Journal of Transportation Research

Journal of Transportation Research

Modeling the spread of Corona virus in order to improve the public transportation

Document Type : Original Article

Authors
Alireza Absalan 1
Alireza sarkar 2
1 M.Sc., Student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
10.22034/tri.2025.320919.2994
Abstract
The appearance of new infectious diseases has become a serious global problem. Public transportation networks are an important factor in the rapid spread of such diseases. In this study, investigation of the spread of COVID-19 epidemic virus is done using dynamic SIR (Susceptible – Infected – Recovered) mathematical model with MATLAB. In addition, we studied accurate statistics of infected cases and deaths due to COVID-19 for the time intervals in various transportations in Iran, for instance, the transportation via rail, air and road transportation in specific time periods from Tehran city to Mashhad city also Tehran to Shiraz. Average of the Reproductive number (R0) COVID-19 in Farvardin and Ordibehesht months of 1399 also 1400 for Tehran to Mashhad is about 2.10 for rail transportation, 2.11 for air transportation and 2.16 for road transportation also for Tehran to Shiraz is about 2.09 for rail transportation, 2.10 for air transportation and 2.12 for road transportation. In each transportation type, the modelling is done and the comparison based on the statistics of deaths of passengers from Tehran to Mashhad and Shiraz shows the risks of outbreak and infection in road transportation are higher than air and rail transportation and the risks of outbreaks and infection in air transportation are higher than rail transportation. This results indicate that rail transport in safer in terms of the prevalence of COVID-19 and the contamination from air and road transportation.
Keywords
Public Transportation
Epidemic Virus Modeling
COVID-19
SIR Mathematical Model
Reproductive Number
-فرزام منش، مجتبی(1399)، بررسی چالش­های ویروس کرونا در ناحیه شهری، کنفرانس بین المللی عمران در ایران.
-Antrim, Aaron (2020). Automated passenger counter. Transitwiki.  https://www.transitwiki.org/TransitWiki/index.php/Automated_passenger_counter
-Brauer, Fred (2017). Mathematical epidemiology: Past, present, and future. Journal of Infectious Disease Modelling. 2(2). 113-127.
-Cartenì ,Armando; Di Francesco Luigi ; Maria, Martino (2021). The role of transport accessibility within the spread of the Coronavirus pandemic in Italy. Journal of Safety Science.133. 104999
-Chappell, Bill; Romo, Vanessa (2020). Coronavirus: All 50 States Report Cases, South America Has Nearly 1,000 Cases. NPR. https://www.npr.org/sections/health-shots.
-Chong, Ka Chun; Ying Zee, Benny Chung (2012). Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus. BMC Infectious Diseases .12. 309
-d'Onofrio, Alberto; Manfredi, Piero; Salinelli, Ernesto (2015). Dynamic behavior of a discrete-time SIR model with information dependent vaccine uptake. Journal of Difference Equations and Applications. 22(3). 485–512.
-Fang, Hanming; Wang, Long; Yang, Yang (2020). Human Mobility Restrictions and the Spread of the Novel Coronavirus in China. Journal of Public Economics. 191. 104272
-Ferguson, Neil, Cummings, Derek A. Fraser, Christophe, Cajka, James, Cooley, Philip, Burke, Donald (2006). Strategies for mitigating an influenza pandemic. Nature. 442(7101), 448–452.
-George, Noel; Tyagi, Naresh; Prasad, Jang Bahadur (2021). COVID-19 pandemic and its average recovery time in Indian states. Clinical Epidemiology and Global Health. 11.100740
-Hamdy, Youssef; Nejat, Alghamdi; Magdy A. Ezzat; Alaa A. El-Bary (2020). A modified SEIR model applied to the data of COVID-19 spread in Saudi Arabia. AIP Advances. 10.  125210
-Hart, Kim (2020). Public transit's death spiral. AxiosWebsite.https://www.axios.com
-Horni, Andreas; Nagel, Kai; Axhausen, Kay (2016). The Multi-Agent Transport Simulation MATSim; Ubiquity Press: London, UK. 618-619.
-Koziol, Kamil; Stanislawski, Rafal; Bialic, Grzegorz (2020). Fractional-Order SIR Epidemic Model for Transmission of COVID-19. Applied Sciences. 10(23).8316.
- Li­, Tianyi (2020). Simulating the Spread of Epidemics in China on the Multi-layer Transportation Network: Beyond the Coronavirus in Wuhan. IOP Science. 130. 4
-Li, Jian; Xiang, Tao; He, Linghui (2020). Modeling epidemic spread in transportation network. Journal of Traffic And Transportation Engineering. 8(2). 139-152.
-Lounis, Mohamed; Kumar, Dilip (2020). Estimation of SIR model’s parameters of COVID-19 in Algeria. Bulletin of the National Research Centre. 44(180)
-McNally, Tadgh (2020). Face shields are now an acceptable face covering on all public transport. TheJournal.ie.
-Perez, Liliana; Dragicevic, Suzana (2009). An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics. 8. 1–17.
-Prem, Kiesha; Liu, Yang; Russell, Timothy (2020)­. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet Public Health. 5(5). 261-270.
-Ryan, Órla (2020). Half of Dublin Bus passengers are wearing face masks but compliance on transport varies widely. TheJournal.ie. https://www.thejournal.ie/face-masks-on-public-transport
-Sinha Dutta, Sanchari (2020). Covid-19 incubation period. News-Medical.
-Smieszek, Timo, Balmer, Michael, Hattendorf, Jan, Axhausen, Kay, Zinsstag, Jakob, Scholz, Roland (2011). Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model. BMC Infectious Diseases. 11.115.
-Tirachini, Alejandro (2020). COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs. Journal of Public Transportation, 22(1).
-Wan, Liyang (2020)­. The high-speed railway network and the cross-town transmission of epidemics: evidence from Chinese COVID-19. Lab. Rev. Lab. Econ. Ind. Relat. 37–48.
-Zhu, Pengyu; Guo, Yuqing (2021). The role of high-speed rail and air travel in the spread of COVID-19 in China. Journal of Travel Medicine and Infectious Disease. 42. 102097