A new travel time prediction method for intelligent transportation systems

Hyunjo Lee; Nihad Karim Chowdhury; Jaewoo Chang

doi:10.1007/978-3-540-85563-7_61

A new travel time prediction method for intelligent transportation systems

Hyunjo Lee^*
, Nihad Karim Chowdhury
, Jaewoo Chang

^*Corresponding author for this work

Jeonbuk National University

Research output: Contribution to conference › Conference paper › peer-review

14 Scopus citations

Abstract

Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings
Publisher	Springer Verlag
Pages	473-483
Number of pages	11
Edition	PART 1
ISBN (Print)	3540855629, 9783540855620
DOIs	https://doi.org/10.1007/978-3-540-85563-7_61
State	Published - 2008
Event	12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb, Croatia Duration: 2008.09.3 → 2008.09.5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5177 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
Country/Territory	Croatia
City	Zagreb
Period	08.09.3 → 08.09.5

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Intelligent transportation system (ITS)
Linear regression
Naïve Bayesian classification
Travel time prediction

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems

Access to Document

10.1007/978-3-540-85563-7_61

Cite this

Lee, H., Chowdhury, N. K., & Chang, J. (2008). A new travel time prediction method for intelligent transportation systems. In Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings (PART 1 ed., pp. 473-483). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5177 LNAI, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-85563-7_61

Lee, Hyunjo ; Chowdhury, Nihad Karim ; Chang, Jaewoo. / A new travel time prediction method for intelligent transportation systems. Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. pp. 473-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "A new travel time prediction method for intelligent transportation systems",

abstract = "Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Na{\"i}ve Bayesian Classification (NBC) model because Na{\"i}ve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis.",

keywords = "Intelligent transportation system (ITS), Linear regression, Na{\"i}ve Bayesian classification, Travel time prediction",

author = "Hyunjo Lee and Chowdhury, \{Nihad Karim\} and Jaewoo Chang",

year = "2008",

doi = "10.1007/978-3-540-85563-7\_61",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings",

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Lee, H, Chowdhury, NK & Chang, J 2008, A new travel time prediction method for intelligent transportation systems. in Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5177 LNAI, Springer Verlag, pp. 473-483, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, Zagreb, Croatia, 08.09.3. https://doi.org/10.1007/978-3-540-85563-7_61

A new travel time prediction method for intelligent transportation systems. / Lee, Hyunjo; Chowdhury, Nihad Karim; Chang, Jaewoo.
Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. p. 473-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5177 LNAI, No. PART 1).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - A new travel time prediction method for intelligent transportation systems

AU - Lee, Hyunjo

AU - Chowdhury, Nihad Karim

AU - Chang, Jaewoo

PY - 2008

Y1 - 2008

N2 - Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis.

AB - Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis.

KW - Intelligent transportation system (ITS)

KW - Linear regression

KW - Naïve Bayesian classification

KW - Travel time prediction

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M3 - Conference paper

AN - SCOPUS:57849089208

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SN - 9783540855620

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings

PB - Springer Verlag

T2 - 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008

Y2 - 3 September 2008 through 5 September 2008

ER -

Lee H, Chowdhury NK, Chang J. A new travel time prediction method for intelligent transportation systems. In Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1 ed. Springer Verlag. 2008. p. 473-483. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-85563-7_61

A new travel time prediction method for intelligent transportation systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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