Spatiotemporal dynamics of public transport demand: a case study of Riga

    Dmitry Pavlyuk Affiliation
    ; Nadežda Spiridovska Affiliation
    ; Irina Yatskiv (Jackiva) Affiliation


Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.

First published online 6 January 2021

Keyword : user travel behaviour, transport modelling, big data, public transport, smart card data, clustering

How to Cite
Pavlyuk, D., Spiridovska, N., & Yatskiv (Jackiva), I. (2020). Spatiotemporal dynamics of public transport demand: a case study of Riga. Transport, 35(6), 576-587.
Published in Issue
Dec 31, 2020
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Alsger, A. A.; Tavassoli, A.; Hickman, M.; Mesbah, M. 2018. Variation of transit demand based on smart card data, in Transportation Research Board 97th Annual Meeting, 7–11 January 2018, Washington, DC, US, 1–24.

Barry, J. J.; Freimer, R.; Slavin, H. 2009. Use of entry-only automatic fare collection data to estimate linked transit trips in New York City, Transportation Research Record: Journal of the Transportation Research Board 2112: 53–61.

Briand, A.-S.; Côme, E.; Trépanier, M.; Oukhellou, L. 2017. Analyzing year-to-year changes in public transport passenger behaviour using smart card data, Transportation Research Part C: Emerging Technologies 79: 274–289.

Cats, O.; Wang, Q.; Zhao, Y. 2015. Identification and classification of public transport activity centres in Stockholm using passenger flows data, Journal of Transport Geography 48: 10–22.

Chen, X.; Wang, Y.; Tang, J.; Dai, Z.; Ma, X. 2020. Examining regional mobility patterns of public transit and automobile users based on the smart card and mobile Internet data: a case study of Chengdu, China, IET Intelligent Transport Systems 14(1): 45–55.

Chen, Z.; Fan, W. 2018. Extracting bus transit boarding stop information using smart card transaction data, Journal of Modern Transportation 26(3): 209–219.

Deschaintres, E.; Morency, C.; Trépanier, M. 2019. Analyzing transit user behavior with 51 weeks of smart card data, Transportation Research Record: Journal of the Transportation Research Board 2673(6): 33–45.

EC. 2013. Annex 1: a Concept for Sustainable Urban Mobility Plans to the Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. COM(2013) 913 Final. European Commission (EC). 5 p. Available from Internet:

Egu, O.; Bonnel, P. 2020. Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon, Travel Behaviour and Society 19: 112–123.

El Mahrsi, M. K.; Côme, E.; Oukhellou, L.; Verleysen, M. 2017. Clustering smart card data for urban mobility analysis, IEEE Transactions on Intelligent Transportation Systems 18(3): 712–728.

Faroqi, H.; Mesbah, M.; Kim, J. 2018. Applications of transit smart cards beyond a fare collection tool: a literature review, Advances in Transportation Studies 45: 107–122.

Gentile, G.; Noekel, K. 2016. Modelling Public Transport Passenger Flows in the Era of Intelligent Transport Systems: COST Action TU1004 (TransITS). Springer. 641 p.

Goulet-Langlois, G.; Koutsopoulos, H. N.; Zhao, J. 2016. Inferring patterns in the multi-week activity sequences of public transport users, Transportation Research Part C: Emerging Technologies 64: 1–16.

Goulet-Langlois, G.; Koutsopoulos, H. N.; Zhao, Z.; Zhao, J. 2018. Measuring regularity of individual travel patterns, IEEE Transactions on Intelligent Transportation Systems 19(5): 1583–1592.

Han, G.; Sohn, K. 2016. Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model, Transportation Research Part B: Methodological 83: 121–135.

He, L.; Agard, B.; Trépanier, M. 2020. A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method, Transportmetrica A: Transport Science 16(1): 56–75.

Kieu, L. M.; Bhaskar, A.; Chung, E. 2015. Passenger segmentation using smart card data, IEEE Transactions on Intelligent Transportation Systems 16(3): 1537–1548.

Kumar, P.; Khani, A.; He, Q. 2018. A robust method for estimating transit passenger trajectories using automated data, Transportation Research Part C: Emerging Technologies 95: 731–747.

Long, Y.; Thill, J.-C. 2015. Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing, Computers, Environment and Urban Systems 53: 19–35.

Ma, X.; Wu, Y.-J.; Wang, Y.; Chen, F.; Liu, J. 2013. Mining smart card data for transit riders’ travel patterns, Transportation Research Part C: Emerging Technologies 36: 1–12.

Manley, E.; Zhong, C.; Batty, M. 2018. Spatiotemporal variation in travel regularity through transit user profiling, Transportation 45(3): 703–732.

Morency, C.; Trépanier, M.; Agard, B. 2007. Measuring transit use variability with smart-card data, Transport Policy 14(3): 193–203.

Munizaga, M. A.; Palma, C. 2012. Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile, Transportation Research Part C: Emerging Technologies 24: 9–18.

OMD. 2019. Open Mobility Data. Public Database. Available from Internet:

Pelletier, M.-P.; Trépanier, M.; Morency, C. 2011. Smart card data use in public transit: a literature review, Transportation Research Part C: Emerging Technologies 19(4): 557–568.

Qi, G.; Huang, A.; Guan, W.; Fan, L. 2019. Analysis and prediction of regional mobility patterns of bus travellers using smart card data and points of interest data, IEEE Transactions on Intelligent Transportation Systems 20(4): 1197–1214.

Rupprecht, S.; Brand, L.; Böhler-Baedeker, S.; Brunner, L. M. 2019. Guidelines for Developing and Implementing a Sustainable Urban Mobility Plan. 2nd Edition. European Platform on Sustainable Urban Mobility Plans 166 p. Available from Internet:

Tao, S.; Rohde, D.; Corcoran, J. 2014. Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap, Journal of Transport Geography 41: 21–36.

Trépanier, M.; Tranchant, N.; Chapleau, R. 2007. Individual trip destination estimation in a transit smart card automated fare collection system, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 11(1): 1–14.

Wang, W.; Attanucci, J.; Wilson, N. H. M. 2011. Bus passenger origin-destination estimation and related analyses using automated data collection systems, Journal of Public Transportation 14(4): 131–150.

Wei, S.; Yuan, J.; Qiu, Y.; Luan, X.; Han, S.; Zhou, W.; Xu, C. 2017. Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system, PLoS ONE 12(6): e0178023.

Welch, T. F.; Widita, A. 2019. Big data in public transportation: a review of sources and methods, Transport Reviews 39(6): 795–818.

Xu, Z.; Cui, G.; Zhong, M.; Wang, X. 2019. Anomalous urban mobility pattern detection based on GPS trajectories and POI data, ISPRS International Journal of Geo-Information 8(7): 308.

Zhong, C.; Manley, E.; Arisona, S. M.; Batty, M.; Schmitt, G. 2015. Measuring variability of mobility patterns from multiday smart-card data, Journal of Computational Science 9: 125–130.