Modified K-means clustering for travel time prediction based on historical traffic data

Rudra Pratap Deb Nath; Hyun Jo Lee; Nihad Karim Chowdhury; Jae Woo Chang

doi:10.1007/978-3-642-15387-7_55

Modified K-means clustering for travel time prediction based on historical traffic data

Rudra Pratap Deb Nath
, Hyun Jo Lee
, Nihad Karim Chowdhury
, Jae Woo Chang

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

38 Scopus citations

Abstract

Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naïve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).

Original language	English
Title of host publication	Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings
Pages	511-521
Number of pages	11
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-15387-7_55
State	Published - 2010
Event	14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010 - Cardiff, United Kingdom Duration: 2010.09.8 → 2010.09.10

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6276 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010
Country/Territory	United Kingdom
City	Cardiff
Period	10.09.8 → 10.09.10

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Chain Average (CA)
Intelligent Transportation System (ITS)
K-means Clustering
Naïve Bayesian Classification (NBC)
Successive Moving Average (SMA)

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems

Access to Document

10.1007/978-3-642-15387-7_55

Cite this

Deb Nath, R. P., Lee, H. J., Chowdhury, N. K., & Chang, J. W. (2010). Modified K-means clustering for travel time prediction based on historical traffic data. In Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings (PART 1 ed., pp. 511-521). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6276 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-15387-7_55

Deb Nath, Rudra Pratap ; Lee, Hyun Jo ; Chowdhury, Nihad Karim et al. / Modified K-means clustering for travel time prediction based on historical traffic data. Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings. PART 1. ed. 2010. pp. 511-521 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Modified K-means clustering for travel time prediction based on historical traffic data",

abstract = "Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Na{\"i}ve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).",

keywords = "Chain Average (CA), Intelligent Transportation System (ITS), K-means Clustering, Na{\"i}ve Bayesian Classification (NBC), Successive Moving Average (SMA)",

author = "\{Deb Nath\}, \{Rudra Pratap\} and Lee, \{Hyun Jo\} and Chowdhury, \{Nihad Karim\} and Chang, \{Jae Woo\}",

year = "2010",

doi = "10.1007/978-3-642-15387-7\_55",

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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Deb Nath, RP, Lee, HJ, Chowdhury, NK & Chang, JW 2010, Modified K-means clustering for travel time prediction based on historical traffic data. in Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, 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. 6276 LNAI, pp. 511-521, 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010, Cardiff, United Kingdom, 10.09.8. https://doi.org/10.1007/978-3-642-15387-7_55

Modified K-means clustering for travel time prediction based on historical traffic data. / Deb Nath, Rudra Pratap; Lee, Hyun Jo; Chowdhury, Nihad Karim et al.
Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings. PART 1. ed. 2010. p. 511-521 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6276 LNAI, No. PART 1).

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

TY - GEN

T1 - Modified K-means clustering for travel time prediction based on historical traffic data

AU - Deb Nath, Rudra Pratap

AU - Lee, Hyun Jo

AU - Chowdhury, Nihad Karim

AU - Chang, Jae Woo

PY - 2010

Y1 - 2010

N2 - Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naïve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).

AB - Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naïve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).

KW - Chain Average (CA)

KW - Intelligent Transportation System (ITS)

KW - K-means Clustering

KW - Naïve Bayesian Classification (NBC)

KW - Successive Moving Average (SMA)

UR - https://www.scopus.com/pages/publications/78449255254

U2 - 10.1007/978-3-642-15387-7_55

DO - 10.1007/978-3-642-15387-7_55

M3 - Conference paper

AN - SCOPUS:78449255254

SN - 3642153860

SN - 9783642153860

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

SP - 511

EP - 521

BT - Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings

T2 - 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010

Y2 - 8 September 2010 through 10 September 2010

ER -

Deb Nath RP, Lee HJ, Chowdhury NK, Chang JW. Modified K-means clustering for travel time prediction based on historical traffic data. In Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings. PART 1 ed. 2010. p. 511-521. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-15387-7_55

Modified K-means clustering for travel time prediction based on historical traffic data

Abstract

Publication series

Conference

UN SDGs

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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