pengling ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT chitianhe ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT limengzhu ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT yaoxiaojing ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT shaojing ahiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT wangxiaomeng hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT pengling hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT chitianhe hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT limengzhu hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT yaoxiaojing hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata AT shaojing hiddenmarkovmodelforurbanscaletrafficestimationusingfloatingcardata |