International Journal of Transportation Engineering

International Journal of Transportation Engineering

In Light of the Automated Fare Collection Data, How Did the Travel Patterns of Transit Riders in Tehran Change following COVID-19?

Document Type : Research Paper

Authors
Mostafa Shafaati 1
Mahmoud Saffarzadeh 2
1 PHD candidate, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
2 Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
10.22119/ijte.2023.413809.1649
Abstract
The spread of COVID-19 caused some problems in public transportation. The pandemic created new challenges for developing countries like Iran, where public transportation is already plagued by many problems. As a result of COVID-19 concerns, it was speculated that unpredictable travel patterns would result. Based on Automatic Fare Collection data, in which passengers use a smartcard to enter a stop, this study evaluates this speculation. The dataset includes one month of transactions for each of the three COVID-19-related years (2019, 2020, and 2021) in Tehran, the country's capital. By using time series clustering, it was found that a new pattern of travel has emerged. Before vaccination, most origins were in the eastern part of the city; whereas, in the new era, most of the origins are in the western part of the city. The peak hours have also undergone a significant change. Prior to the pandemic, the peak hour occurred between 7 and 8 o'clock in the morning, and demand reduced until the evening peak hour, but as a result of the new pattern, demand did not decrease significantly after 8:00:00, which resulted in new peak hours. It is anticipated that these changes will have a domino effect on Tehran's transit system as a whole. The system may not be able to handle the changes in behavior, as it was designed to deal with pre-pandemic behavior patterns. There is a need for rescheduling to resolve the problem. Additionally, the government should develop a long-term plan for restoring public transportation demand to its pre-pandemic level.
Keywords
Public Transportation
COVID-19 vaccination
Transit Ridership
Machine Learning
Time series clustering
- Abdullah, M., Ali, N., Hussain, S. A., Aslam, A. B., & Javid, M. A. (2021). Measuring changes in travel behavior pattern due to COVID-19 in a developing country: A case study of Pakistan. Transport policy, 108, 21-33.
 
- Aggarwal CC. Data mining: the textbook. New York: springer; 2015 Apr 13.
 
- Ali, M., Alqahtani, A., Jones, M. W., & Xie, X. (2019). Clustering and classification for time series data in visual analytics: A survey. IEEE Access, 7, 181314-181338.
 
- Almannaa, M. H., Elhenawy, M., & Rakha, H. A. (2019). A novel supervised clustering algorithm for transportation system applications. IEEE transactions on intelligent transportation systems, 21(1), 222-232.
 
- Alonso de Armiño, C., Manzanedo, M. Á., & Herrero, Á. (2020). Analysing the intermeshed patterns of road transportation and macroeconomic indicators through neural and clustering techniques. Pattern Analysis and Applications, 23(3), 1059-1070.
 
- Arab-Mazar, Z., Sah, R., Rabaan, A. A., Dhama, K., & Rodriguez-Morales, A. J. (2020). Mapping the incidence of the COVID-19 hotspot in Iran–Implications for Travellers. Travel medicine and infectious disease, 34, 101630.
 
- Asadi, R., & Regan, A. (2021). Clustering of time series data with prior geographical information. arXiv preprint arXiv:2107.01310.
 
- Aziz, H. A., & Ukkusuri, S. V. (2018). A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures. International Journal of Sustainable Transportation, 12(3), 192-204.
 
- Barter, R. L., & Yu, B. (2018). Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data. Journal of Computational and Graphical Statistics, 27(4), 910-922.
 
- Beck, M. J., & Hensher, D. A. (2020). Insights into the impact of COVID-19 on household travel and activities in Australia–The early days under restrictions. Transport policy, 96, 76-93.
 
- Beck, M. J., & Hensher, D. A. (2020). Insights into the impact of COVID-19 on household travel and activities in Australia–The early days under restrictions. Transport policy, 96, 76-93.
 
- Benita, F. (2021). Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. Sustainable Cities and Society, 70, 102916.
 
- Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1-27.
 
- Cao, J., Li, S., Noland, R. B., & Ge, Y. E. (2021). The first 25 years of Transportation Research Part D: Transport and Environment. Transportation Research Part D: Transport and Environment, 100, 103078.
 
- Cazelles, B., Comiskey, C., Nguyen-Van-Yen, B., Champagne, C., & Roche, B. (2021). Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods. International Journal of Infectious Diseases, 104, 693-695.
 
- Chabchoub, Y., & Fricker, C. (2014, November). Classification of the vélib stations using Kmeans, Dynamic Time Wraping and DBA averaging method. In 2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (pp. 1-5). IEEE.
 
- Chen, R., Zhang, J., Ravishanker, N., & Konduri, K. (2019). Clustering activity–travel behavior time series using topological data analysis. Journal of Big Data Analytics in Transportation, 1, 109-121.
 
- Ciriaco, T. G. M., Pitombo, C. S., & Assirati, L. (2023). Travel behavior and activity resilience regarding the COVID-19 pandemic in Brazil: An approach based on smartphone panel data. Case Studies on Transport Policy, 12, 100998.
 
- Dai, J., Liu, Z., & Li, R. (2021). Improving the subway attraction for the post-COVID-19 era: The role of fare-free public transport policy. Transport Policy, 103, 21-30.
 
- Das, S., Boruah, A., Banerjee, A., Raoniar, R., Nama, S., & Maurya, A. K. (2021). Impact of COVID-19: A radical modal shift from public to private transport mode. Transport Policy, 109, 1-11.
- Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2), 224-227.
 
- Dzisi, E. K. J., & Dei, O. A. (2020). Adherence to social distancing and wearing of masks within public transportation during the COVID 19 pandemic. Transportation Research Interdisciplinary Perspectives, 7, 100191.
 
- Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. ACM Sigmod Record, 23(2), 419-429.
 
- Giouroukelis, M., Papagianni, S., Tzivellou, N., Vlahogianni, E. I., & Golias, J. C. (2022). Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece. Case Studies on Transport Policy, 10(2), 1069-1077.
 
- Gkiotsalitis, K., & Cats, O. (2021). Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transport Reviews, 41(3), 374-392.
 
- Han, J., Kamber, M. and Pei, J., 2011. Data Transformation and Data Discretization, chap. 3.
 
- 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.
 
- Javed, A., Lee, B. S., & Rizzo, D. M. (2020). A benchmark study on time series clustering. Machine Learning with Applications, 1, 100001.
 
- Jenelius, E., & Cebecauer, M. (2020). Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transportation Research Interdisciplinary Perspectives, 8, 100242.
 
- Kutela, B., Novat, N., & Langa, N. (2021). Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach. Sustainable cities and society, 67, 102729.
 
- Lane, B. W. (2012). A time-series analysis of gasoline prices and public transportation in US metropolitan areas. Journal of Transport Geography, 22, 221-235.
 
- Limsawasd, C., Athigakunagorn, N., Khathawatcharakun, P., & Boonmee, A. (2022). Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak. Transport Policy, 126, 225-238.
 
- Lizana, M., Choudhury, C., & Watling, D. (2023). Using smart card data to model public transport user profiles in light of the COVID-19 pandemic. Travel Behaviour and Society, 33, 100620.
 
- Loa, P., Hossain, S., Mashrur, S. M., Liu, Y., Wang, K., Ong, F., & Habib, K. N. (2021). Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area. Transport policy, 110, 71-85.
 
- MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
- Mogaji, E. (2022). Wishful thinking? Addressing the long-term implications of COVID-19 for transport in Nigeria. Transportation Research Part D: Transport and Environment, 105, 103206.
 
- Mogaji, E., Adekunle, I., Aririguzoh, S., & Oginni, A. (2022). Dealing with impact of COVID-19 on transportation in a developing country: Insights and policy recommendations. Transport Policy, 116, 304-314.
 
- Muller, M. (2007). Dynamic time warping in information retrieval for music and motion. Dynamic time warping Information retrieval for music and motion, 69-84.
 
- Nikolaidou, A., Kopsacheilis, A., Georgiadis, G., Noutsias, T., Politis, I., & Fyrogenis, I. (2023). Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis. Cities, 134, 104206.
 
- Paparrizos, J., & Gravano, L. (2017). Fast and accurate time-series clustering. ACM Transactions on Database Systems (TODS), 42(2), 1-49.
 
- Paparrizos, J., & Gravano, L. (2017). Fast and accurate time-series clustering. ACM Transactions on Database Systems (TODS), 42(2), 1-49.
 
- Parker, M. E., Li, M., Bouzaghrane, M. A., Obeid, H., Hayes, D., Frick, K. T. & Chatman, D. G. (2021). Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period. Transport policy, 111, 53-62.
 
- Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern recognition, 44(3), 678-693.
- Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q. & Keogh, E. (2013). Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3), 1-31.
 
- Rothengatter, W., Zhang, J., Hayashi, Y., Nosach, A., Wang, K., & Oum, T. H. (2021). Pandemic waves and the time after Covid-19–Consequences for the transport sector. Transport Policy, 110, 225-237.
 
- Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
 
- Ryabko, B. Y., Stognienko, V. S., & Shokin, Y. I. (2004). A new test for randomness and its application to some cryptographic problems. Journal of statistical planning and inference, 123(2), 365-376.
 
- Shabani, A., Shabani, A., Ahmadinejad, B., & Salmasnia, A. (2022). Measuring the customer satisfaction of public transportation in Tehran during the COVID-19 pandemic using MCDM techniques. Case studies on transport policy, 10(3), 1520-1530.
 
- Sundarkumar, G. G., BV, S. B., Munigety, C. R., & Arora, A. S. (2021). A time series clustering based approach for construction of real-world drive cycles. Transportation Research Part D: Transport and Environment, 97, 102896.
 
- Thaithatkul, P., Sanghatawatana, P., Anuchitchanchai, O., Laosinwattana, W., Liang, J., & Chalermpong, S. (2023). Travel behavior change of public transport users during the COVID-19 pandemic: Evidence from Bangkok. Asian Transport Studies, 9, 100102.
 
- Wang, K., Liu, Y., Mashrur, S. M., Loa, P., & Habib, K. N. (2021). COVid-19 influenced households’ Interrupted Travel Schedules (COVHITS) survey: Lessons from the fall 2020 cycle. Transport Policy, 112, 43-62.
 
- Wang, Y., & Gao, Y. (2022). Travel satisfaction and travel well-being: Which is more related to travel choice behaviour in the post COVID-19 pandemic? Evidence from public transport travellers in Xi’an, China. Transportation Research Part A: Policy and Practice, 166, 218-233.
 
- Wu X, Kumar V, editors. The top ten algorithms in data mining. CRC press; 2009 Apr 9.
 
- Xin, M., Shalaby, A., Feng, S., & Zhao, H. (2021). Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method. Transport Policy, 111, 1-16.
 
- Zafri, N. M., Khan, A., Jamal, S., & Alam, B. M. (2021). Impacts of the COVID-19 pandemic on active travel mode choice in Bangladesh: a study from the perspective of sustainability and new normal situation. Sustainability, 13(12), 6975.
- Zhang, J., & Hayashi, Y. (2022). Research frontier of COVID-19 and passenger transport: A focus on policymaking. Transport Policy, 119, 78-88.
 
- Zhang, J., Hayashi, Y., & Frank, L. D. (2021). COVID-19 and transport: Findings from a world-wide expert survey. Transport policy, 103, 68-85.
 
- Zhao, Q., Qi, Y., & Wali, M. M. (2023). A method for assessing the COVID-19 infection risk of riding public transit. International Journal of Transportation Science and Technology, 12(1), 301-314.
International Journal of Transportation Engineering
Volume 14, Issue 1 - Serial Number 53
Summer 2026
Pages 2387-2418