Decentralized control of traffic signals with priority for ambulances

• Autorzy: Lewandowski M. , Płaczek B. , Bernas M.
• Języki publikacji: EN

Abstrakty

EN

In this paper delays and average travel times of vehicles are analyzed for various decentralized traffic control algorithms that can provide priority for ambulances. Decentralized control strategy is scalable and can be used in road networks where traffic lights are controlled autonomously for multiple intersections of different types. The experiments were performed in a realistic simulation model of complex road network, which is typical for European cities. It was shown that utilization of detailed traffic data from vehicular sensor network significantly improves the performance of signal control algorithms. After proper selection of algorithm parameters, the decentralized control strategy not only provides a quick transition of ambulances, but also has minimal effect on the delay of non-priority vehicles. Research for mesh road network organization has been performed in previous work [16].

Słowa kluczowe

EN

vehicular sensor networks; traffic signal control; priority vehicles

PL

sieci czujników ruchu; sterowanie ruchem drogowym; pojazdy uprzywilejowane

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2017
• Tom: Vol. 26
• Strony: 9--17
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Lewandowski M.

• University of Silesia in Katowice, Poland

autor

Płaczek B.

• University of Silesia in Katowice, Poland

autor

Bernas M.

• University of Bielsko-Biala, Poland

Bibliografia

• [1] AMRAM O., SCHUURMAN N., HAMEED S. M. Mass casualty modelling: a spatial tool to support triage decision making. International journal of health geographics, 2011, Vol. 10. BioMed Central, p. 40.
• [2] BERNAS M., WISNIEWSKA J. Quantum road traffic model for ambulance travel time estimation. Journal of Medical Informatics & Technologies, 2013, Vol. 22. pp. 257–264.
• [3] BULLOCK D., MORALES J. M., SANDERSON B. Impact of signal preemption on the operation of the virginia route 7 corridor. Proceedings of the 1999 ITS America Conference, 1999.
• [4] CHIU S., CHAND S. Self-organizing traffic control via fuzzy logic. Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on, 1993. pp. 1897–1902.
• [5] COOLS S.-B., GERSHENSON C., DHOOGHE B. Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 2013. Springer, pp. 45–55.
• [6] DAS C., TRIPATHY S. P. A review on virtualization in wireless sensor network. IJACTA, 2014, Vol. 1. pp. 028–034.
• [7] DEL ARCO E., MORGADO E., RAMIRO-BARGUEÑO J., MORA-JIMENEZ I., CAAMAÑO A. J. Vehicular sensor networks in congested traffic: Linking stv field reconstruction and communications channel. Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, 2011. pp. 606–613.
• [8] FISHER R., LASSA J. Interactive, open source, travel time scenario modelling: tools to facilitate participation in health service access analysis. International journal of health geographics, 2017, Vol. 16. BioMed Central, p. 13.
• [9] GERSHENSON C., ROSENBLUETH D. A. Modeling self-organizing traffic lights with elementary cellular automata. arXiv preprint arXiv:0907.1925, 2009.
• [10] GERSHENSON C., ROSENBLUETH D. A. Self-organizing traffic lights at multiple-street intersections. Complexity, 2012, Vol. 17. Wiley Online Library, pp. 23–39.
• [11] HE Q., HEAD K. L., DING J. Multi-modal traffic signal control with priority, signal actuation and coordination. Transportation Research Part C: Emerging Technologies, 2014, Vol. 46. Elsevier, pp. 65–82.
• [12] KANO T., SUGIYAMA Y., ISHIGURO A. Autonomous decentralized control of traffic signals that can adapt to changes in traffic. Collective Dynamics, 2016, Vol. 1. pp. 1–18.
• [13] KIM Y., LEE J. A secure analysis of vehicular authentication security scheme of rsus in vanet. Journal of Computer Virology and Hacking Techniques, 2016, Vol. 12. Springer, pp. 145–150.
• [14] KWON H.-Y., LEE M.-K. Fast signature verification with shared implicit certificates for vehicular communication. International Conference on Broadband and Wireless Computing, Communication and Applications, 2016. pp. 525–533.
• [15] LÄMMER S., HELBING D. Self-control of traffic lights and vehicle flows in urban road networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, Vol. 2008. IOP Publishing, p. P04019.
• [16] LEWANDOWSKI M., PŁACZEK B., BERNAS M. Self-organizing traffic signal control with prioritization strategy aided by vehicular sensor network. IFIP International Conference on Computer Information Systems and Industrial Management, 2017. pp. 536–547.
• [17] ŁUKASIK P., PIÓRKOWSKI A. Development of ambulance speed characteristics based on actual data. Studia Informatica, 2016, Vol. 37. pp. 113–124.
• [18] MOGHIMIDARZI S., FURTH P. G., CESME B. Predictive–tentative transit signal priority with self-organizing traffic signal control. Transportation Research Record: Journal of the Transportation Research Board, 2016, no. 2557. Transportation Research Board of the National Academies, pp. 77–85.
• [19] NOORI H., FU L., SHIRAVI S. A connected vehicle based traffic signal control strategy for emergency vehicle preemption. Tranportation Research Board 95th Annual Meeting, 2016, no. 16-6763.
• [20] NOORI H., VALKAMA M. Impact of vanet-based v2x communication using ieee 802.11 p on reducing vehicles traveling time in realistic large scale urban area. Connected Vehicles and Expo (ICCVE), 2013 International Conference on, 2013. pp. 654–661.
• [21] PŁACZEK B. A self-organizing system for urban traffic control based on predictive interval microscopic model. Engineering Applications of Artificial Intelligence, 2014, Vol. 34. Elsevier, pp. 75–84.
• [22] PLACZEK B. A cellular automata approach for simulation-based evolutionary optimization of self-organizing traffic signal control. Journal of Cellular Automata, 2016, Vol. 11.
• [23] REZTSOV A. Self-organising traffic lights (sotl) as an upper bound estimate. 2014.
• [24] REZTSOV A. Self-organising traffic lights (sotl) do not outperform sydney coordinated adaptive traffic system (scats). 2015.
• [25] SIMIĆ D., KOVAČEVIĆ I., SVIRČEVIĆ V., SIMIĆ S. Hybrid firefly model in routing heterogeneous fleet of vehicles in logistics distribution. Logic Journal of the IGPL, 2015, Vol. 23. Oxford University Press, pp. 521–532.
• [26] SIMS A. G., DOBINSON K. W. The sydney coordinated adaptive traffic (scat) system philosophy and benefits. IEEE Transactions on vehicular technology, 1980, Vol. 29. IEEE, pp. 130–137.
• [27] SMITH C. M., FRY H., ANDERSON C., MAGUIRE H., HAYWARD A. C. Optimising spatial accessibility to inform rationalisation of specialist health services. International journal of health geographics, 2017, Vol. 16. BioMed Central, p. 15.
• [28] VANDERSCHUREN M., MCKUNE D. Emergency care facility access in rural areas within the golden hour?: Western cape case study. International journal of health geographics, 2015, Vol. 14. BioMed Central, p. 5.
• [29] WONGPIROMSARN T., UTHAICHAROENPONG T., WANG Y., FRAZZOLI E., WANG D. Distributed traffic signal control for maximum network throughput. Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, 2012. pp. 588–595.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).