{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:52:15Z","timestamp":1775145135479,"version":"3.50.1"},"reference-count":140,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010238","name":"K2","doi-asserted-by":"publisher","award":["K2"],"award-info":[{"award-number":["K2"]}],"id":[{"id":"10.13039\/501100010238","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001858","name":"VINNOVA","doi-asserted-by":"publisher","award":["VINNOVA"],"award-info":[{"award-number":["VINNOVA"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005934","name":"Malm\u00f6 University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005934","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Public Transp"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.<\/jats:p>","DOI":"10.1007\/s12469-023-00334-7","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T21:03:09Z","timestamp":1700514189000},"page":"99-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Artificial intelligence for improving public transport: a mapping study"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6019-1182","authenticated-orcid":false,"given":"\u00c5.","family":"Jevinger","sequence":"first","affiliation":[]},{"given":"C.","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"J. A.","family":"Persson","sequence":"additional","affiliation":[]},{"given":"P.","family":"Davidsson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"issue":"1","key":"334_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/su11010189","volume":"11","author":"R Abduljabbar","year":"2019","unstructured":"Abduljabbar R, Dia H, Liyanage S, Bagloee SA (2019) Applications of artificial intelligence in transport: an overview. Sustainability 11(1):189, 1\u201324. https:\/\/doi.org\/10.3390\/su11010189","journal-title":"Sustainability"},{"key":"334_CR2","doi-asserted-by":"publisher","unstructured":"Adamson K, Campbell P, Orsoni A (2005) Hybrid Decision Support Based on Knowledge Discovery and AI Techniques for the Management of Maintenance Services in the Public Transport Sector. Proceedings of 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 674\u2013678. https:\/\/doi.org\/10.1109\/IDAACS.2005.283071","DOI":"10.1109\/IDAACS.2005.283071"},{"key":"334_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5827-6_12","volume-title":"Prediction of Ticket Prices for Public Transport Using Linear Regression and Random Forest Regression Methods: A Practical Approach Using Machine Learning","author":"AD Aditi","year":"2020","unstructured":"Aditi AD, Dureja A, Abrol S, Dureja A (2020) Prediction of Ticket Prices for Public Transport Using Linear Regression and Random Forest Regression Methods: A Practical Approach Using Machine Learning. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics, Springer, 140\u2013150. https:\/\/doi.org\/10.1007\/978-981-15-5827-6_12"},{"key":"334_CR4","doi-asserted-by":"publisher","unstructured":"Agafonov AA, Yumaganov AS (2019) Performance comparison of machine learning methods in the bus arrival time prediction problem. Proceedings of CEUR Workshop, 57\u201362. https:\/\/doi.org\/10.18287\/1613-0073-2019-2416-57-62","DOI":"10.18287\/1613-0073-2019-2416-57-62"},{"key":"334_CR5","doi-asserted-by":"publisher","unstructured":"Amrani A, Pasini K, Khouadjia M (2020) Enhance Journey Planner with Predictive Travel Information for Smart City Routing Services. 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), 304\u2013308.\u00a0https:\/\/doi.org\/10.1109\/FISTS46898.2020.9264859","DOI":"10.1109\/FISTS46898.2020.9264859"},{"key":"334_CR6","doi-asserted-by":"publisher","unstructured":"Ayman A, Wilbur M, Sivagnanam A, Pugliese P, Dubey A, Laszka A (2020) Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets. 2020 IEEE International Conference on Smart Computing, 41\u201348.\u00a0https:\/\/doi.org\/10.1109\/SMARTCOMP50058.2020.00026","DOI":"10.1109\/SMARTCOMP50058.2020.00026"},{"issue":"3","key":"334_CR7","doi-asserted-by":"publisher","first-page":"04016070","DOI":"10.1061\/(ASCE)CP.1943-5487.0000644","volume":"31","author":"H Bahuleyan","year":"2017","unstructured":"Bahuleyan H, Vanajakshi LD (2017) Arterial path-level travel-time estimation using machine-learning techniques. J Comput Civil Eng 31(3):04016070. https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000644","journal-title":"J Comput Civil Eng"},{"issue":"1","key":"334_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.joi.2006.08.001","volume":"1","author":"J Bar-Ilan","year":"2007","unstructured":"Bar-Ilan J, Levene M, Lin A (2007) Some measures for comparing citation databases. J Informetr 1(1):26\u201334. https:\/\/doi.org\/10.1016\/j.joi.2006.08.001","journal-title":"J Informetr"},{"key":"334_CR9","unstructured":"Barbosa R, Cardoso DO, Carvalho D, Fran\u00e7a FM (2017) A neuro-symbolic approach to GPS trajectory classification. Proceedings of European Symposium on Artificial Neural Networks, 411\u2013416"},{"issue":"3","key":"334_CR10","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1017\/S0269888913000118","volume":"29","author":"AL Bazzan","year":"2014","unstructured":"Bazzan AL, Kl\u00fcgl F (2014) A review on agent-based technology for traffic and transportation. Knowl Eng Rev 29(3):375. https:\/\/doi.org\/10.1017\/S0269888913000118","journal-title":"Knowl Eng Rev"},{"key":"334_CR11","doi-asserted-by":"publisher","unstructured":"Bei Y, Ge Y, Zhang D (2020) A machine learning based shared bikes scheduling method. Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing, 32\u201336. https:\/\/doi.org\/10.1145\/3416921.3416938","DOI":"10.1145\/3416921.3416938"},{"issue":"6","key":"334_CR12","doi-asserted-by":"publisher","first-page":"4526","DOI":"10.1109\/JIOT.2018.2834907","volume":"5","author":"N Belapurkar","year":"2018","unstructured":"Belapurkar N, Harbour J, Shelke S, Aksanli B (2018) Building Data-Aware and Energy-Efficient smart spaces. IEEE Internet of Things J 5(6):4526\u20134537. https:\/\/doi.org\/10.1109\/JIOT.2018.2834907","journal-title":"IEEE Internet of Things J"},{"issue":"12","key":"334_CR13","first-page":"2104","volume":"8","author":"R Bembalkar","year":"2019","unstructured":"Bembalkar R, Game P (2019) Infrastructure cost reduction of Municipal Public Transport using machine learning. Int J Sci Technol Res 8(12):2104\u201321074","journal-title":"Int J Sci Technol Res"},{"key":"334_CR14","doi-asserted-by":"publisher","first-page":"359","DOI":"10.2495\/UT120311","volume":"128","author":"A Berbey","year":"2012","unstructured":"Berbey A, Gal\u00e1n R, Bobi SJD, Caballero R (2012) A fuzzy logic approach to modelling the passengers\u2019 flow and dwelling time. WIT Trans Built Environ 128:359\u2013369. https:\/\/doi.org\/10.2495\/UT120311","journal-title":"WIT Trans Built Environ"},{"issue":"5","key":"334_CR15","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.3390\/e17052688","volume":"17","author":"A Berbey Alvarez","year":"2015","unstructured":"Berbey Alvarez A, Merchan F, Calvo Poyo FJ, Caballero George RJ (2015) A fuzzy logic-based Approach for Estimation of Dwelling Times of Panama Metro Stations. Entropy 17(5):2688\u20132705. https:\/\/doi.org\/10.3390\/e17052688","journal-title":"Entropy"},{"key":"334_CR16","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/978-3-642-40994-3_50","volume-title":"Machine learning and knowledge Discovery in Databases","author":"M Berlingerio","year":"2013","unstructured":"Berlingerio M, Calabrese F, Di Lorenzo G, Nair R, Pinelli F, Sbodio ML (2013) AllAboard: A System for Exploring Urban mobility and optimizing Public Transport using Cellphone Data. In: Blockeel H, Kersting K, Nijssen S, \u017delezn\u00fd F (eds) Machine learning and knowledge Discovery in Databases. Lecture Notes in Computer Science, vol 8190. Springer, Berlin, Heidelberg, pp 663\u2013666. https:\/\/doi.org\/10.1007\/978-3-642-40994-3_50"},{"key":"334_CR17","unstructured":"Biyani P (2019) To each route its own ETA: A generative modeling framework for ETA prediction. Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC). arXiv preprint arXiv:1906.09925"},{"key":"334_CR18","unstructured":"Blandin S, Wynter L, Poonawala H, Laguna S, Dura B (2019) FASTER: Fusion AnalyticS for public Transport Event Response. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 1404\u20131412"},{"key":"334_CR19","doi-asserted-by":"publisher","unstructured":"Bocchetti G, Flammini F, Pragliola C, Pappalardo A (2009) Dependable integrated surveillance systems for the physical security of metro railways. 2009 Third ACM\/IEEE International Conference on Distributed Smart Cameras (ICDSC), 1\u20137. https:\/\/doi.org\/10.1109\/ICDSC.2009.5289385","DOI":"10.1109\/ICDSC.2009.5289385"},{"key":"334_CR20","doi-asserted-by":"crossref","unstructured":"Borodinov AA, Myasnikov VV (2019) Analysis of the preferences of public transport passengers in the task of building a personalized recommender system. Proceedings of CEUR Workshop Proceedings. 198\u2013205","DOI":"10.18287\/1613-0073-2019-2391-198-205"},{"key":"334_CR21","doi-asserted-by":"publisher","unstructured":"Borodinov AA, Myasnikov VV (2020a) Evaluating classifiers to determine user-preferred stops in a personalized recommender system. Twelfth International Conference on Machine Vision (ICMV 2019), 11433, 114330\u00a0N. https:\/\/doi.org\/10.1117\/12.2556536","DOI":"10.1117\/12.2556536"},{"key":"334_CR22","doi-asserted-by":"publisher","unstructured":"Borodinov AA, Myasnikov VV (2020b) Method of Determining User Preferences for the Personalized Recommender Systems for Public Transport Passengers. International Conference on Analysis of Images, Social Networks and Texts, 341\u2013351. https:\/\/doi.org\/10.1007\/978-3-030-39575-9_34","DOI":"10.1007\/978-3-030-39575-9_34"},{"issue":"4","key":"334_CR23","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/bdcc4040036","volume":"4","author":"F Branda","year":"2020","unstructured":"Branda F, Marozzo F, Talia D (2020) Ticket sales prediction and dynamic pricing strategies in Public Transport. Big Data Cogn Comput 4(4):36. https:\/\/doi.org\/10.3390\/bdcc4040036","journal-title":"Big Data Cogn Comput"},{"key":"334_CR24","doi-asserted-by":"publisher","unstructured":"Cao X, Dong D, Zeng X (2011) Application of Agent in Bus Signal Priority Intersection. Proceedings of 2011 Tenth International Symposium on Autonomous Decentralized Systems, 276\u2013280. https:\/\/doi.org\/10.1109\/ISADS.2011.37","DOI":"10.1109\/ISADS.2011.37"},{"key":"334_CR25","first-page":"269","volume":"20","author":"CS Chang","year":"1996","unstructured":"Chang CS (1996) Re-engineering the Station management processes in Hong Kong Mass Transit Railway Corporation. WIT Trans Built Environ 20:269\u2013278. https:\/\/www.witpress.com\/elibrary\/wit-transactions-on-the-built-environment\/20\/8882","journal-title":"WIT Trans Built Environ"},{"issue":"4","key":"334_CR26","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1177\/0361198119839339","volume":"2673","author":"R Chapleau","year":"2019","unstructured":"Chapleau R, Gaudette P, Spurr T (2019) Application of machine learning to two large-sample Household travel surveys: a characterization of travel modes. Transp Res Rec 2673(4):173\u2013183. https:\/\/doi.org\/10.1177\/0361198119839339","journal-title":"Transp Res Rec"},{"issue":"2","key":"334_CR27","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TITS.2010.2048313","volume":"11","author":"B Chen","year":"2010","unstructured":"Chen B, Cheng HH (2010) A review of the applications of agent technology in traffic and transportation systems. IEEE Trans Intell Transp Syst 11(2):485\u2013497. https:\/\/doi.org\/10.1109\/TITS.2010.2048313","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"334_CR28","unstructured":"Claiborne J, Gupta A (2018) Machine Learning Classifiers for Predicting Transit Fraud. Proceedings of AMCIS 2018. https:\/\/aisel.aisnet.org\/amcis2018\/DataScience\/Presentations\/37"},{"key":"334_CR29","first-page":"1","volume":"22","author":"L Cui","year":"2020","unstructured":"Cui L, Su D, Zhou Y, Zhang L, Wu Y, Chen S (2020) Edge learning for surveillance video uploading sharing in public transport systems. IEEE Trans Intell Transp Syst 22:1\u201310","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"334_CR30","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.trc.2005.07.002","volume":"13","author":"P Davidsson","year":"2005","unstructured":"Davidsson P, Henesey L, Ramstedt L, T\u00f6rnquist J, Wernstedt F (2005) An analysis of agent-based approaches to transport logistics. Transp Res Part C: Emerg Technol 13(4):255\u2013271. https:\/\/doi.org\/10.1016\/j.trc.2005.07.002","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"12","key":"334_CR31","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.3390\/su8121248","volume":"8","author":"P Davidsson","year":"2016","unstructured":"Davidsson P, Hajinasab B, Holmgren J, Jevinger \u00c3, Persson JA (2016) The Fourth Wave of Digitalization and Public Transport: Opportunities and Challenges. Sustainability 8(12):1248. https:\/\/doi.org\/10.3390\/su8121248","journal-title":"Sustainability"},{"key":"334_CR32","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s12469-020-00251-z","volume":"13","author":"V Degeler","year":"2020","unstructured":"Degeler V, Heydenrijk-Ottens L, Luo D, van Oort N, van Lint H (2020) Unsupervised approach towards analysing the public transport bunching swings formation phenomenon. Public Transp 13:533\u2013555. https:\/\/doi.org\/10.1007\/s12469-020-00251-z","journal-title":"Public Transp"},{"issue":"4","key":"334_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1177\/0361198119837153","volume":"2673","author":"Y Deng","year":"2019","unstructured":"Deng Y, Yan Y (2019) Propensity score weighting with generalized boosted Models to explore the Effects of the built environment and residential self-selection on travel behavior. Transp Res Rec 2673(4):373\u2013383. https:\/\/doi.org\/10.1177\/0361198119837153","journal-title":"Transp Res Rec"},{"issue":"1","key":"334_CR34","doi-asserted-by":"publisher","first-page":"15841","DOI":"10.1016\/j.ifacol.2017.08.2324","volume":"50","author":"V Dimanche","year":"2017","unstructured":"Dimanche V, Goupil A, Philippot A, Riera B, Urban A, Gabriel G (2017) Massive Railway Operating Data Visualization; a Tool for RATP Operating Expert. IFAC-PapersOnLine 50(1):15841\u201315846. https:\/\/doi.org\/10.1016\/j.ifacol.2017.08.2324","journal-title":"IFAC-PapersOnLine"},{"issue":"5","key":"334_CR35","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1080\/08839514.2019.1582859","volume":"33","author":"F Elizalde-Ram\u00edrez","year":"2019","unstructured":"Elizalde-Ram\u00edrez F, Nigenda RS, Mart\u00ednez-Salazar IA, R\u00edos-Sol\u00eds Y (2019) Travel plans in Public Transit Networks using Artificial Intelligence Planning Models. Appl Artif Intell 33(5):440\u2013461. https:\/\/doi.org\/10.1080\/08839514.2019.1582859","journal-title":"Appl Artif Intell"},{"key":"334_CR36","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.trpro.2018.12.210","volume":"37","author":"M Ferrara","year":"2019","unstructured":"Ferrara M, Liberto C, Nigro M, Trojani M, Valenti G (2019) Multimodal choice model for e-mobility scenarios. Transp Res Procedia 37:409\u2013416. https:\/\/doi.org\/10.1016\/j.trpro.2018.12.210","journal-title":"Transp Res Procedia"},{"issue":"20","key":"334_CR37","doi-asserted-by":"publisher","first-page":"11450","DOI":"10.3390\/su132011450","volume":"13","author":"L Ge","year":"2021","unstructured":"Ge L, Sarhani M, Vo\u00df S, Xie L (2021) Review of transit data sources: Potentials, challenges and complementarity. Sustainability 13(20):11450. https:\/\/doi.org\/10.3390\/su132011450","journal-title":"Sustainability"},{"key":"334_CR38","doi-asserted-by":"publisher","unstructured":"Genser A, Amb\u00fchl L, Yang K, Menendez M, Kouvelas A (2020) Time-to-Green predictions: A framework to enhance SPaT messages using machine learning. Paper presented at the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1\u20136,\u00a0https:\/\/doi.org\/10.1109\/ITSC45102.2020.9294548","DOI":"10.1109\/ITSC45102.2020.9294548"},{"key":"334_CR39","doi-asserted-by":"crossref","unstructured":"Ghaemi MS, Agard B, Nia VP, Tr\u00e9panier M (2015) Challenges in spatial-temporal data analysis targeting public transport. Proceedings of Symposium on Information Control in Manufacturing, 442\u2013447","DOI":"10.1016\/j.ifacol.2015.06.121"},{"key":"334_CR40","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.procs.2016.11.026","volume":"101","author":"A Golubev","year":"2016","unstructured":"Golubev A, Chechetkin I, Parygin D, Sokolov A, Shcherbakov M (2016) Geospatial Data Generation and Preprocessing Tools for Urban Computing System Development1. Procedia Comput Sci 101:217\u2013226. https:\/\/doi.org\/10.1016\/j.procs.2016.11.026","journal-title":"Procedia Comput Sci"},{"key":"334_CR41","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.neucom.2019.08.100","volume":"391","author":"M Grzenda","year":"2020","unstructured":"Grzenda M, Kwasiborska K, Zaremba T (2020) Hybrid short term prediction to address limited timeliness of public transport data streams. Neurocomputing 391:305\u2013317. https:\/\/doi.org\/10.1016\/j.neucom.2019.08.100","journal-title":"Neurocomputing"},{"key":"334_CR42","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.eswa.2017.01.057","volume":"78","author":"J Hagenauer","year":"2017","unstructured":"Hagenauer J, Helbich M (2017) A comparative study of machine learning classifiers for modeling travel mode choice. Expert Syst Appl 78:273\u2013282. https:\/\/doi.org\/10.1016\/j.eswa.2017.01.057","journal-title":"Expert Syst Appl"},{"issue":"1","key":"334_CR44","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s11042-019-08167-y","volume":"79","author":"EU Haq","year":"2020","unstructured":"Haq EU, Huarong X, Xuhui C, Wanqing Z, Jianping F, Abid F (2020) A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera. Multimed Tools Appl 79(1):1007\u20131036. https:\/\/doi.org\/10.1007\/s11042-019-08167-y","journal-title":"Multimed Tools Appl"},{"key":"334_CR45","doi-asserted-by":"publisher","unstructured":"Heghedus C (2017) PhD Forum: Forecasting Public Transit Using Neural Network Models. 2017 IEEE International Conference on Smart Computing (SMARTCOMP), 1\u20132. https:\/\/doi.org\/10.1109\/SMARTCOMP.2017.7947031","DOI":"10.1109\/SMARTCOMP.2017.7947031"},{"key":"334_CR46","doi-asserted-by":"publisher","unstructured":"Heghedus C, Chakravorty A, Rong C (2019) Neural Network Frameworks. Comparison on Public Transportation Prediction. 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 842\u2013849. https:\/\/doi.org\/10.1109\/IPDPSW.2019.00138","DOI":"10.1109\/IPDPSW.2019.00138"},{"key":"334_CR47","doi-asserted-by":"publisher","unstructured":"Herrmann P, Puka E, Skoglund TR (2020) Machine Learning-based Update-time Prediction for Battery-friendly Passenger Information Displays. 2020 IEEE 8th International Conference on Smart City and Informatization (iSCI), 49\u201359.\u00a0https:\/\/doi.org\/10.1109\/iSCI50694.2020.00016","DOI":"10.1109\/iSCI50694.2020.00016"},{"key":"334_CR48","doi-asserted-by":"publisher","unstructured":"Holzinger A, Kieseberg P, Weippl E, Tjoa AM (2018) Current advances, trends and challenges of machine learning and knowledge extraction: From machine learning to explainable AI. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 1\u20138. https:\/\/doi.org\/10.1007\/978-3-319-99740-7_1","DOI":"10.1007\/978-3-319-99740-7_1"},{"issue":"10","key":"334_CR49","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/2500892","volume":"56","author":"A Hoonlor","year":"2013","unstructured":"Hoonlor A, Szymanski BK, Zaki MJ (2013) Trends in computer science research. Commun ACM 56(10):74\u201383. https:\/\/doi.org\/10.1145\/2500892","journal-title":"Commun ACM"},{"key":"334_CR50","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.landusepol.2016.06.004","volume":"57","author":"N Hu","year":"2016","unstructured":"Hu N, Legara EF, Lee KK, Hung GG, Monterola C (2016) Impacts of land use and amenities on public transport use, urban planning and design. Land Use Policy 57:356\u2013367. https:\/\/doi.org\/10.1016\/j.landusepol.2016.06.004","journal-title":"Land Use Policy"},{"issue":"6","key":"334_CR51","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1049\/iet-its.2016.0276","volume":"11","author":"J Jung","year":"2017","unstructured":"Jung J, Sohn K (2017) Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. IET Intel Transp Syst 11(6):334\u2013339. https:\/\/doi.org\/10.1049\/iet-its.2016.0276","journal-title":"IET Intel Transp Syst"},{"issue":"1","key":"334_CR52","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s40890-017-0038-9","volume":"3","author":"AS Kedia","year":"2017","unstructured":"Kedia AS, Sowjanya D, Salini PS, Jabeena M, Katti BK (2017) Transit shift response analysis through fuzzy rule based-choice model: a case study of indian Metropolitan City. Transp Dev Econ 3(1):8. https:\/\/doi.org\/10.1007\/s40890-017-0038-9","journal-title":"Transp Dev Econ"},{"key":"334_CR53","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.procs.2019.04.184","volume":"151","author":"P Killeen","year":"2019","unstructured":"Killeen P, Ding B, Kiringa I, Yeap T (2019) IoT-based predictive maintenance for fleet management. Procedia Comput Sci 151:607\u2013613","journal-title":"Procedia Comput Sci"},{"key":"334_CR54","unstructured":"Kitchenham B, Charters SM (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report, Ver. 2.3 EBSE"},{"issue":"3","key":"334_CR55","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1080\/01441647.2019.1704307","volume":"40","author":"AN Koushik","year":"2020","unstructured":"Koushik AN, Manoj M, Nezamuddin N (2020) Machine learning applications in activity-travel behaviour research: a review. Transp Rev 40(3):288\u2013311. https:\/\/doi.org\/10.1080\/01441647.2019.1704307","journal-title":"Transp Rev"},{"issue":"3","key":"334_CR56","first-page":"948","volume":"11","author":"S Kuberkar","year":"2020","unstructured":"Kuberkar S, Singhal TK (2020) Factors influencing adoption intention of AI powered chatbot for public transport services within a smart city. Int J Emerg Technol 11(3):948\u2013958","journal-title":"Int J Emerg Technol"},{"key":"334_CR57","doi-asserted-by":"publisher","unstructured":"Kulkarni G, Abellera L, Panangadan A (2018) Unsupervised classification of online community input to advance transportation services. Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 261\u2013267. https:\/\/doi.org\/10.1109\/CCWC.2018.8301704","DOI":"10.1109\/CCWC.2018.8301704"},{"key":"334_CR58","unstructured":"Kumar V, Kumar BA, Vanajakshi LD, Subramanian SC (2014) Comparison of Model Based and Machine Learning Approaches for Bus Arrival Time Prediction. Presented at the Transportation Research Board 93rd Annual Meeting Transportation Research Board, 14-2518."},{"key":"334_CR59","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/978-981-13-0869-7_28","volume-title":"Big Data Analysis and Deep Learning Applications AISC","author":"T Kyaw","year":"2019","unstructured":"Kyaw T, Oo NN, Zaw W (2019) Building Travel Speed Estimation Model for Yangon City from Public Transport Trajectory Data. In: Zin TT, Lin JC-W (eds) Big Data Analysis and Deep Learning Applications AISC. Springer, Berlin, pp 250\u2013257. https:\/\/doi.org\/10.1007\/978-981-13-0869-7_28"},{"key":"334_CR60","unstructured":"Lavesson N, Davidsson P (2006) Quantifying the impact of learning algorithm parameter tuning. The 21th National Conference on Artificial Intelligence (AAAI), Vol.\u00a01, 395\u2013400"},{"key":"334_CR61","doi-asserted-by":"publisher","unstructured":"Lazar A, Ballow A, Jin L, Spurlock CA, Sim A, Wu K (2019) Machine Learning for Prediction of Mid to Long Term Habitual Transportation Mode Use. Proceedings of 2019 IEEE International Conference on Big Data (Big Data), 4520\u20134524. https:\/\/doi.org\/10.1109\/BigData47090.2019.9006411","DOI":"10.1109\/BigData47090.2019.9006411"},{"key":"334_CR62","doi-asserted-by":"publisher","unstructured":"Lepr\u00eatre F, Fonlupt C, Verel S, Marion V (2019) Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem. International Conference on Artificial Evolution (Evolution Artificielle), 42\u201352. https:\/\/doi.org\/10.1007\/978-3-030-45715-0_4","DOI":"10.1007\/978-3-030-45715-0_4"},{"key":"334_CR63","doi-asserted-by":"publisher","unstructured":"Leung CK, Elias JD, Minuk SM, de Jesus ARR, Cuzzocrea A (2020) An Innovative Fuzzy Logic-Based Machine Learning Algorithm for Supporting Predictive Analytics on Big Transportation Data. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1\u20138. https:\/\/doi.org\/10.1109\/FUZZ48607.2020.9177823","DOI":"10.1109\/FUZZ48607.2020.9177823"},{"key":"334_CR64","doi-asserted-by":"publisher","unstructured":"Li T, Fong S, Yang L (2018a) Counting Passengers in Public Buses by Sensing Carbon Dioxide Concentration: Data Collection and Machine Learning. Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things, 43\u201348. https:\/\/doi.org\/10.1145\/3289430.3289461","DOI":"10.1145\/3289430.3289461"},{"issue":"1","key":"334_CR65","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/info9010018","volume":"9","author":"T Li","year":"2018","unstructured":"Li T, Sun D, Jing P, Yang K (2018b) Smart card data mining of public transport destination: a literature review. Information 9(1):18. https:\/\/doi.org\/10.3390\/info9010018","journal-title":"Information"},{"key":"334_CR66","first-page":"70","volume":"1\u201316","author":"L Liang","year":"2019","unstructured":"Liang L, Xu M, Grant-Muller S, Mussone L (2019) Household travel mode choice estimation with large-scale data\u2014An empirical analysis based on mobility data in Milan. Int J Sustain Transp 1\u201316:70","journal-title":"Int J Sustain Transp"},{"issue":"4","key":"334_CR67","doi-asserted-by":"publisher","first-page":"911","DOI":"10.3233\/IDA-173443","volume":"22","author":"F Lin","year":"2018","unstructured":"Lin F, Jiang J, Fan J, Wang S (2018) A stacking model for variation prediction of public bicycle traffic flow. Intell Data Anal 22(4):911\u2013933. https:\/\/doi.org\/10.3233\/IDA-173443","journal-title":"Intell Data Anal"},{"key":"334_CR68","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.3233\/JIFS-189307","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu Q, Huang Z (2020) Research on intelligent prevention and control of COVID-19 in China\u2019s urban rail transit based on artificial intelligence and big data. J Intell Fuzzy Syst 39:9085\u20139090","journal-title":"J Intell Fuzzy Syst"},{"key":"334_CR69","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6625435","author":"W Liu","year":"2020","unstructured":"Liu W, Tan Q, Wu W, Abulkasim H (2020) Forecast and early warning of regional bus passenger flow based on machine learning. Math Probl Eng. https:\/\/doi.org\/10.1155\/2020\/6625435","journal-title":"Math Probl Eng"},{"issue":"5","key":"334_CR70","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.3390\/su11051262","volume":"11","author":"S Liyanage","year":"2019","unstructured":"Liyanage S, Dia H, Abduljabbar R, Bagloee SA (2019) Flexible mobility on-demand: an environmental scan. Sustainability 11(5):1262. https:\/\/doi.org\/10.3390\/su11051262","journal-title":"Sustainability"},{"issue":"4","key":"334_CR71","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1080\/10095020.2020.1815596","volume":"23","author":"O Lock","year":"2020","unstructured":"Lock O, Pettit C (2020) Social media as passive geo-participation in transportation planning \u2013 how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Geo-spatial Inform Sci 23(4):275\u2013292. https:\/\/doi.org\/10.1080\/10095020.2020.1815596","journal-title":"Geo-spatial Inform Sci"},{"key":"334_CR72","unstructured":"Mackett RL (1994) Determining appropriate public transport system for a city. Transportation Research Record, 44\u201344. Retrieved 09.30.2020, from http:\/\/onlinepubs.trb.org\/Onlinepubs\/trr\/1994\/1451\/1451.pdf#page=50"},{"key":"334_CR73","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-1-4757-2475-2_15","volume-title":"Transport, Land-Use and the Environment","author":"RL Mackett","year":"1996","unstructured":"Mackett RL (1996) Modelling the implications of new public transport technology: an approach using artificial intelligence. In: Hayashi Y, Roy J (eds) Transport, Land-Use and the Environment. Springer, US, pp 297\u2013315"},{"key":"334_CR74","volume-title":"Suggesting alternate traffic mode and cost optimization on traffic-related impacts using machine learning techniques.\u00a0intelligent computing in engineering","author":"MS Manivannan","year":"2020","unstructured":"Manivannan MS, Kavitha R, Srikanth R, Narayanan V (2020) Suggesting alternate traffic mode and cost optimization on traffic-related impacts using machine learning techniques.\u00a0intelligent computing in engineering. Springer, Singapore"},{"key":"334_CR75","unstructured":"Market Research Future (2021) Public Transport Market Research Report: Information by Type (Bus, Light Rail, Regional Taxi, Metro and Tram), Application (City and Rural) and Region - Forecast till 2027. Report ID: MRFR\/AM\/7205-CR"},{"key":"334_CR76","unstructured":"Markets and Markets (2019) Railway System Market by System Type, Transit Type, Application & Region - Global Forecast to 2025. Report ID: 4763771"},{"key":"334_CR77","doi-asserted-by":"publisher","first-page":"3780","DOI":"10.1016\/j.procs.2020.09.009","volume":"176","author":"MW Mastalerz","year":"2020","unstructured":"Mastalerz MW, Malinowski A, Kwiatkowski S, \u015aniegula A, Wieczorek B (2020) Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems. Procedia Comput Sci 176:3780\u20133793. https:\/\/doi.org\/10.1016\/j.procs.2020.09.009","journal-title":"Procedia Comput Sci"},{"key":"334_CR78","doi-asserted-by":"publisher","first-page":"101401","DOI":"10.1016\/j.compenvurbsys.2019.101401","volume":"78","author":"JR Mayaud","year":"2019","unstructured":"Mayaud JR, Tran M, Nuttall R (2019) An urban data framework for assessing equity in cities: comparing accessibility to healthcare facilities in Cascadia. Comput Environ Urban Syst 78:101401. https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101401","journal-title":"Comput Environ Urban Syst"},{"key":"334_CR001","unstructured":"McCarthy J (1998) What is artificial intelligence? Technical Report. Stanford University"},{"key":"334_CR80","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1609\/aimag.v27i4.1904","volume":"27","author":"J McCarthy","year":"2006","unstructured":"McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag 27:12\u201312. https:\/\/doi.org\/10.1609\/aimag.v27i4.1904","journal-title":"AI Mag"},{"key":"334_CR81","doi-asserted-by":"publisher","unstructured":"Minea M, Dumitrescu C, Chiva I-C, Artificial Intelligence (2019) Unconventional Public Transport Anonymous Data Collection employing. 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1\u20136. https:\/\/doi.org\/10.1109\/ECAI46879.2019.9041957","DOI":"10.1109\/ECAI46879.2019.9041957"},{"key":"334_CR82","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/11538356_21","volume-title":"Advances in Intelligent Computing, LNCS","author":"M Molina","year":"2005","unstructured":"Molina M (2005) An Intelligent Assistant for Public Transport Management. In: Huang D-S, Zhang X-P, Huang G-B (eds) Advances in Intelligent Computing, LNCS. Springer, Berlin, pp 199\u2013208"},{"key":"334_CR83","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.asoc.2016.06.031","volume":"47","author":"L Moreira-Matias","year":"2016","unstructured":"Moreira-Matias L, Cats O, Gama J, Mendes-Moreira J, de Sousa JF (2016) An online learning approach to eliminate Bus bunching in real-time. Appl Soft Comput 47:460\u2013482. https:\/\/doi.org\/10.1016\/j.asoc.2016.06.031","journal-title":"Appl Soft Comput"},{"key":"334_CR84","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.jtrangeo.2017.05.014","volume":"62","author":"B Moya-G\u00f3mez","year":"2017","unstructured":"Moya-G\u00f3mez B, Garc\u00eda-Palomares JC (2017) The impacts of congestion on automobile accessibility. What happens in large european cities? J Transp Geogr 62:148\u2013159. https:\/\/doi.org\/10.1016\/j.jtrangeo.2017.05.014","journal-title":"J Transp Geogr"},{"issue":"1","key":"334_CR85","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/0377-2217(94)E0349-G","volume":"83","author":"K Nachtigall","year":"1995","unstructured":"Nachtigall K (1995) Time depending shortest-path problems with applications to railway networks. Eur J Oper Res 83(1):154\u2013166. https:\/\/doi.org\/10.1016\/0377-2217(94)E0349-G","journal-title":"Eur J Oper Res"},{"key":"334_CR86","doi-asserted-by":"publisher","first-page":"5287","DOI":"10.1007\/s00521-020-05318-3","volume":"33","author":"T Nguyen","year":"2021","unstructured":"Nguyen T, Nguyen-Phuoc DQ, Wong YD (2021) Developing artificial neural networks to estimate real-time onboard bus ride comfort. Neural Comput Appl 33:5287\u20135299. https:\/\/doi.org\/10.1007\/s00521-020-05318-3","journal-title":"Neural Comput Appl"},{"issue":"3","key":"334_CR87","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/urbansci4030036","volume":"4","author":"U Niklas","year":"2020","unstructured":"Niklas U, von Behren S, Soylu T, Kopp J, Chlond B, Vortisch P (2020) Spatial factor\u2014using a Random Forest classification model to measure an internationally comparable urbanity index. Urban Sci 4(3):36. https:\/\/doi.org\/10.3390\/urbansci4030036","journal-title":"Urban Sci"},{"key":"334_CR88","unstructured":"Olczyk A, Galuszk A (2017) Cloud-based machine learning for bus arrival time prediction. Proceedings of Carpathian Logistic Congress, 173\u2013177"},{"key":"334_CR89","doi-asserted-by":"publisher","unstructured":"Othman MSB, Tan G (2018) Machine learning aided simulation of public transport utilization. 2018 IEEE\/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 1\u20132. https:\/\/doi.org\/10.1109\/DISTRA.2018.8601011","DOI":"10.1109\/DISTRA.2018.8601011"},{"key":"334_CR90","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.35940\/ijeat.A2636.109119","volume":"9","author":"N Othman","year":"2019","unstructured":"Othman N, Hussin M, Mahmood RAR (2019) Sentiment evaluation of Public Transport in Social Media using Na\u00efve Bayes Method. Int J Eng Adv Technol 9:2305\u20132308","journal-title":"Int J Eng Adv Technol"},{"key":"334_CR91","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3165303","author":"SM Palacio","year":"2018","unstructured":"Palacio SM (2018) Machine Learning Forecasts of Public Transport Demand: A Comparative Analysis of Supervised Algorithms Using Smart Card Data. XREAP WP. https:\/\/doi.org\/10.2139\/ssrn.3165303","journal-title":"XREAP WP"},{"key":"334_CR92","doi-asserted-by":"publisher","unstructured":"Paletta L, Wiesenhofer S, Brandle N, Sidla O, Lypetskyy Y (2005) Visual surveillance system for monitoring of passenger flows at public transportation junctions. Proceedings of 2005 IEEE Intelligent Transportation Systems, 2005, 862\u2013867. https:\/\/doi.org\/10.1109\/ITSC.2005.1520163","DOI":"10.1109\/ITSC.2005.1520163"},{"key":"334_CR93","doi-asserted-by":"publisher","unstructured":"Pandurangi A, Byrne C, Anderson C, Cui E, McArdle G (2020) Design and development of an application for predicting bus travel times using a segmentation approach. In: Proceedings of the 6th international conference on geographical information systems theory, applications and management (GISTAM), pp 72\u201380. https:\/\/doi.org\/10.5220\/0009393800720080","DOI":"10.5220\/0009393800720080"},{"key":"334_CR94","unstructured":"Pasini K, Khouadjia M, Same A, Ganansia F, Oukhellou L (2019) LSTM encoder-predictor for short-term train load forecasting. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 535\u2013551. https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-46133-1_32"},{"key":"334_CR95","doi-asserted-by":"publisher","unstructured":"Prashanth TL, Tamilselvan AK, Chandrodaya S (2016) Multimodal transport model: Enhancing collaboration among mobility sharing schemes by identifying an optimal transit station. 2016 International Conference on Internet of Things and Applications (IOTA), 286\u2013291. https:\/\/doi.org\/10.1109\/IOTA.2016.7562739","DOI":"10.1109\/IOTA.2016.7562739"},{"key":"334_CR96","volume-title":"How AI boosts industry profits and innovation","author":"M Purdy","year":"2017","unstructured":"Purdy M, Daugherty P (2017) How AI boosts industry profits and innovation. Accenture Ltd, Dublin, Ireland"},{"issue":"2","key":"334_CR97","doi-asserted-by":"publisher","first-page":"791","DOI":"10.11591\/ijeecs.v11.i2.pp791-796","volume":"11","author":"SP Raflesia","year":"2018","unstructured":"Raflesia SP, Lestarini D, Rodiah D, Firdaus,  (2018) Opinion mining using machine learning approach: case study of light rail transit development in Indonesia. Indones J Electr Eng Comput Sci 11(2):791\u2013796. https:\/\/doi.org\/10.11591\/ijeecs.v11.i2.pp791-796","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"334_CR98","first-page":"229","volume":"21","author":"MM Rahimi","year":"2020","unstructured":"Rahimi MM, Naghizade E, Stevenson M, Winter S (2020) Service quality monitoring in confined spaces through mining Twitter data. J Spat Inform Sci 21:229\u2013261.","journal-title":"J Spat Inform Sci"},{"key":"334_CR99","doi-asserted-by":"crossref","unstructured":"Reddy KK, Kumar BA, Vanajakshi L (2016) Bus travel time prediction under high variability conditions. Curr Sci, 700\u2013711. Retrieved 05, 2019, from http:\/\/www.jstor.org\/stable\/24908545","DOI":"10.18520\/cs\/v111\/i4\/700-711"},{"key":"334_CR100","doi-asserted-by":"crossref","unstructured":"Rohit MH, Computer Vision (2020) An IoT based System for Public Transport Surveillance using real-time Data Analysis and Computer Vision. 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC), 1\u20136. 10.1109\/ICAECC50550.2020.9339485","DOI":"10.1109\/ICAECC50550.2020.9339485"},{"key":"334_CR101","unstructured":"Roulland F, Ulloa L, Mondragon A, Niemaz M, Bouchard G, Ciriza V (2014) Learning mobility user choice and demand models from public transport fare collection data, 1\u20135. 21st World Congress on Intelligent Transport Systems, ITSWC"},{"key":"334_CR102","volume-title":"Artificial intelligence-a modern approach,\u00a0third international edition","author":"SJ Russell","year":"2010","unstructured":"Russell SJ, Norvig P (2010) Artificial intelligence-a modern approach,\u00a0third international edition. Pearson Education London, London"},{"issue":"11","key":"334_CR103","first-page":"604","volume":"36","author":"G Scemama","year":"1995","unstructured":"Scemama G (1995) CLAIRE: an independent, AI-based supervisor for congestion management. Traffic Eng Control 36(11):604\u2013612","journal-title":"Traffic Eng Control"},{"key":"334_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2478\/ttt-2019-0005","volume":"15","author":"N Shakeel","year":"2019","unstructured":"Shakeel N, Baig F, Saddiq MA (2019) Modeling Commuter\u2019s sociodemographic characteristics to predict public transport usage frequency by applying supervised machine learning method. Transp Tech Technol 15:1\u20137. https:\/\/doi.org\/10.2478\/ttt-2019-0005","journal-title":"Transp Tech Technol"},{"key":"334_CR105","doi-asserted-by":"publisher","unstructured":"Shalit N, Fire M, Ben-Elia E (2020) Imputation of Missing Boarding Stop Information in Smart Card Data with Machine Learning Methods. Intelligent Data Engineering and Automated Learning-IDEAL 2020, 17\u201327. https:\/\/doi.org\/10.1007\/978-3-030-62362-3_3","DOI":"10.1007\/978-3-030-62362-3_3"},{"key":"334_CR106","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-981-13-6577-5_48","volume-title":"Advances in interdisciplinary Engineering,\u00a0LMNE","author":"SK Sharma","year":"2019","unstructured":"Sharma SK, Sharma RC (2019) Pothole detection and warning system for Indian roads. In: Kumar M, Pandey RK, Kumar V (eds) Advances in interdisciplinary Engineering,\u00a0LMNE. Springer, Singapore, pp 511\u2013519"},{"key":"334_CR107","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.promfg.2020.02.204","volume":"44","author":"N Shatnawi","year":"2020","unstructured":"Shatnawi N, Al-Omari AA, Al-Qudah H (2020) Optimization of Bus stops locations using GIS techniques and Artificial Intelligence. Procedia Manuf 44:52\u201359. https:\/\/doi.org\/10.1016\/j.promfg.2020.02.204","journal-title":"Procedia Manuf"},{"key":"334_CR108","doi-asserted-by":"crossref","unstructured":"Singla A, Santoni M, Bart\u00f3k G, Mukerji P, Meenen M, Krause A (2015) Incentivizing Users for Balancing Bike Sharing Systems, 723\u2013729, Twenty-Ninth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v29i1.9251"},{"key":"334_CR109","doi-asserted-by":"crossref","unstructured":"Skhosana M, Ezugwu A, Rana N, Abdulhamid SI (2020) An Intelligent Machine Learning-Based Real-Time Public Transport System. Lecture Notes in Computer Science, 649\u2013665, International Conference on Computational Science and Its Applications, 20th International Conference","DOI":"10.1007\/978-3-030-58817-5_47"},{"issue":"11","key":"334_CR110","first-page":"16","volume":"16","author":"M Song","year":"2015","unstructured":"Song M, Weng X, Yao S, He Q (2015) Path selection of urban public transportation based on artificial intelligence ant colony algorithm. Int J Simul-Syst Sci Technol 16(11):16","journal-title":"Int J Simul-Syst Sci Technol"},{"key":"334_CR111","doi-asserted-by":"crossref","unstructured":"Sosnowska J, Skibski O (2018) Path Evaluation and Centralities in Weighted Graphs-An Axiomatic Approach. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 3856\u20133862","DOI":"10.24963\/ijcai.2018\/536"},{"key":"334_CR112","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/9693272","volume":"2018","author":"S Sun","year":"2018","unstructured":"Sun S, Yang D (2018) Identifying public transit commuters based on both the smartcard data and survey data: a case study in Xiamen, China. J Adv Transp 2018:9693272. https:\/\/doi.org\/10.1155\/2018\/9693272","journal-title":"J Adv Transp"},{"key":"334_CR113","doi-asserted-by":"publisher","unstructured":"Sykes J-D, Fleur RS, Norkulov D, Dong Z, Amineh RK (2019) Conscious GPS: A System to Aid the Visually Impaired to Navigate Public Transportation. 2019 IEEE 40th Sarnoff Symposium, 1\u20136. https:\/\/doi.org\/10.1109\/Sarnoff47838.2019.9067826","DOI":"10.1109\/Sarnoff47838.2019.9067826"},{"key":"334_CR114","doi-asserted-by":"publisher","unstructured":"Tan D, Wang J, Liu H, Wang X (2011) The optimization of bus scheduling based on genetic algorithm. Proceedings of 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), 1530\u20131533. https:\/\/doi.org\/10.1109\/TMEE.2011.6199499","DOI":"10.1109\/TMEE.2011.6199499"},{"key":"334_CR115","doi-asserted-by":"publisher","first-page":"101927","DOI":"10.1016\/j.scs.2019.101927","volume":"53","author":"T Tang","year":"2020","unstructured":"Tang T, Liu R, Choudhury C (2020) Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data. Sustain Cities Soc 53:101927. https:\/\/doi.org\/10.1016\/j.scs.2019.101927","journal-title":"Sustain Cities Soc"},{"issue":"4","key":"334_CR116","first-page":"921","volume":"36","author":"S Tekin","year":"2018","unstructured":"Tekin S, K\u00f6fteci S, Aydin MM, Yildirim MS (2018) Trip optimization for public transportation systems with linear goal programming (LGP) method. Sigma: J Eng Nat Sci\/M\u00fchendislik ve Fen Bilimleri Dergisi 36(4):921\u2013933","journal-title":"Sigma: J Eng Nat Sci\/M\u00fchendislik ve Fen Bilimleri Dergisi"},{"key":"334_CR117","doi-asserted-by":"publisher","unstructured":"Toqu\u00e9 F, Khouadjia M, Come E, Trepanier M, Oukhellou L (2017) Short & long term forecasting of multimodal transport passenger flows with machine learning methods. Presented at the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp.\u00a0560\u2013566. https:\/\/doi.org\/10.1109\/ITSC.2017.8317939","DOI":"10.1109\/ITSC.2017.8317939"},{"key":"334_CR118","doi-asserted-by":"publisher","unstructured":"Tran L, Mun M, Lim M, Yamato J, Huh N, Shahabi C (2020) DeepTRANS: a deep learning system for public bus travel time estimation using traffic forecasting. Proceedings of the VLDB Endowment, 13, 2957\u20132960. doi: https:\/\/doi.org\/10.14778\/3415478.3415518","DOI":"10.14778\/3415478.3415518"},{"key":"334_CR119","doi-asserted-by":"crossref","unstructured":"Tu Q, Weng J-C, Yuan R-L (2016) Impact analysis of public transport fare adjustment on travel mode choice for travelers in Beijing. 16th COTA International Conference of Transportation Professional, 850\u2013863","DOI":"10.1061\/9780784479896.078"},{"issue":"5","key":"334_CR120","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/S1361-9209(01)00027-X","volume":"7","author":"B Ubbels","year":"2002","unstructured":"Ubbels B, Nijkamp P (2002) Unconventional funding of urban public transport. Transp Res Part D: Transp Environ 7(5):317\u2013329. https:\/\/doi.org\/10.1016\/S1361-9209(01)00027-X","journal-title":"Transp Res Part D: Transp Environ"},{"key":"334_CR121","unstructured":"UITP Asia Pacific Centre for Transport Excellence CTE (2020) Artificial Intelligence in Mass Public Transport. Executive Summary. Retrieved 05. 2020, from https:\/\/cms.uitp.org\/wp\/wp-content\/uploads\/2020\/08\/UITP-AP-CTE-AI-in-PT-Executive-Summary-Dec-2018_0.pdf"},{"key":"334_CR002","unstructured":"Ull\u00f3n HR, Ugarte LF, Mariotto FT, Lacusta E, de Almeida MC (2020) Data-driven solution for planning bus routes of the public transport in UNICAMP. In: Proceedings of the 33rd international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems (ECOS), pp 2097\u20132108"},{"issue":"176","key":"334_CR122","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1111\/1468-2451.55020144","volume":"55","author":"P Van Egmond","year":"2003","unstructured":"Van Egmond P, Nijkamp P, Vindigni G (2003) A comparative analysis of the performance of urban public transport systems in Europe. Int Soc Sci J 55(176):235\u2013247. https:\/\/doi.org\/10.1111\/1468-2451.55020144","journal-title":"Int Soc Sci J"},{"key":"334_CR123","unstructured":"Velosa F, Florez H (2020) Edge solution with machine learning and open data to interpret signs for people with visual disability. ICAI Workshops. https:\/\/ceur-ws.org\/Vol-2714\/icaiw_waai_2.pdf"},{"key":"334_CR124","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tbs.2020.02.004","volume":"20","author":"R Victoriano","year":"2020","unstructured":"Victoriano R, Paez A, Carrasco J-A (2020) Time, space, money, and social interaction: using machine learning to classify people\u2019s mobility strategies through four key dimensions. Travel Behav Soc 20:1\u201311. https:\/\/doi.org\/10.1016\/j.tbs.2020.02.004","journal-title":"Travel Behav Soc"},{"key":"334_CR125","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.trpro.2018.11.029","volume":"34","author":"B Wang","year":"2018","unstructured":"Wang B, Kim I (2018) Short-term prediction for bike-sharing service using machine learning. Transp Res Procedia 34:171\u2013178. https:\/\/doi.org\/10.1016\/j.trpro.2018.11.029","journal-title":"Transp Res Procedia"},{"key":"334_CR126","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.trc.2019.08.017","volume":"107","author":"W Wang","year":"2019","unstructured":"Wang W, Liu J, Yao B, Jiang Y, Wang Y, Yu B (2019) A data-driven hybrid control framework to improve transit performance. Transp Res Part C: Emerg Technol 107:387\u2013410. https:\/\/doi.org\/10.1016\/j.trc.2019.08.017","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"23","key":"334_CR127","doi-asserted-by":"publisher","first-page":"10007","DOI":"10.3390\/su122310007","volume":"12","author":"S Wang","year":"2020","unstructured":"Wang S, Lu C, Liu C, Zhou Y, Bi J, Zhao X (2020) Understanding the energy consumption of battery electric buses in urban public transport systems. Sustainability 12(23):10007. https:\/\/doi.org\/10.3390\/su122310007","journal-title":"Sustainability"},{"key":"334_CR128","doi-asserted-by":"publisher","unstructured":"Wei Y, Song N, Ke L, Chang M-C, Lyu S (2017) Street object detection\/tracking for AI city traffic analysis. 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, 1\u20135. https:\/\/doi.org\/10.1109\/UIC-ATC.2017.8397669","DOI":"10.1109\/UIC-ATC.2017.8397669"},{"issue":"6","key":"334_CR129","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1080\/01441647.2019.1616849","volume":"39","author":"TF Welch","year":"2019","unstructured":"Welch TF, Widita A (2019) Big data in public transportation: a review of sources and methods. Transp Reviews 39(6):795\u2013818. https:\/\/doi.org\/10.1080\/01441647.2019.1616849","journal-title":"Transp Reviews"},{"key":"334_CR130","first-page":"326","volume-title":"City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms, Automatio.","author":"A Wilkowski","year":"2020","unstructured":"Wilkowski A, Mykhalevych I, Luckner M (2020) City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms, Automatio. Springer International Publishing, Berlin, pp 326\u201333"},{"key":"334_CR131","doi-asserted-by":"publisher","unstructured":"Xie S-Y, Gao S, Xu B (2004) Study of an optimum scheduling algorithm about buses in city intelligent transport systems. Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), 5, 2795\u20132799. https:\/\/doi.org\/10.1109\/ICMLC.2004.1378507","DOI":"10.1109\/ICMLC.2004.1378507"},{"key":"334_CR132","doi-asserted-by":"publisher","unstructured":"Xue M, Wu H, Chen W, Ng WS, Goh GH (2014) Identifying tourists from public transport commuters. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1779\u20131788. https:\/\/doi.org\/10.1145\/2623330.2623352","DOI":"10.1145\/2623330.2623352"},{"key":"334_CR133","doi-asserted-by":"publisher","first-page":"101521","DOI":"10.1016\/j.compenvurbsys.2020.101521","volume":"83","author":"Y Yang","year":"2020","unstructured":"Yang Y, Heppenstall A, Turner A, Comber A (2020a) Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Comput Environ Urban Syst 83:101521. https:\/\/doi.org\/10.1016\/j.compenvurbsys.2020.101521","journal-title":"Comput Environ Urban Syst"},{"issue":"4","key":"334_CR134","doi-asserted-by":"publisher","first-page":"31","DOI":"10.11916\/j.issn.1005-9113.2018007","volume":"27","author":"C Yang","year":"2020","unstructured":"Yang C, Ru X, Hu B (2020b) Route temporal-spatial information based residual neural networks for bus arrival time prediction. J Harbin Inst Technol (New series) 27(4):31\u201339. https:\/\/doi.org\/10.11916\/j.issn.1005-9113.2018007","journal-title":"J Harbin Inst Technol (New series)"},{"key":"334_CR135","doi-asserted-by":"publisher","unstructured":"Yu L, Wu W, Li X, Li G, Ng WS, Ng S-K, Huang Z, Arunan A, Watt HM (2015) iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data. 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), 49\u201356. https:\/\/doi.org\/10.1109\/VAST.2015.7347630","DOI":"10.1109\/VAST.2015.7347630"},{"key":"334_CR136","doi-asserted-by":"publisher","unstructured":"Yu D, Ding M, Wang C (2018) A design for a public transport information service in China. International Conference of Design, User Experience, and Usability, 435\u2013444. https:\/\/doi.org\/10.1007\/978-3-319-91806-8_34","DOI":"10.1007\/978-3-319-91806-8_34"},{"issue":"11","key":"334_CR137","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.3390\/electronics9111876","volume":"9","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Shao C, Cao Z, He Z, Zhu C, Wang Y, Jang V (2020) Bus Dynamic Travel Time Prediction: using a deep feature extraction framework based on RNN and DNN. Electronics 9(11):1876. https:\/\/doi.org\/10.3390\/electronics9111876","journal-title":"Electronics"},{"key":"334_CR138","doi-asserted-by":"publisher","unstructured":"Zhang Y, Chen G (2018) Inferring social-demographics of travellers based on smart card data. Proceedings of 2nd International Conference on Advanced Research Methods and Analytics, 55\u201362 https:\/\/doi.org\/10.4995\/CARMA2018.2018.8310","DOI":"10.4995\/CARMA2018.2018.8310"},{"issue":"10","key":"334_CR139","doi-asserted-by":"publisher","first-page":"434","DOI":"10.3390\/ijgi8100434","volume":"8","author":"T Zhang","year":"2019","unstructured":"Zhang T, Wang J, Cui C, Li Y, He W, Lu Y, Qiao Q (2019) Integrating geovisual analytics with machine learning for human mobility pattern discovery. ISPRS Int J Geo-Inf 8(10):434. https:\/\/doi.org\/10.3390\/ijgi8100434","journal-title":"ISPRS Int J Geo-Inf"},{"key":"334_CR140","doi-asserted-by":"publisher","first-page":"102479","DOI":"10.1016\/j.jtrangeo.2019.102479","volume":"79","author":"X Zhou","year":"2019","unstructured":"Zhou X, Wang M, Li D (2019) Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning. J Transp Geogr 79:102479. https:\/\/doi.org\/10.1016\/j.jtrangeo.2019.102479","journal-title":"J Transp Geogr"}],"container-title":["Public Transport"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12469-023-00334-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12469-023-00334-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12469-023-00334-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T16:26:52Z","timestamp":1708792012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12469-023-00334-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":140,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["334"],"URL":"https:\/\/doi.org\/10.1007\/s12469-023-00334-7","relation":{},"ISSN":["1866-749X","1613-7159"],"issn-type":[{"value":"1866-749X","type":"print"},{"value":"1613-7159","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,20]]},"assertion":[{"value":"19 July 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}