Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques

Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus route...
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Autor*in:	Seyed Mohammad Hossein Moosavi [verfasserIn] Mahdi Aghaabbasi [verfasserIn] Choon Wah Yuen [verfasserIn] Danial Jahed Armaghani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	machine learning tree-based models high-frequency route low-frequency service travel time prediction

Übergeordnetes Werk:	In: Journal of Soft Computing in Civil Engineering - Pouyan Press, 2018, 7(2023), 2, Seite 74-97
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:2 ; pages:74-97

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.22115/scce.2023.356348.1503

Katalog-ID:	DOAJ088813606

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520			\|a Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method.
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author	Seyed Mohammad Hossein Moosavi
spellingShingle	Seyed Mohammad Hossein Moosavi misc machine learning misc tree-based models misc high-frequency route misc low-frequency service misc travel time prediction misc Technology misc T Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques
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topic_title	Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques machine learning tree-based models high-frequency route low-frequency service travel time prediction
topic	misc machine learning misc tree-based models misc high-frequency route misc low-frequency service misc travel time prediction misc Technology misc T
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title	Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques
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title_full	Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques
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author_browse	Seyed Mohammad Hossein Moosavi Mahdi Aghaabbasi Choon Wah Yuen Danial Jahed Armaghani
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title_sort	evaluation of applicability and accuracy of bus travel time prediction in high and low frequency bus routes using tree-based ml techniques
title_auth	Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques
abstract	Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method.
abstractGer	Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method.
abstract_unstemmed	Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method.
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title_short	Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques
url	https://doi.org/10.22115/scce.2023.356348.1503 https://doaj.org/article/3f2077ce7851442d82d99b2bf2682f0d http://www.jsoftcivil.com/article_168922_8889297458018cde3b6387844f74ff43.pdf https://doaj.org/toc/2588-2872
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