Determination of intercity passenger demand by means of a vehicle video surveillance system

Introduction. One of the current unresolved issues is to determine the potential (total, intensive) transport demand, i.e. the theoretical number of trips that are possible in the segment of the intercity bus transportation market on regular routes.
Materials and methods. The key information for the mathematical model of determining intercity transport demand is the volume of departures and arrivals between settlements. These statistics are available mainly for public transport. Information on overall transport demand, as a rule, is unavailable. For the road network, departure volumes are often calculated as a certain percentage of the total population.
Currently, methods of studying transport demand based on the collection, integration and analysis of large and heterogeneous data generated by various sources in the spheres of human activities represent a promising perspective. Within this approach, the application of information from a vehicle video surveillance system for tracking the intercity traffic flows is being considered. The article formulates a system of equations linking transport communications between settlements and the passenger flow being considered, which is divided into incoming, outgoing, and transit.
When using video surveillance of a vehicle in calculations with the use of the proposed correction factor, we take into account the effect associated with the movement of the observer in space, which is as follows: the objects taken into account by the moving observer will be available to the stationary observer after a period of time, which can be defined as the ratio of the distance between the observers to the speed of the object.
Results. The proposed in the article approach for tracking transport and passenger flows has been tested on an isolated transport corridor, along which passengers travel by road (scheduled and chartered buses, cars), and other modes of transport (e.g., rail) are absent.
Discussion and conclusion. The coefficient of determination of the obtained mathematical model allows us to conclude that it is acceptable, 18% of the variation in the dependent variable can be attributed to unknown, hidden parameters or statistical errors in the initial data.

1. Woldeamanuel M. Evaluating the competitiveness of intercity buses in terms of sustainability indicators. Journal of Public Transportation. 2012; Vol. 15, No. 3: 5. DOI:10.5038/2375-0901.15.3.5

2. Javid R., Sadeghvaziri E. Investigating the Relationship Between Access to Intercity Bus Transportation and Equity. Transportation Research Record, 2022, p. 03611981221088218. DOI: 10.1177/03611981221088218

3. Group K. Effective Approaches to Meeting Rural Intercity Bus Transportation Needs. Report 79, Transit Cooperative Research Program. Transportation Research Board, National Research Council, Washington, D.C., 2002. 184 p. DOI: 10.5038/2375-0901.15.3.7

4. Nielsen G., Lange T. Network design for public transport success–theory and examples. Norwegian Ministry of Transport and Communications, Oslo, 2008. 30 p.

5. Ryan F., Allard R. F., Moura F., The Incorporation of Passenger Connectivity and Intermodal Considerations in Intercity Transport Planning, Transport Reviews, 2015, DOI: 10.1080/01441647.2015.1059379

6. Ortuzar J.D., Willumsen L.G. Modelling transport John Willey & Sons, 2011. 586 p. DOI: 10.1002/9781119993308

7. Alderighi M., Cento, A., Nijkamp, P., Rietveld, P. Network competition – the coexistence of hub-and-spoke and point-to-point systems. Journal of Air Transport Management. 2005; 11(5): 328–334. DOI:10.1016/j.jairtraman.2005.07.006

8. Alderighi M, Feder C, Nijkamp P, Ungureanu EI Simple pricing rules in complex air transport systems. Handbook on Entropy, Complexity and Spatial Dynamics: A Rebirth of Theory? Chapter 18. 2021: pp. 304 – 320 DOI: 10.4337/9781839100598.00027

9. Merrina A., Sparavigna, A., Wolf, R. A. The intermodal networks: A survey on intermodalism. World Review of Intermodal Transport Research. 2007; 1(3): 286–299.

10. Ranjbari A., Hickman M.,·Chiu YC. A Mathematical Optimization Model for Solving the Intercity Transit Network Design Problem // CASPT 2018 Extended Abstract. Available at: http://www.caspt.org/wp-content/uploads/2018/10/Papers/CASPT_2018_paper_128.pdf. (accessed 25th May 2025)

11. Sunhyung Yoo, Jinwoo Brian Lee, and Hoon Han. A reinforcement learning approach for bus network design and frequency setting optimization. Public Transport. 2023: pp 1–32. DOI: 10.1007/s12469-022-00319-y

12. Korjagin M.E., Chistjakov A.S. Baza dannyh dlja opisanija rynka mezhdugorodnyh passazhirskih pere-vozok [Long Distance Passenger Market Description Database]. Vestnik Sibirskogo gosudarstvennogo universiteta putej soobshhenija. 2021; 1(56): 38–45. (in Russ.) DOI: 10.52170/1815-9262-2021-56-38

13. Makarova E.A., Elizarov S.B., Muktepavel S.V. Avtomatizirovannaja sistema prognozirovanija passazhirskih transportnyh potokov na baze ASU «Jekspress» [Automated system for forecasting passenger traffic flows based on ACS “Ex-press”]. Vestnik nauchno-issledovatel’skogo insti-tuta zheleznodorozhnogo transporta. 2011; 4: 21–27. (in Russ.) – EDN NYHLZT.

14. Grosche Tobias , Rothlauf, Franz, Heinzl, Armin. Gravity models for airline passenger volume estimation. Journal of Air Transport Management. 2007. 13. 175-183. DOI: 10.1016/j.jairtraman.2007.02.001

15. Birolini S., Pais A. A, Cattaneo M., Paleari S. Integrated flight scheduling and fleet assignment with improved supply-demand interactions. Transportation Research Part B: Methodological. 2021. 149. 162-180. DOI: 10.1016/j.trb.2021.05.001

16. Shtotskaya A.A., Mikhailov A.Yu. Assessment of transport mobility of the population on the basis of disaggregated models. Bulletin of Irkutsk State Technical University. 2017. Vol. 21. No. 5. P. 199–207. (in Russ.) DOI: 10.21285/1814-3520-2017-5-199-207

17. Ibarra-Rojas, O., J., Delgado, R. Giesen, and J.C. Muñoz. Planning, Operation, and Control of Bus Transport Systems: A Literature Review. Transportation Research Part B: Methodological. 2015. 77: 38-75. DOI: 10.1016/j.trb.2015.03.002

18. Grigorova T., Davidich Yu., Dolya V. Assessment of Elasticity of Demand for Services of Suburban Road Passenger Transport . Technology Audit and Production Reserves. 2015. Vol. 3, No 2. Р. 13–16. DOI: 10.15587/2312-8372.2015.44768/

19. Wang Sen, Gao Yi. A literature review and citation analyses of air travel demand studies published between 2010 and 2020. Journal of Air Transport Management. 2021. 79. 102135. DOI: 10.1016/j.jairtra-man.2021.102135

20. Lu M., Zhu H., Luo X., Lei, L. Intercity travel demand analysis model. Advances in Mechanical Engineering, 2015. 6. DOI: 10.1155/2014/108180

21. N. Dike, Declan, Ejem Ejem, Erumaka Onyinyechi, Chukwu, Oluchi, Ibe, Callistus. Estimation of inter-city travel demand for public road transport in Nigeria. Journal of Sustainable Development of Transport and Logistics. 2018. 3. 88-98. DOI:10.14254/js-dtl.2018.3-3.7.

22. Chistyakov A., Koryagin M. Interurban Travel Mode Choice ModelWhich Based on Departures Frequency and Passengers’ Preferences. InternationalScientific Siberian Transport Forum. Springer, Cham, 2021. P. 964-973.

23. Jingxu Chen, Zhiyuan Liu, Senlai Zhu, Wei Wang. Design of limited-stop bus service with capacity constraint and stochastic travel time. Transportation Research Part E: Logistics and Transportation Review. 2015; Volume 83: 1-15. DOI: 10.1016/j.tre.2015.08.007

24. Dolya C.V. Gravity Model Formalization for Parameter Calculation of Intercity Passenger Transport Correspondence. Science аnd Technique. .2017. 16 (5), 437–443. (in Russ.) DOI: 10.21122/2227-1031-2017-16-5-437-443

25. Nurminskij E. A., Pugachev I. N., Shamraj N. B., Sedjukevich V. N. Opredelenie passazhiropotokov v regional’noj transportnoj sisteme na osnove modificirovannyh gravitacionnyh modelej [Determination of passenger traffic in the regional transport system on the basis of modified gra-vital models]. Nauka i tehnika. 2015; 5. 2015: 39 – 45. (in Russ.) EDN UMFMTL.

26. Gorbachev P.F., Krikun V.I. Modelirovanie sprosa na perevozku passazhirov v prigorodnom soobshhenii [Modeling Demand for Commuter Transportation]. VEZhPT. 2013; 3(62):12–15. (in Russ.) EDN QAJHHZ.

27. Fadeev A.I., Ilyankov A.M. Transport supply management on regular intercity bus lines. The Russian Automobile and Highway Industry Journal. 2023;20(5):632-648. (In Russ.) DOI: 10.26518/2071-7296-2023-20-5-632-648. EDN: WMBMDI

28. Kinene A., Birolini S. Optimization of subsidized air transport networks using electric aircraft. Transportation Research Part B Methodological. 2024. 190. DOI: 103065. 10.1016/j.trb.2024.103065

29. Grosche T., Rothlauf F., Heinzl A. Gravity models for airline passenger volume estimation. Journal of Air Transport Management. 2007. 13. 175-183. DOI: 10.1016/j.jairtraman. 2007.02.001

30. Azemsha S.A., Skirkovskiy S.V., Gorev A.E. Determining the regularities in the change of the passenger traffic volume from the number of inhabitants of the settlement. Bulletin of Civil Engineers, 2019, no. 5 (76), pp. 206-216. – EDN KFKQMX. (in Russ.)

31. Zulkarnain A., Pasaribu H., Sembiring J. An Estimating Air Travel Demand in North Sumatra Using Gravity Model Approach with Economic and Route Analysis. Langit Biru: Jurnal Ilmiah Aviasi. 2025. 18. 1-12. DOI: 10.54147/langitbiru.v18i1.1284

32. Akulov V.V. Analysis of the accounting methods of traffic on the roads. Naukovedenie. 2012;(4): 1ТРГСУ412. Available at: https://naukovedenie.ru/PD-F/1trgsu412.pdf (accessed 25th May 2025). (In Russ., abstract in Eng.).

33. Khusainov R.M., Talipov N.G., Kataev A.S., Shalaeva D.V. Intelligent traffic flow analysis system in automated traffic management systems. Software products and systems. 2024. №1. URL: https://cy-berleninka.ru/article/n/intellektualnaya-sistema-anali-za-transportnyh-potokov-v-avtomatizirovannyh-siste-mah-upravleniya-dorozhnym-dvizheniem (accessed 31th May 2025). (in Russ.)

34. Zheng Y., Capra L., Wolfson O., Yang H. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., vol. 5, no. 3 pp. 1–55, Sep. 2014 DOI: http://dx.doi.org/10.1145/2629592

35. Chen M., Mao Sh., Zhang Y., Leung Victor C.M. Big Data. Related Technologies. Challenges, and Future Prospects, Spinger, 2014. 100 p.

36. Tregubov V.N. Mobile Data Usage in Urban Transport Research. Transportation Systems and Technology. 2020;6(2):20-33. (in Russ.) DOI: 10.17816/transsyst20206220-33

37.