Zacytuj

Aliari, S., Sadabadi, K. F. 2019. Automatic detection of major freeway congestion events using wireless traffic sensor data: machine learning approach. Transportation Research Record, 2673(7), pp. 436-442. Available: https://doi.org/10.1177/0361198119843859Search in Google Scholar

Ando, T., Sato, T., Hashimoto, N., Tran, Y., Konishi, N., Takeda, Y., Akamatsu, M. 2021. Variability in human mobility during the third wave of COVID-19 in japan. Sustainability (Basel, Switzerland), 13(23), pp. 13131. Available: https://doi.org/10.3390/su132313131Search in Google Scholar

Aparicio, J. T., Arsenio, E., Henriques, R. 2021. Understanding the impacts of the COVID-19 pandemic on public transportation travel patterns in the city of lisbon. Sustainability (Basel, Switzerland), 13(15), pp. 8342. Available: https://doi.org/10.3390/su13158342Search in Google Scholar

Cao, Y., Li, X. 2022. Multi-model attention fusion multilayer perceptron prediction method for subway OD passenger flow under COVID-19. Sustainability (Basel, Switzerland), 14(21), pp. 14420. Available: https://doi.org/10.3390/su142114420Search in Google Scholar

Cheshmehzangi, A., Sedrez, M., Ren, J., Kong, D., Shen, Y., Bao, S., Xu, J., Su, Z., Dawodu, A. 2021. The effect of mobility on the spread of COVID-19 in light of regional differences in the european union. Sustainability (Basel, Switzerland), 13(10), pp. 5395. Available: https://doi.org/10.3390/su13105395Search in Google Scholar

Docquier, F., Golenvaux, N., Nijssen, S., Schaus, P., Stips, F. 2022. Cross-border mobility responses to COVID-19 in europe: New evidence from facebook data. Globalization and Health, 18(1), pp. 41-41. Available: https://doi.org/10.1186/s12992-022-00832-6Search in Google Scholar

Faye, S., Chaudet, C. 2016. Characterizing the topology of an urban wireless sensor network for road traffic management. IEEE Transactions on Vehicular Technology, 65(7), 5720-5725. Available: https://doi.org/10.1109/TVT.2015.2465811Search in Google Scholar

Garunovic, N., Bogdanović, V., Davidović, S., Mirović, V., Mitrović Simić, J. 2021. Characteristics of traffic flows at roundabouts in the city of banja luka before and during COVID-19 crisis. Put i Saobraćaj, 67(4), pp. 31-35. Available: https://doi.org/10.31075/67.04.06Search in Google Scholar

Gupta, A., Katarya, R. 2023. Possibility of the COVID-19 third wave in india: Mapping from second wave to third wave. Indian Journal of Physics, 97(2), pp. 389-399. Available: https://doi.org/10.1007/s12648-022-02425-wSearch in Google Scholar

Hrudkay, K., Bárta, D. 2022. Senzorové siete pre nastavenie regulácie dopravy miest zohľadňujúce klimatickú zmenu. /Sensor networks for setting urban traffic regulation taking into consideration climate change [electronic]/. In: Dopravná infraštruktúra v mestách [electronic]: zborník. - 1. vyd. - Žilina: Žilinská univerzita v Žiline. p. 1-10. ISBN 978-80-554-1904-6 (online).Search in Google Scholar

Hrudkay, K., Madleňáková, L., Čulík, K., Morgoš, J. 2022. City logistics the centre of the Slovak county town. In: 10th Carpathial Logistics Congress, Hotel Pod Zámkom, Bojnice, Slovakia, EU, June 15 - 17, pp. 147-153. Available: https://doi.org/10.37904/clc.2022.4552Search in Google Scholar

Huang, T., Chakraborty, P., Sharma, A., Hegde, C. 2021. Large-scale data-driven traffic sensor health monitoring. Journal of Big Data Analytics in Transportation, 3(3), pp. 229-245. Available: https://doi.org/10.1007/s42421-021-00049-wSearch in Google Scholar

Jaekel, B., Muley, D. 2022. Transport impacts in germany and state of qatar: An assessment during the first wave of COVID-19. Transportation Research Interdisciplinary Perspectives, 13, pp. 100540-100540. Available: https://doi.org/10.1016/j.trip.2022.100540Search in Google Scholar

Jang, S. Y., Hussain-Alkhateeb, L., Rivera Ramirez, T., Al-Aghbari, A. A., Chackalackal, D. J., Cardenas-Sanchez, R., Carrillo, M. A., Oh, I., Alfonso-Sierra, E. A., Oechsner, P., Kibiwott Kirui, B., Anto, M., Diaz-Monsalve, S., Kroeger, A. 2021. Factors shaping the COVID-19 epidemic curve: a multi-country analysis. BMC infectious diseases, 21(1), 1-16. Available: https://doi.org/10.1186/s12879-021-06714-3Search in Google Scholar

Katrakazas, C., Michelaraki, E., Sekadakis, M., Yannis, G. 2020. A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety. Transportation Research Interdisciplinary Perspectives, 7, pp. 100186-100186. Available: https://doi.org/10.1016/j.trip.2020.100186Search in Google Scholar

Lee, H., Noh, E., Jeon, H., Nam, E. W. 2021. Association between traffic inflow and COVID-19 prevalence at the provincial level in south korea. International Journal of Infectious Diseases, 108, pp. 435-442. Available: https://doi.org/10.1016/j.ijid.2021.05.054Search in Google Scholar

Li, H., Lv, Z., Li, J., Xu, Z., Yue, W., Sun, H., Sheng, Z. 2022. Traffic flow forecasting in the COVID-19: A deep spatial-temporal model based on discrete wavelet transformation. ACM Transactions on Knowledge Discovery from Data, Available: https://doi.org/10.1145/3564753Search in Google Scholar

Liu, Z., Stern, R. 2021. Quantifying the traffic impacts of the COVID-19 shutdown. Journal of Transportation Engineering, Part A, 147(5), pp. 04021014. Available: https://doi.org/10.1061/JTEPBS.0000527Search in Google Scholar

Peng, Y., Jiang, Y., Lu, J., Zou, Y. 2018. Examining the effect of adverse weather on road transportation using weather and traffic sensors. PloS One, 13(10), pp. e0205409-e0205409. Available: https://doi.org/10.1371/journal.pone.0205409Search in Google Scholar

Prasse, B., Achterberg, M. A., Ma, L., Van Mieghem, P. F. A. 2020. Network-inference-based prediction of the COVID-19 epidemic outbreak in the chinese province hubei. Applied Network Science, 5(1), pp. 35-35. Available: https://doi.org/10.1007/s41109-020-00274-2Search in Google Scholar

Rasca, S., Markvica, K., Ivanschitz, B. P. 2021. Impacts of COVID-19 and pandemic control measures on public transport ridership in European urban areas–The cases of Vienna, Innsbruck, Oslo, and Agder. Transportation Research Interdisciplinary Perspectives, 10, pp. 100376. Available: https://doi.org/10.1016/j.trip.2021.100376Search in Google Scholar

Ravina, M., Esfandabadi, Z. S., Panepinto, D., Zanetti, M. 2021. Traffic-induced atmospheric pollution during the COVID-19 lockdown: Dispersion modeling based on traffic flow monitoring in Turin, Italy. Journal of Cleaner Production, 317, pp. 128425. Available: https://doi.org/10.1016/j.jclepro.2021.128425Search in Google Scholar

Rodríguez González, A. B., Wilby, M. R., Vinagre Díaz, J. J., Fernández Pozo, R. 2021. Characterization of COVID-19’s impact on mobility and short-term prediction of public transport demand in a mid-size city in spain. Sensors (Basel, Switzerland), 21(19), pp. 6574. Available: https://doi.org/10.3390/s21196574Search in Google Scholar

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, pp. 225-237. Available: https://doi.org/10.1016/j.tranpol.2021.06.003Search in Google Scholar

Seong, H., Hyun, H. J., Yun, J. G., Noh, J. Y., Cheong, H. J., Kim, W. J., Song, J. Y. 2021. Comparison of the second and third waves of the COVID-19 pandemic in south korea: Importance of early public health intervention. International Journal of Infectious Diseases, 104, pp. 742-745. Available: https://doi.org/10.1016/j.ijid.2021.02.004Search in Google Scholar

Singh, A., Guo, T., Bush, T., Abreu, P., Leach, F. C. P., Stacey, B., Economides, G., Anderson, R., Cole, S., Thomas, G. N., Pope, F. D., & Bartington, S. E. 2022. Impacts of COVID-19 lockdown on traffic flow, active travel and gaseous pollutant concentrations; implications for future emissions control measures in oxford, UK. Sustainability (Basel, Switzerland), 14(23), pp. 16182. Available: https://doi.org/10.3390/su142316182Search in Google Scholar

Taghvaeeyan, S., Rajamani, R. 2014. Portable roadside sensors for vehicle counting, classification, and speed measurement. IEEE Transactions on Intelligent Transportation Systems, 15(1), pp. 73-83. Available: https://doi.org/10.1109/TITS.2013.2273876Search in Google Scholar

Tirachini, A., Cats, O. 2020. COVID-19 and public transportation: Current assessment, prospects, and research needs. Journal of Public Transportation, 22(1), pp. 1-21. https://doi.org/10.5038/2375-0901.22.1.1Search in Google Scholar

Treiber, M., Kesting, A. 2017. The intelligent driver model with stochasticity-new insights into traffic flow oscillations. Transportation research procedia, 23, pp. 174-187. Available: https://doi.org/10.1016/j.trb.2017.08.012Search in Google Scholar

Tsvetkova, A., Kulkov, I., Busquet, C., Kao, P., Kamargianni, M. 2022. Implications of COVID-19 pandemic on the governance of passenger mobility innovations in europe. Transportation Research Interdisciplinary Perspectives, 14, pp. 100581. Available: https://doi.org/10.1016/j.trip.2022.100581Search in Google Scholar

Zeng, J., Tang, J. 2023. Modeling dynamic traffic flow as visibility graphs: A network-scale prediction framework for lane-level traffic flow based on LPR data. IEEE Transactions on Intelligent Transportation Systems, pp. 1-16. Available: https://doi.org/10.1109/TITS.2022.3231959Search in Google Scholar

Zhang, X., Xu, Y., Shao, Y. 2022. Forecasting traffic flow with spatial–temporal convolutional graph attention networks. Neural Computing & Applications, 34(18), pp. 15457-15479. Available: https://doi.org/10.1007/s00521-022-07235-zSearch in Google Scholar

Zhu, N., Liu, Y., Ma, S., He, Z. 2014. Mobile traffic sensor routing in dynamic transportation systems. IEEE Transactions on Intelligent Transportation Systems, 15(5), pp. 2273-2285. Available: https://doi.org/10.1109/TITS.2014.2314732Search in Google Scholar

Ye, P., Wen, D. 2017. Optimal traffic sensor location for origin-destination estimation using a compressed sensing framework. IEEE Transactions on Intelligent Transportation Systems, 18(7), pp. 1857-1866. Available: https://doi.org/10.1109/TITS.2016.2614828Search in Google Scholar