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...
Ausführliche Beschreibung
Autor*in: |
Seyed Mohammad Hossein Moosavi [verfasserIn] Mahdi Aghaabbasi [verfasserIn] Choon Wah Yuen [verfasserIn] Danial Jahed Armaghani [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: Journal of Soft Computing in Civil Engineering - Pouyan Press, 2018, 7(2023), 2, Seite 74-97 |
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Übergeordnetes Werk: |
volume:7 ; year:2023 ; number:2 ; pages:74-97 |
Links: |
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DOI / URN: |
10.22115/scce.2023.356348.1503 |
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Katalog-ID: |
DOAJ088813606 |
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10.22115/scce.2023.356348.1503 doi (DE-627)DOAJ088813606 (DE-599)DOAJ3f2077ce7851442d82d99b2bf2682f0d DE-627 ger DE-627 rakwb eng Seyed Mohammad Hossein Moosavi verfasserin aut Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. machine learning tree-based models high-frequency route low-frequency service travel time prediction Technology T Mahdi Aghaabbasi verfasserin aut Choon Wah Yuen verfasserin aut Danial Jahed Armaghani verfasserin aut In Journal of Soft Computing in Civil Engineering Pouyan Press, 2018 7(2023), 2, Seite 74-97 (DE-627)1031100369 (DE-600)2943022-7 25882872 nnns volume:7 year:2023 number:2 pages:74-97 https://doi.org/10.22115/scce.2023.356348.1503 kostenfrei https://doaj.org/article/3f2077ce7851442d82d99b2bf2682f0d kostenfrei http://www.jsoftcivil.com/article_168922_8889297458018cde3b6387844f74ff43.pdf kostenfrei https://doaj.org/toc/2588-2872 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 7 2023 2 74-97 |
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Seyed Mohammad Hossein Moosavi |
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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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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 |
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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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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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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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree-based models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">high-frequency route</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">low-frequency service</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">travel time prediction</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mahdi Aghaabbasi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Choon Wah Yuen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Danial Jahed Armaghani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Soft Computing in Civil Engineering</subfield><subfield code="d">Pouyan Press, 2018</subfield><subfield code="g">7(2023), 2, Seite 74-97</subfield><subfield code="w">(DE-627)1031100369</subfield><subfield code="w">(DE-600)2943022-7</subfield><subfield code="x">25882872</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2</subfield><subfield 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