Abstract
Crowding in public transport is one of the reasons that nudges road users to shift from public transport to private modes of transport. To provide the passengers with a facility to plan their trips as per the dynamic crowding levels, this work proposes a framework for a passenger information system (PIS), in which the transit choices are differentiated with respect to crowding levels on the transit routes at different times of the day. A granular crowding prediction model is developed and integrated with PIS. In this, firstly, the transit segment relation (TSR) is constituted and used to make clusters based on the ridership index. Further, a time-series model is trained for each cluster using boarding TSR. A case study of Bhubaneswar, India, is presented, and three months of ticketing data are used to demonstrate the performance of the proposed prediction model. The prediction model is integrated into the PIS to expedite various route choices.
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The use of the HERE public transit API is not essential; see Sect. 3.2.
In contrast to the manual work, for a larger scenario this can be scripted; see Choudhary and Agarwal (2021).
Refer to https://dataspace.mobi/dataset/city-bus-bhubaneswar-gtfs-static. It was accessed on Mar. 10, 2021.
References
Agarwal P, Rambha T (2021) Scalable algorithms for bicriterion trip-based transit routing. Tech. rep., Indian Institute of Science, Bangalore. https://doi.org/10.48550/arxiv.2111.06654
Aghabayk K, Esmailpour J, Shiwakoti N (2021) Effects of COVID-19 on rail passengers’ crowding perceptions. Transp Res Part A Policy Pract 154:186–202. https://doi.org/10.1016/j.tra.2021.10.011
Alsger AA, Mesbah M, Ferreira L, Seifi H (2015) Use of smart card fare data to estimate public transport origin–destination matrix. Transp Res Rec 2535(1):88–96. https://doi.org/10.3141/2535-10
Alsger A, Tavassoli A, Mesbah M, Ferreira L, Hickman M (2018) Public transport trip purpose inference using smart card fare data. Transp Res Part C Emerg Technol 87:123–137. https://doi.org/10.1016/j.trc.2017.12.016
Arabghalizi T, Labrinidis A (2020) Data-driven bus crowding prediction models using context-specific features. ACM/IMS Trans Data Sci 1(3):1–33. https://doi.org/10.1145/3406962
Barabino B, Lai C, Olivo A (2020) Fare evasion in public transport systems: a review of the literature. Public Transp 12(1):27–88. https://doi.org/10.1007/s12469-019-00225-w
Basu D, Hunt JD (2012) Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: a stated preference approach. Transp Res Part A Policy Pract 46(9):1465–1476. https://doi.org/10.1016/j.tra.2012.05.010
Bhandari J, Fedujwar R, Agarwal A (2024) Occupancy prediction at transit stops using ANN. In: 16th international conference on communication systems & networks (COMSNETS), pp 825–832. https://doi.org/10.1109/COMSNETS59351.2024.10427191
Björklund G, Swärdh JE (2017) Estimating policy values for in-vehicle comfort and crowding reduction in local public transport. Transp Res Part A Policy Pract 106:453–472. https://doi.org/10.1016/j.tra.2017.10.016
Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27. https://doi.org/10.1080/03610927408827101
Chen Z, Fan W (2018) Extracting bus transit boarding stop information using smart card transaction data. J Mod Transp 26(3):209–219. https://doi.org/10.1007/s40534-018-0165-y
Chen E, Ye Z, Wang C, Xu M (2020) Subway passenger flow prediction for special events using smart card data. IEEE Trans Intell Transp Syst 21(3):1109–1120. https://doi.org/10.1109/tits.2019.2902405
Choudhary R, Agarwal A (2024) Route selection for real-time air quality monitoring to maximize spatiotemporal coverage. J Transp Geogr 115:103812. https://doi.org/10.1016/j.jtrangeo.2024.103812
Choudhary R, Ratra S, Agarwal A (2022) Multimodal routing framework for urban environments considering real-time air quality and congestion. Atmos Pollut Res 13(9):101525. https://doi.org/10.1016/j.apr.2022.101525
Cyril A, George V, Mulangi R (2017) Electronic ticket machine data analytics for public bus transport planning. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS). IEEE, pp 3917–3922. https://doi.org/10.1109/icecds.2017.8390198
Decouvelaere R, Trépanier M, Agard B (2022) Modulated spatiotemporal clustering of smart card users. Public Transp. https://doi.org/10.1007/s12469-022-00305-4
Delling D, Pajor T, Werneck RF (2015) Round-based public transit routing. Transp Sci 49(3):591–604. https://doi.org/10.1287/trsc.2014.0534
Dou M, He T, Yin H et al. (2015) Predicting passengers in public transportation using smart card data. In: Sharaf M, Cheema M, Qi J (eds) Databases theory and applications. ADC 2015. Lecture notes in computer science, vol 9093. Springer, Cham, pp 28–40. https://doi.org/10.1007/978-3-319-19548-3_3
Fedujwar R, Agarwal A (2024) A systematic review on crowding valuation in public transport. Publ Transp. https://doi.org/10.1007/s12469-024-00363-w
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976. http://utstat.toronto.edu/reid/sta414/frey-affinity.pdf
Gao K, Sun L, Tu H, Li H (2018) Heterogeneity in valuation of travel time reliability and in-vehicle crowding for mode choices in multimodal networks. J Transp Eng Part A Syst 144:10. https://doi.org/10.1061/jtepbs.0000187
Ge L, Voß S, Xie L (2022) Robustness and disturbances in public transport. Public Transp 14(1):191–261. https://doi.org/10.1007/s12469-022-00301-8
Gong Z, Du B, Liu Z et al. (2020) SD-seq2seq : a deep learning model for bus bunching prediction based on smart card data. In: 29th international conference on computer communications and networks (ICCCN), pp 1–9. https://doi.org/10.1109/ICCCN49398.2020.9209686
Halyal S, Mulangi RH, Harsha MM (2022) Forecasting public transit passenger demand: with neural networks using APC data. Case Stud Transp Policy 10(2):965–975. https://doi.org/10.1016/j.cstp.2022.03.011
Han Q, Liu K, Zeng L et al. (2020) A bus arrival time prediction method based on position calibration and LSTM. IEEE Access 8:42372–42383. https://doi.org/10.1109/ACCESS.2020.2976574
Hirsch L, Thompson K (2011) I can sit but I’d rather stand: commuter’s experience of crowdedness and fellow passenger behaviour in carriages on Australian metropolitan trains. In: 34th Australian transport research forum. https://www.worldtransitresearch.info/research/4323/
Jiang W, Ma Z, Koutsopoulos HN (2022) Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems. Neural Comput Appl 34(6):4813–4830. https://doi.org/10.1007/s00521-021-06669-1
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):334–339. https://doi.org/10.1049/iet-its.2016.0276
Klumpenhouwer W, Wirasinghe SC (2016) Cost-of-crowding model for light rail train and platform length. Public Transp 8(1):85–101. https://doi.org/10.1007/s12469-015-0118-3
Li G, Chen A (2022) Frequency-based path flow estimator for transit origin-destination trip matrices incorporating automatic passenger count and automatic fare collection data. Transp Res Part E Logis Transp Rev 163(102):754. https://doi.org/10.1016/j.tre.2022.102754
Li Z, Hensher D (2013) Crowding in public transport: a review of objective and subjective measures. J Public Transp 16(2):107–134. https://doi.org/10.5038/2375-0901.16.2.6
Liang S, Ma M, He S, Zhang H (2019) Short-term passenger flow prediction in urban public transport: Kalman filtering combined K-nearest neighbor approach. IEEE Access 7:120937–120949. https://doi.org/10.1109/ACCESS.2019.2937114
Liu L, Chen RC (2017) A novel passenger flow prediction model using deep learning methods. Transp Res Part C Emerg Technol 84:74–91. https://doi.org/10.1016/j.trc.2017.08.001
Liu J, Wen H (2016) Public transport crowding valuation: evidence from college students in Guangzhou. J Public Transp 19(3):78–97. https://doi.org/10.5038/2375-0901.19.3.5
Ma X, Wu YJ, Wang Y, Chen F, Liu J (2013) Mining smart card data for transit riders’ travel patterns. Transp Res Part C Emerg Technol 36:1–12. https://doi.org/10.1016/j.trc.2013.07.010
Mittal V, Sasetty S, Choudhary R, Agarwal A (2022) Deep-learning spatio-temporal prediction framework for PM under dynamic monitoring. Transp Res Rec 2676(8):56–73. https://doi.org/10.1177/03611981221082589
Mo B, Koutsopoulos HN, Zhao J (2022) Inferring passenger responses to urban rail disruptions using smart card data: a probabilistic framework. Transp Res Part E Logist Transp Rev 159(102):628. https://doi.org/10.1016/j.tre.2022.102628
Morency C, Trépanier M, Agard B (2007) Measuring transit use variability with smart-card data. Transp Policy 14(3):193–203. https://doi.org/10.1016/j.tranpol.2007.01.001
Nagaraj N, Gururaj HL, Swathi BH, Hu Y-C (2022) Passenger flow prediction in bus transportation system using deep learning. Multimed Tools Appl 81(9):12519–12542. https://doi.org/10.1007/s11042-022-12306-3
Pasini K, Khouadjia M, Samé, Ganansia F, Oukhellou L (2020) LSTM encoder–predictor for short-term train load forecasting. In: Brefeld U, Fromont E, Hotho A et al. (eds) Machine learning and knowledge discovery in databases, pp 535–551
Peftitsi S, Jenelius E, Cats O (2022) Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains. Transp Res Part A Policy Pract 166:354–368. https://doi.org/10.1016/j.tra.2022.10.011
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv. https://doi.org/10.48550/arxiv.1609.04747
Sahu PK, Sharma G, Guharoy A (2018) Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai. Public Transp 10(3):379–398. https://doi.org/10.1007/s12469-018-0190-6
Sajanraj TD, Mulerikkal J, Raghavendra S, Vinith R, Fabera V (2021) Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems. Neural Netw World 31(3):173–189. https://doi.org/10.14311/nnw.2021.31.009
Schmöcker JD, Kurauchi F, Shimamoto H (2017) An overview on opportunities and challenges of smart card data analysis. In: Kurauchi F, Schmöcker JD (eds) Public transport planning with smart card data. CRC Press, pp 1–12. https://doi.org/10.1201/9781315370408
Shao M, Xie C, Li T, Sun L (2022) Influence of in-vehicle crowding on passenger travel time value: insights from bus transit in Shanghai, China. Int J Transp Sci Technol 11(4):665–677. https://doi.org/10.1016/j.ijtst.2021.09.001
Suman HK, Agarwal A, Bolia NB (2020) Public transport operations after lockdown: how to make it happen? Trans Indian Natl Acad Eng 5(2):149–156. https://doi.org/10.1007/s41403-020-00121-x
TERI (2020) Impact of COVID-19 on urban mobility in India: evidence from a perception study. Tech. rep., The Energy and Resources Institute (TERI). https://www.teriin.org/sites/default/files/2020-05/behavioural-effects-covid19_0.pdf
Thavikulwat P (2008) Affinity propagation: a clustering algorithm for computer-assisted business simulations and experiential exercises. Dev Bus Simul Exp Learn Proc Annu ABSEL Conf 35:220–224
Thombre A, Agarwal A (2021) A paradigm shift in urban mobility: policy insights from travel before and after COVID-19 to seize the opportunity. Transp Policy 110:335–353. https://doi.org/10.1016/j.tranpol.2021.06.010
Tirachini A, Hensher DA, Rose JM (2013) Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transp Res Part A Policy Pract 53:36–52. https://doi.org/10.1016/j.tra.2013.06.005
Tirachini A, Hurtubia R, Dekker T et al. (2017) Estimation of crowding discomfort in public transport: results from Santiago de Chile. Transp Res Part A Policy Pract 103:311–326. https://doi.org/10.1016/j.tra.2017.06.008
van Oort N, Brands T, de Romph E (2019) Short-term prediction of ridership on public transport with smart card data. Transp Res Rec 2535(1):105–111. https://doi.org/10.3141/2535-12
Xie Z, Zhu J, Wang F, Li W, Wang T (2020) Long short-term memory based anomaly detection: a case study of China railway passenger ticketing system. IET Intel Transp Syst 15(1):98–106. https://doi.org/10.1049/itr2.12007
Xu Y, Jin K (2021) An LSTM approach for predicting the short-time passenger flow of urban bus. In: 2nd international conference on artificial intelligence in electronics engineering, pp 35–40. https://doi.org/10.1145/3460268.3460274
Yang X, Xue Q, Ding M, Wu J, Gao Z (2021) Short-term prediction of passenger volume for urban rail systems: a deep learning approach based on smart-card data. Int J Prod Econ 231(107):920. https://doi.org/10.1016/j.ijpe.2020.107920
Yu H, Chen D, Wu Z, Ma X, Wang Y (2016) Headway-based bus bunching prediction using transit smart card data. Transp Res Part C Emerg Technol 72:45–59. https://doi.org/10.1016/j.trc.2016.09.007
Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270. https://doi.org/10.1162/neco_a_01199
Zhang Y, Cheng T (2020) A deep learning approach to infer employment status of passengers by using smart card data. IEEE Trans Intell Transp Syst 21(2):617–629. https://doi.org/10.1109/TITS.2019.2896460
Zhang C, Zhang L, Liu Y, Yang X (2018) Short-term prediction of bike-sharing usage considering public transport: a LSTM approach. In: 21st international conference on intelligent transportation systems (ITSC), pp 1564–1571. https://doi.org/10.1109/ITSC.2018.8569726
Zhang J, Chen F, Shen Q (2019) Cluster-based LSTM network for short-term passenger flow forecasting in urban rail transit. IEEE Access 7:147653–147671. https://doi.org/10.1109/access.2019.2941987
Zhao Z, Chen W, Wu X, Chen PCY, Liu J (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intel Transp Syst 11(2):68–75. https://doi.org/10.1049/iet-its.2016.0208
Zou Q, Yao X, Zhao P, Wei H, Ren H (2016) Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway. Transportation 45(3):919–944. https://doi.org/10.1007/s11116-016-9756-9
Zuo Z, Yin W, Yang G et al. (2019) Determination of bus crowding coefficient based on passenger flow forecasting. J Adv Transp 2019:2751916(1–12). https://doi.org/10.1155/2019/2751916
Acknowledgements
The authors wish to thank the National Institute of Urban Affairs (NIUA), Deutsche Gesellschaft für internationale Zusammenarbeit (GiZ) India for facilitating the data and suggestions to make the model realistic. The authors also acknowledge the support of Devesh Pratap Singh, Itisha Jain, undergraduate scholar, and Rupam Fedujwar, Research Scholar at Indian Institute of Technology Roorkee, for their contribution.
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Shrivastava, A., Rawat, N. & Agarwal, A. Deep-learning-based model for prediction of crowding in a public transit system. Public Transp 16, 449–484 (2024). https://doi.org/10.1007/s12469-024-00360-z
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DOI: https://doi.org/10.1007/s12469-024-00360-z