Adaptive model for traffic congestion prediction

Pankaj Mishra, Rafik Hadfi, Takayuki Ito

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

2 Citations (Scopus)

Abstract

Traffic congestion is influenced by various factors like the weather, the physical conditions of the road, as well as the traffic routing. Since such factors vary depending on the type of road network, restricting the traffic prediction model to pre-decided congestion factors could compromise the prediction accuracy. In this paper, we propose a traffic prediction model that could adapt to the road network and appropriately consider the contribution of each congestion causing or reflecting factors. Basically our model is based on the multiple symbol Hidden Markov Model, wherein correlation among all the symbols (congestion causing factors) are build using the bivariate analysis. The traffic congestion state is finally deduced on the basis of influence from all the factors. Our prediction model was evaluated by comparing two different cases of traffic flow. We compared the models built for uninterrupted (without traffic signal) and interrupted (with traffic signal) traffic flow. The resulting prediction accuracy is of 79% and 88% for uninterrupted and interrupted traffic flow respectively.

Original languageEnglish
Title of host publicationTrends in Applied Knowledge-Based Systems and Data Science
Subtitle of host publication29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
EditorsHamido Fujita, Jun Sasaki, Moonis Ali, Masaki Kurematsu, Ali Selamat
PublisherSpringer
Pages782-793
Number of pages12
ISBN (Print)9783319420066
DOIs
Publication statusPublished - 14 Jul 2016
Externally publishedYes
EventInternational Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2016 - Morioka, Japan
Duration: 2 Aug 20164 Aug 2016
Conference number: 29th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9799
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2016
Abbreviated titleIEA/AIE 2016
CountryJapan
CityMorioka
Period2/08/164/08/16

Keywords

  • hide marko model
  • road network
  • traffic flow
  • queue length
  • average speed

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