Prioritizing Vehicles that Carry Important Characters (Political) When Crossing Signalized Intersections

Document Type : Original Article


1 Department of Information Technology, Imam Hosein Comprehensive University, Tehran, Iran.

2 Department of Security, Imam Hosein Comprehensive University, Tehran, Iran.


In the urban transportation network as the traffic signals went green at the intersection of the upper hand, a group of vehi-cles move together and arrive at the next intersection, almost in group. If, at the same time as the group arrives, the signal of the corresponding route at this intersection is green, the total delay and stop of the vehicles will be significantly reduced and the intersection efficiency will increase significantly. The same strategy was implemented on the political vehicles in the study, so that the delay and stop time for them could be reduced. In this study, part of the political vehicle route from Saad-Abad Palace to the presidential office on Pasteur Street is considered. In this study, various strategies were developed to prioritize the vehicles in the Aimsun simulator software. Then, to detect the arrival of these vehicles to the intersection, two identifiers were embedded, one before the intersection and the other after it was installed. Among the results of this study are the following: There is an average increase in the average travel time for a scenario with an extra green time of 10 se-conds and 15 seconds. The average delay time was 7 seconds for the additional green time scenario of 10 seconds and the average delay of 6 seconds for the sub-scenario of 15 seconds increased. The average number of stops per vehicle in-creased by 0.1 stops per vehicle in both cases.


Main Subjects

1. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM. Election forecasts with Twitter: How 140 characters reflect the political landscape. Social science computer review. 2011;29(4):402-18.
2. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM. Predicting elections with twitter: What 140 characters reveal about political sentiment. Icwsm. 2010;10(1):178-85.
3. Bigdeli Rad H, Bigdeli Rad V. A Survey on the Rate of Public Satisfaction about Subway Facilities in the City of Tehran Using Servqual Model. Space Ontology International Journal. 2018;7(1):11-7.
4. Albagul A, Hrairi M, Hidayathullah M. Design and development of sensor based   traffic   light   system.   American   Journal   of   Applied   Sciences. 2006;3(3):1745-9.
5. Myr D. Multi-objective optimization for real time traffic light control and navigation systems for urban saturated networks. Google Patents; 2015.
6. Chiu S, Chand S, editors. Adaptive traffic signal control using fuzzy logic. Fuzzy  Systems,  1993,  Second  IEEE  International  Conference  on;  1993: IEEE.
7. Gradinescu V, Gorgorin C, Diaconescu R, Cristea V, Iftode L, editors. Adaptive traffic lights using car-to-car communication. Vehicular Technology Conference, 2007 VTC2007-Spring IEEE 65th; 2007: IEEE.
8. Yousef KM, Al-Karaki MN, Shatnawi AM. Intelligent traffic light flow control system using wireless sensors networks. J Inf Sci Eng. 2010;26(3):753-68.
9. Abdi A, Bigdeli Rad H, Azimi E, editors. Simulation and analysis of traffic flow for traffic calming. Proceedings of the  Institution of  Civil Engineers- Municipal Engineer; 2016: Thomas Telford Ltd.
10. Zou F, Yang B, Cao Y, editors. Traffic light control for a single intersection based on wireless sensor network. Electronic Measurement & Instruments,2009 ICEMI'09 9th International Conference on; 2009: IEEE.
11. Shaker H, Bigdeli Rad H. Evaluation and Simulation of New Roundabouts Traffic Parameters by Aimsun Software. Journal of Civil Engineering and Materials Application. 2018;2(3):146-58.
12. Robertson, M. T. (1994). U.S. Patent No. 5,345,232. Washington, DC: U.S. Patent and Trademark Office.
13. Ginsberg, M. L., Austin, M. M., Chang, P. A., & Mattison, S. C. (2011). U.S. Patent Application No. 12/639,770.
14.  Tlig  M,  Bhouri  N.  A  multi-agent system  for  urban  traffic  and  buses regularity control. Procedia-Social and Behavioral Sciences. 2011;20:896-905.
15. Bhouri N, Haciane S, Balbo F, editors. A multi-agent system to regulate urban traffic: Private vehicles and public transport. Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on; 2010: IEEE.
16. Liu H, Skabardonis A, Li M. Simulation of transit signal priority using the
NTCIP architecture. Journal of Public Transportation. 2006;9(3):7.
17. Baker RJ, Collura J, Dale JJ, Head L, Hemily B, Ivanovic M, et al. An overview of transit signal priority. 2002.
18. Theil H. Applied economic forecasting. 1971.
19. Pindyck Robert S, Rubinfeld DL. Econometric Models and Econometric Forecasts. Boston: Irwin McGraw-Hill; 1998.
20. SUN C, XU J. Study on Traffic Signal Timing Optimization for Single Point Intersection Based on Synchro Software System [J]. Journal of Highway and Transportation Research and Development. 2009;11:025.