Intelligent Transport Systems in urban conditions is one of the solutions to reduce congestion of vehicles and the amount of harmful emissions. An important component of ITS is the assessment of the duration of a public transport trip. It is necessary to focus on the study of the duration of the bus (the duration of traffic between stops and the dwelling time). In this paper, the authors focused on determining the dependence of the duration of buses at stops depending on the demand of passengers. The dwelling time of buses at stops is not considered independent of the duration of the journey. The duration of the bus is the periods of time when the buses wait at the stops, and the travel time, which is the duration of the bus between each two stops. The study was conducted on the bus route #3A in Lviv. To determine the dwelling time of the bus at stops, it is necessary to take into account information about passengers and the trajectory of buses. The obtained data can increase the accuracy of forecasting in different traffic situations in comparison with the most modern methods.
1. Berezny R. & Konecny V. (2017). The impact of the quality of transport services on passenger demand in the suburban bus transport. Elsevier, #192. - P. 40-45. (in Ukrainian).
https://doi.org/10.1016/j.proeng.2017.06.007
2. Gurmu Z., Fan W. (2014). Artificial neural network travel time prediction model for buses using only gps data. Public Transport. 17, P.3. (in English).
https://doi.org/10.5038/2375-0901.17.2.3
3. Yang M., Chen C., Wang L., Yan X., Zhou L. (2016). Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World 26. 205 P. (in English).
https://doi.org/10.14311/NNW.2016.26.011
4. Bai C., Peng Z., Lu Q., Sun J. (2015). Dynamic bus travel time prediction models on road with multiple bus routes. Comput. intell. Neurosci. P. 63. (in English).
https://doi.org/10.1155/2015/432389
5. Xu H., Ying J. (2017). Bus arrival time prediction with real-time and historic data. Clust. Comput. 20. P. 3099-3106. (in English).
https://doi.org/10.1007/s10586-017-1006-1
6. Zhou M., Wang D., Li Q., Yue Y., Tu W., Cao R. (2017). Impacts of weather on public transport ridership: Results from mining data from different sources. Transport. Res. Part C: Emerg. Technol. 75. P. 17-29. (in English).
https://doi.org/10.1016/j.trc.2016.12.001
7. Cheng Z., Chow M., Jung D., Jeon J. (2017). A big data based deep learning approach for vehicle speed prediction. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE). IEEE. P. 389-394. (in English).
https://doi.org/10.1109/ISIE.2017.8001278
8. Ma Z., Koutsopoulos H., Ferreira L., Mesbah M. (2017). Estimation of trip travel time distribution using a generalized markov chain approach. Transport. Res. Part C: Emerg. Technol. 74. P. 1-21. (in English).
https://doi.org/10.1016/j.trc.2016.11.008
9. Kumar B., Vanajakshi L., Subramanian S. (2017). Bus travel time prediction using a time-space discretization approach. Transport. Res. Part C: Emerg. Technol. 79. P. 308-332. (in English).
https://doi.org/10.1016/j.trc.2017.04.002
10. Brakewood C., Macfarlane G., Watkins K. (2015). The impact of real-time information on bus ridership in new york city. Transport. Res. Part C: Emerg. Technol. 53. P. 59-75. (in English).
https://doi.org/10.1016/j.trc.2015.01.021
11. Rahman M., Wirasinghe S., Kattan L. (2018). Analysis of bus travel time distributions for varying horizons and real-time applications. Transport. Res. Part C: Emerg. Technol. 86. P. 453-466. (in English).
https://doi.org/10.1016/j.trc.2017.11.023
12. Comi A., Nuzzolo A., Brinchi S. and Verghini R. (2017). Bus dispatching irregularity and travel time dispersion. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). pp. 856-860. doi: 10.1109/MTITS.2017.8005632 (in English).
https://doi.org/10.1109/MTITS.2017.8005632
13. Fusco G., Colombaroni C. and Isaenko N. (2016). Short-term speed predictions exploiting big data on large urban road networks. In: Transportation Research Part C: Emerging Technologies 73, pp.183-201. (in English).
https://doi.org/10.1016/j.trc.2016.10.019
14. Comi A., Nuzzolo A., Brinchi S., Verghini R. (2017). Bus travel time variability: some experimental evidences. Transportation Research Procedia 27, pp. 101-108. (in English).
https://doi.org/10.1016/j.trpro.2017.12.072
15. He P., Jiang G., Lam S. and Tang D. (2019). Travel-Time Prediction of Bus Journey With Multiple Bus Trips. IEEE Trans. on Intelligent Transportation Systems. (in English).
https://doi.org/10.1109/TITS.2018.2883342
16. Moreira-Matias L., Mendes-Moreira J., de Sousa J., Gama J. (2015). Improving Mass Transit Operations by Using AVL-Based Systems: A Survey. IEEE Transactions on Intelligent Transportation System, doi: 10.1109/TITS.2014.2376772. (in English).
https://doi.org/10.1109/TITS.2014.2376772
17. Shalaby A. and Farhan A. (2003). Bus travel time prediction model for dynamic operations control and passenger information systems. Transp. Research Board 2. (in English).
18. Cats O. (2019). Determinants of bus riding time deviations: Relationship between driving patterns and transit performance. Journal of Transportation Engineering Part A: Systems 145(1),04018078. (in English).
https://doi.org/10.1061/JTEPBS.0000201
19. Hyndman R. and Athanasopoulos G. (2018). Forecasting: principles and practice. Second edition. www.otexts.org. (in English).