Dynamic Traffic Light System to Reduce The Waiting Time of Emergency Vehicles at Intersections within IoT Environment

Authors

  • Yahya Tashtoush Jordan University of Science and Technology, Jordan
  • Mohammed Al-refai Jordan University of Science and Technology, Jordan
  • Ghaith Al-refai German Jordanian University, Jordan
  • Dirar Abdul-Kareem Darweesh Jordan University of Science and Technology, Jordan
  • Noor Zaghal Jordan University of Science and Technology, Jordan
  • Omar Darwish Eastern Michigan University, Ypsilanti, USA

DOI:

https://doi.org/10.15837/ijccc.2022.3.4482

Keywords:

Dynamic traffic light, Emergency vehicle, Expert system, IoT, Waiting time, Intersections

Abstract

Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenarios

References

[1] Al-Khateeb, K.; Johari, J.A.Y. (2008). Intelligent dynamic traffic light sequence using RFID, In 2008 International Conference on Computer and Communication Engineering, pp. 1367-1372, IEEE, 2008. https://doi.org/10.1109/ICCCE.2008.4580829

[2] Ata, A.; Khan, M. A. ; Abbas, S.; Ahmad, G.; Fatima, A. (2019). Modelling smart road traffic congestion control system using machine learning techniques, Neural Network World 29, no. 2: 99-110, 2019. https://doi.org/10.14311/NNW.2019.29.008

[3] Blosseville, J. M. ; Krafft, C.; Lenoir, F.; Motyka, V.; Beucher, S. (1989). TITAN: A traffic measurement system using image processing techniques, In Second International Conference on Road Traffic Monitoring, pp. 84-88, IET, 1989.

[4] Collotta, M.; Pau, Giovanni.; Scatí , G. ; Campisi, T. (2014). A dynamic traffic light management system based on wireless sensor networks for the reduction of the red-light running phenomenon, Transport and Telecommunication 15, no. 1: 1-11, 2014. https://doi.org/10.2478/ttj-2014-0001

[5] Collotta, M.; Bello, L.L. ; Pau, G. (2015). A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers, Expert Systems with Applications 42, no. 13: 5403-5415, 2015. https://doi.org/10.1016/j.eswa.2015.02.011

[6] Cucchiara, R.; Massimo, P. ; Paola, M. (2000). Image analysis and rule-based reasoning for a traffic monitoring system, IEEE Transactions on Intelligent Transportation Systems 1, no. 2: 119-130, 2000. https://doi.org/10.1109/6979.880969

[7] Dangi, K.; Kushwaha, M.S. ; Bakthula, R. (2020). An Intelligent Traffic Light Control System Based on Density of Traffic, In Emerging Technology in Modelling and Graphics, pp. 741-752, Springer, Singapore, 2020. https://doi.org/10.1007/978-981-13-7403-6_65

[8] Fleck, J. L.; Cassandras, C.G. ; Geng, Y. (2015). Adaptive quasi-dynamic traffic light control, IEEE Transactions on Control Systems Technology 24, no. 3: 830-842, 2015. https://doi.org/10.1109/TCST.2015.2468181

[9] Frank, A.; Al Aamri, Y.S.K. ; Zayegh, A. (2019). IoT based smart traffic density control using image processing, In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1-4, IEEE, 2019. https://doi.org/10.1109/ICBDSC.2019.8645568

[10] George, A.A.; Krishna, A.; Dias, T.; Vargheese, A.S. ; Divya, R.S. (2017). Golden aid an emergency ambulance system, In 2017 International Conference on Networks and Advances in Computational Technologies (NetACT), pp. 473-476, IEEE, 2017. https://doi.org/10.1109/NETACT.2017.8076818

[11] Bilal, G.; ElKhatib, K.; Chahine, K. ; Kherfan, M. (2016). Smart traffic light control system, In 2016 third international conference on electrical, electronics, computer engineering and their applications (EECEA), pp. 140-145, IEEE, 2016.

[12] Goel, A.; Sukanya, R. ; Nidhi, C. (2012). Intelligent traffic light system to prioritized emergency purpose vehicles based on wireless sensor network, International Journal of Computer Applications 40, no. 12: 36-39, 2012. https://doi.org/10.5120/5019-7352

[13] Iwasaki, Y. (1997). An image processing system to measure vehicular queues and an adaptive traffic signal control by using the information of the queues, In Proceedings of Conference on Intelligent Transportation Systems, pp. 195-200, IEEE, 1997.

[14] Iyyappan, S. ; Nandagopal, V. (2013). Automatic accident detection and ambulance rescue with intelligent traffic light system, International journal of advanced research in electrical, electronics and instrumentation engineering 2, no. 4: 1319, 2013.

[15] Qinghui, L.; Kwan, B. W. ; Tung, L. J. (1997). Traffic signal control using fuzzy logic, In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 2, pp. 1644-1649, IEEE, 1997.

[16] Li, Y. ; Fan, X. (2003). Design of signal controllers for urban intersections based on fuzzy logic and weightings, In Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, vol. 1, pp. 867-871, IEEE, 2003.

[17] Memis, S.; Demir, E.; Karamasa, í‡.; Korucuk, S. (2020). Prioritization of road transportation risks: An application in Giresun province, Operational Research in En! gineering Sciences: Theory and Applications, 3(2), pp. 111-126, 2020. https://doi.org/10.31181/oresta2003111m

[18] Nellore, K. ; Hancke, G. (2016). Traffic management for emergency vehicle priority based on visual sensing, Sensors 16, no. 11:1892, 2016. https://doi.org/10.3390/s16111892

[19] Niittymí¤ki, J. (2001). Installation and experiences of field testing a fuzzy signal controller, European journal of operational research 131, no. 2:273-281, 2001. https://doi.org/10.1016/S0377-2217(00)00127-2

[20] Ozkurt, C. ; Camci, F. (2009). Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks, Mathematical and Computational Applications 14, no. 3: 187-196, 2009. https://doi.org/10.3390/mca14030187

[21] Rathore, M. M.; Anand, P.; Hong, W.; HyunCheol, S.; Imtiaz, A. ; Sharjil, S.(2018). Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data, Sustainable cities and society 40, 600-610, 2018. https://doi.org/10.1016/j.scs.2017.12.022

[22] Saraf, R. K. (1995). Adaptive traffic control using neural networks, , 4989-4989, 1995.

[23] Stojcic, M. (2018). Application of ANFIS model in road traffic and transportation: a literature review from 1993 to 2018, Operational Research in Engineering Sciences: Theory and Applications, 1(1), pp. 40-61, 2018. https://doi.org/10.31181/oresta19012010140s

[24] Sumi, L. ; Ranga, V. (2018). Intelligent traffic management system for prioritizing emergency vehicles in a smart city, International Journal of Engineering 31, no. 2: 278-283, 2018. https://doi.org/10.5829/ije.2018.31.02b.11

[25] Tzes, A.; McShane, W. R. ; Kim, S. (1995). Expert fuzzy logic traffic signal control for transportation networks, In 1995 Compendium of Technical Papers. Institute of Transportation Engineers 65th Annual Meeting. Institute of Transportation Engineers (ITE), 1995.

[26] Viriyasitavat, W. ; Tonguz, O.K. (2012). Priority management of emergency vehicles at intersections using self-organized traffic control, In 2012 IEEE Vehicular Technology Conference (VTC Fall), pp. 1-4, IEEE, 2012. https://doi.org/10.1109/VTCFall.2012.6399201

[27] Wen, W. (2008). A dynamic and automatic traffic light control expert system for solving the road congestion problem, Expert Systems with Applications 34, no. 4: 2370-2381, 2008. https://doi.org/10.1016/j.eswa.2007.03.007

[28] Yeshwanth, C.; Sooraj, P. S. A.; Sudhakaran, V. ; Raveendran, V. (2017). Estimation of intersection traffic density on decentralized architectures with deep networks, In 2017 International Smart Cities Conference (ISC2), pp. 1-6, IEEE, 2017. https://doi.org/10.1109/ISC2.2017.8090799

[29] Bhandari, R.; Rajasekhar, k. (2021). Priority- Mobility Aware Clustering ROutiNg (P - MACRON) Algorithm for Lifetime Improvement of comprehensive dynamic Wireless Sensor Network, International Journal of Computers Communications & Control, Volume: 16, Issue: 2,2021. https://doi.org/10.14569/IJACSA.2021.01202100

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Published

2022-03-21

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