AI-based neural network models for bus passenger demand forecasting using smart card data

Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more p...
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Autor*in:	Liyanage, Sohani [verfasserIn] Abduljabbar, Rusul [verfasserIn] Dia, Hussein [verfasserIn] Tsai, Pei-Wei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Rechteinformationen:	Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0

Schlagwörter:	Artificial intelligence Bus demand prediction Deep learning Neural networks On-demand public transport Short-term prediction

Übergeordnetes Werk:	Enthalten in: Journal of urban management - Amsterdam [u.a.] : Elsevier, 2012, 11(2022), 3 vom: Sept., Seite 365-380
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:3 ; month:09 ; pages:365-380

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.jum.2022.05.002

Katalog-ID:	1818041456

Internformat


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allfields	10.1016/j.jum.2022.05.002 doi 10419/271473 hdl (DE-627)1818041456 (DE-599)KXP1818041456 DE-627 ger DE-627 rda eng Liyanage, Sohani verfasserin aut AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206 Abduljabbar, Rusul verfasserin aut Dia, Hussein verfasserin aut Tsai, Pei-Wei verfasserin aut Enthalten in Journal of urban management Amsterdam [u.a.] : Elsevier, 2012 11(2022), 3 vom: Sept., Seite 365-380 Online-Ressource (DE-627)837630150 (DE-600)2837330-3 (DE-576)446573256 2589-0360 nnns volume:11 year:2022 number:3 month:09 pages:365-380 https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true Verlag kostenfrei https://doi.org/10.1016/j.jum.2022.05.002 Resolving-System kostenfrei https://hdl.handle.net/10419/271473 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 11 2022 3 9 365-380 26 01 0206 4194515358 x1z 05-10-22 2403 01 DE-LFER 4208399864 00 --%%-- --%%-- n --%%-- l01 10-11-22 2403 01 DE-LFER https://doi.org/10.1016/j.jum.2022.05.002 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true
spelling	10.1016/j.jum.2022.05.002 doi 10419/271473 hdl (DE-627)1818041456 (DE-599)KXP1818041456 DE-627 ger DE-627 rda eng Liyanage, Sohani verfasserin aut AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206 Abduljabbar, Rusul verfasserin aut Dia, Hussein verfasserin aut Tsai, Pei-Wei verfasserin aut Enthalten in Journal of urban management Amsterdam [u.a.] : Elsevier, 2012 11(2022), 3 vom: Sept., Seite 365-380 Online-Ressource (DE-627)837630150 (DE-600)2837330-3 (DE-576)446573256 2589-0360 nnns volume:11 year:2022 number:3 month:09 pages:365-380 https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true Verlag kostenfrei https://doi.org/10.1016/j.jum.2022.05.002 Resolving-System kostenfrei https://hdl.handle.net/10419/271473 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 11 2022 3 9 365-380 26 01 0206 4194515358 x1z 05-10-22 2403 01 DE-LFER 4208399864 00 --%%-- --%%-- n --%%-- l01 10-11-22 2403 01 DE-LFER https://doi.org/10.1016/j.jum.2022.05.002 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true
allfields_unstemmed	10.1016/j.jum.2022.05.002 doi 10419/271473 hdl (DE-627)1818041456 (DE-599)KXP1818041456 DE-627 ger DE-627 rda eng Liyanage, Sohani verfasserin aut AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206 Abduljabbar, Rusul verfasserin aut Dia, Hussein verfasserin aut Tsai, Pei-Wei verfasserin aut Enthalten in Journal of urban management Amsterdam [u.a.] : Elsevier, 2012 11(2022), 3 vom: Sept., Seite 365-380 Online-Ressource (DE-627)837630150 (DE-600)2837330-3 (DE-576)446573256 2589-0360 nnns volume:11 year:2022 number:3 month:09 pages:365-380 https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true Verlag kostenfrei https://doi.org/10.1016/j.jum.2022.05.002 Resolving-System kostenfrei https://hdl.handle.net/10419/271473 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 11 2022 3 9 365-380 26 01 0206 4194515358 x1z 05-10-22 2403 01 DE-LFER 4208399864 00 --%%-- --%%-- n --%%-- l01 10-11-22 2403 01 DE-LFER https://doi.org/10.1016/j.jum.2022.05.002 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true
allfieldsGer	10.1016/j.jum.2022.05.002 doi 10419/271473 hdl (DE-627)1818041456 (DE-599)KXP1818041456 DE-627 ger DE-627 rda eng Liyanage, Sohani verfasserin aut AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206 Abduljabbar, Rusul verfasserin aut Dia, Hussein verfasserin aut Tsai, Pei-Wei verfasserin aut Enthalten in Journal of urban management Amsterdam [u.a.] : Elsevier, 2012 11(2022), 3 vom: Sept., Seite 365-380 Online-Ressource (DE-627)837630150 (DE-600)2837330-3 (DE-576)446573256 2589-0360 nnns volume:11 year:2022 number:3 month:09 pages:365-380 https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true Verlag kostenfrei https://doi.org/10.1016/j.jum.2022.05.002 Resolving-System kostenfrei https://hdl.handle.net/10419/271473 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 11 2022 3 9 365-380 26 01 0206 4194515358 x1z 05-10-22 2403 01 DE-LFER 4208399864 00 --%%-- --%%-- n --%%-- l01 10-11-22 2403 01 DE-LFER https://doi.org/10.1016/j.jum.2022.05.002 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true
allfieldsSound	10.1016/j.jum.2022.05.002 doi 10419/271473 hdl (DE-627)1818041456 (DE-599)KXP1818041456 DE-627 ger DE-627 rda eng Liyanage, Sohani verfasserin aut AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206 Abduljabbar, Rusul verfasserin aut Dia, Hussein verfasserin aut Tsai, Pei-Wei verfasserin aut Enthalten in Journal of urban management Amsterdam [u.a.] : Elsevier, 2012 11(2022), 3 vom: Sept., Seite 365-380 Online-Ressource (DE-627)837630150 (DE-600)2837330-3 (DE-576)446573256 2589-0360 nnns volume:11 year:2022 number:3 month:09 pages:365-380 https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true Verlag kostenfrei https://doi.org/10.1016/j.jum.2022.05.002 Resolving-System kostenfrei https://hdl.handle.net/10419/271473 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 11 2022 3 9 365-380 26 01 0206 4194515358 x1z 05-10-22 2403 01 DE-LFER 4208399864 00 --%%-- --%%-- n --%%-- l01 10-11-22 2403 01 DE-LFER https://doi.org/10.1016/j.jum.2022.05.002 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true
language	English
source	Enthalten in Journal of urban management 11(2022), 3 vom: Sept., Seite 365-380 volume:11 year:2022 number:3 month:09 pages:365-380
sourceStr	Enthalten in Journal of urban management 11(2022), 3 vom: Sept., Seite 365-380 volume:11 year:2022 number:3 month:09 pages:365-380
format_phy_str_mv	Article
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institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Artificial intelligence Bus demand prediction Deep learning Neural networks On-demand public transport Short-term prediction
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container_title	Journal of urban management
authorswithroles_txt_mv	Liyanage, Sohani @@aut@@ Abduljabbar, Rusul @@aut@@ Dia, Hussein @@aut@@ Tsai, Pei-Wei @@aut@@
publishDateDaySort_date	2022-09-01T00:00:00Z
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spellingShingle	Liyanage, Sohani misc Artificial intelligence misc Bus demand prediction misc Deep learning misc Neural networks misc On-demand public transport misc Short-term prediction AI-based neural network models for bus passenger demand forecasting using smart card data
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topic_title	AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai Artificial intelligence (dpeaa)DE-206 Bus demand prediction (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Neural networks (dpeaa)DE-206 On-demand public transport (dpeaa)DE-206 Short-term prediction (dpeaa)DE-206
topic	misc Artificial intelligence misc Bus demand prediction misc Deep learning misc Neural networks misc On-demand public transport misc Short-term prediction
topic_unstemmed	misc Artificial intelligence misc Bus demand prediction misc Deep learning misc Neural networks misc On-demand public transport misc Short-term prediction
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title	AI-based neural network models for bus passenger demand forecasting using smart card data
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title_full	AI-based neural network models for bus passenger demand forecasting using smart card data Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai
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title_sort	ai-based neural network models for bus passenger demand forecasting using smart card data
title_auth	AI-based neural network models for bus passenger demand forecasting using smart card data
abstract	Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.
abstractGer	Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.
abstract_unstemmed	Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.
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title_short	AI-based neural network models for bus passenger demand forecasting using smart card data
url	https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?isDTMRedir=true&download=true https://doi.org/10.1016/j.jum.2022.05.002 https://hdl.handle.net/10419/271473
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1818041456</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230612151429.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221005s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jum.2022.05.002</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/271473</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1818041456</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1818041456</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liyanage, Sohani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">AI-based neural network models for bus passenger demand forecasting using smart card data</subfield><subfield code="c">Sohani Liyanage, Rusul Abduljabbar, Hussein Dia, Pei-Wei Tsai</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. 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