Choosing the Optimal Method to Provide Public Transportation Priority

Choosing the Optimal Method to Provide Public Transportation Priority

Anatoly Pistsov Dmitrii Zakharov

Department of Road Transport Operation, Industrial University of Tyumen, Russia

Available online: 
| Citation



The article proves that the traffic intensity of cars and buses is uneven both during the day and within 1 h. The dependences of the vehicles delay time when passing the intersection with five ways of providing the priority of public transport (PT) are given. The considered methods are divided into three groups: dedicated lane (passive), traffic light adaptive control (active priority) and combined options (combination of active and passive). To select the optimal method of priority in work, the users total delay time is used, taking into account the drivers and passengers time loss in private and public transport. An estimate of the total delay time was determined using the traffic simulation in PTV Vissim. Algorithms for adaptive control of a traffic light object were developed in the VisVap module.

The best way to grant priority is different for different traffic levels at an intersection. At low traffic intensities of cars and buses, the combined method (dedicated lane and “green extension”) is optimal. At high traffic intensities and a small number of passengers, the “green extension” becomes the best way. As the number of passengers on the bus increases, the effect of each method of granting PT priority changes to a different extent. So, at high traffic intensities, the combined method becomes optimal (dedicated lane and “green extension”).

Differentiation of the methods of providing the priority of PT in space and in time allows you to get the least loss of time for movement for each local section of the street and time period.


active priority, intelligent transportation systems, public transport priority, public transport, transport modelling


[1] Gandia, R.M., Antonialli, F., Oliveira, J.R., (...), Nicolai, I., Oliveira, I.R.C., Willingness to use MaaS in a developing country. International Journal of Transport Development and Integration, 5(1), pp. 57–68, 2021. DOI:10.2495/TDI-V5-N1-57-68.

[2] Dingil, A.E., Rupi, F., Stasiskiene, Z. A., Macroscopic analysis of transport networks: the influence of network design on urban transportation performance. International Journal of Transport Development and Integration, 3(4), pp. 331–343, 2019. DOI:10.2495/TDI-V3-N4-331-343.

[3] Tatum, K., Parnell, K., Cekic, T.I., Knieling, J., Driving factors of sustainable transportation: satisfaction with mode choices and mobility challenges in Oxfordshire and Hamburg. International Journal of Transport Development and Integration, 3(1), pp. 55–66, 2019.

[4] Díaz, G., Macià, H., Valero, V., Boubeta-Puig, J., Cuartero, F. An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets. Neural Computing and Applications, 32(2), pp. 405–426, 2020.

[5] Danilov, O.F., Kolesov, V.I., Sorokin, D.A., Gulaev, M.L., Study on the vehicle linear dynamic interval in a traffic flow. Communications – Scientific Letters of the University of Zilina, 23(1), pp. E11-E22, 2021. DOI:10.26552/COM.C.2021.1.E11-E22.

[6] Huan, N., Yao, E., Fan, Y., Wang, Z., Evaluating the environmental impact of bus signal priority at intersections under hybrid energy consumption conditions. Energies, 12(23), 4555, 2019. DOI:10.3390/en12234555.

[7] Stanley, J., SmartBus: a new service standard. Public Transport International, 55(6), pp. 28–31, 2006.

[8] Zhang, H., Liang, S., Han, Y., Ma, M., Leng, R., Pre-Control Strategies for Downstream Bus Service Reliability with Traffic Signal. IEEE Access, 8, 9165725, pp. 148853– 148864, 2020. DOI:10.1109/ACCESS.2020.3015982.

[9] Ghanbarikarekani, M., Qu, X., Zeibots, M., Qi, W., Minimizing the Average Delay at Intersections via Presignals and Speed Control. Journal of Advanced Transportation, 4121582, 2018. DOI:10.1155/2018/4121582.

[10] Wahlstedt, J., Impacts of bus priority in coordinated traffic signals. Procedia – Social and Behavioral Sciences, 16, pp. 578–587, 2011. DOI:10.1016/j.sbspro.2011.04.478.

[11] Novačko, L., Babojelić, K., Dedić, L., Rožić, T., Simulation-based public transport priority tailored to passenger conflict flows: a case study of the city of Zagreb. Applied Sciences (Switzerland), 11(11), 4820, 2021. DOI:10.3390/app11114820.

[12] He, H., Guler, S.I., Menendez, M., Adaptive control algorithm to provide bus priority with a pre-signal. Transportation Research Part C: Emerging Technologies, 64, pp. 28–44, 2016. DOI:10.1016/j.trc.2016.01.009.

[13] Sun, Y., Li, J., Wei, X., Jiao, Y., Tandem design of bus priority based on a pre-signal system. Sustainability (Switzerland), 13(18), 10109, 2021. 10.3390/su131810109.

[14] Fadyushin, A, Zakharov, D., Influence of the Parameters of the bus lane and the bus stop on the delays of private and public transport. Sustainability 12(22), 9593, 2020. DOI:10.3390/su12229593.

[15] Fadyushin, A., Zakharov, D., Karmanov, D., Estimation of the change in the parameters of traffic in the organization of the bus lane. Transportation Research Procedia, 36, pp. 166–172, 2018. DOI:10.1016/j.trpro.2018.12.059.

[16] Dumbliauskas, V., Grigonis, V., Vitkienë, J., Estimating the effects of public transport priority measures at signal controlled intersections. Baltic Journal of Road and Bridge Engineering, 12(3), pp. 187–192, 2017. DOI:10.3846/bjrbe.2017.23.

[17] Barthauer, M., Friedrich, B. Evaluation of a signal state prediction algorithm for car to infrastructure applications. Transportation Research Procedia, 3, pp. 982–991, 2014. DOI:10.1016/j.trpro.2014.10.078.

[18] Desta, R., Tóth, J., Simulating the performance of integrated bus priority setups with microscopic traffic mockup experiments. Scientific African, 11, e00707, 2021. DOI:10.1016/j.sciaf.2021.e00707.

[19] PTV AG, PTV Visum Manual. Available online: VISUM_18_ENG/ (accessed on 23 July 2020).