New Solutions Based On Wireless Networks For Dynamic Traffic Lights Management: A Comparison Between IEEE 802.15.4 And Bluetooth

Open access

Abstract

The Wireless Sensor Networks are widely used to detect and exchange information and in recent years they have been increasingly involved in Intelligent Transportation System applications, especially in dynamic management of signalized intersections. In fact, the real-time knowledge of information concerning traffic light junctions represents a valid solution to congestion problems. In this paper, a wireless network architecture, based on IEEE 802.15.4 or Bluetooth, in order to monitor vehicular traffic flows near to traffic lights, is introduced. Moreover, an innovative algorithm is proposed in order to determine dynamically green times and phase sequence of traffic lights, based on measured values of traffic flows. Several simulations compare IEEE 802.15.4 and Bluetooth protocols in order to identify the more suitable communication protocol for ITS applications. Furthermore, in order to confirm the validity of the proposed algorithm for the dynamic management of traffic lights, some case studies have been considered and several simulations have been performed.

1. Sánchez, N.; Alfonso, J.; Torres, J.; Menéndez, J.M. (2013) ITS-based cooperative services development framework for improving safety of vulnerable road users. Intelligent Transport Systems, IET, vol.7, no.2, pp. 236-243.

2. Kothuri, S.M.; Tufte, K.; Soyoung Ahn; Bertini, R.L. (2006) Development of an ITS data archive application for improving freeway travel time estimation. IEEE Conference on Intelligent Transportation Systems (ITSC '06), pp.1263-1268.

3. Rommerskirchen, C.; Helmbrecht, M.; Bengler, K. (2013) Increasing complexity of driving situations and its impact on an ADAS for anticipatory assistance for the reduction of fuel consumption. IEEE Intelligent Vehicles Symposium (IV), pp.573-578.

4. Fei-Yue Wang (2010) Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications. IEEE Transactions on Intelligent Transportation Systems, vol.11, no.3, pp.630-638.

5. Skordylis, A; Trigoni, N. (2011) Efficient Data Propagation in Traffic-Monitoring Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, vol.12, no.3, pp.680-694.

6. Eng-Han Ng; Su-Lim Tan; Guzman, J.G. (2008) Road traffic monitoring using a wireless vehicle sensor network. International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2008), pp.1-4.

7. Gallart, V.; Felici-Castell, S.; Delamo, M.; Foster, Andrew; Perez, J.J. (2011) Evaluation of a Real, Low Cost, Urban WSN Deployment for Accurate Environmental Monitoring. IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS), pp.634-639.

8. Bee-Lie Chai; Ki-One Yi; Hyotaek Lim; Gwang-Hoon Kwark (2010) The implementation and performance evaluation of link power control in wireless sensor network. 2nd International Conference on Computer Engineering and Technology (ICCET), vol.6, pp.V6-245, V6-248.

9. Philipp, F.; Ping Zhao; Samman, F.A; Glesner, M.; Dassanayake, K.B.; Maheswararajah, S.; Halgamuge, S. (2012) Adaptive wireless sensor networks powered by hybrid energy harvesting for environmental monitoring. IEEE 6th International Conference on Information and Automation for Sustainability (ICIAfS), pp.285-289.

10. Pileggi, S.F.; Palau, C.E.; Esteve, M. (2010) Multimode WSN: Improving Robustness, Fault Tolerance and Performance of Randomly Deployed Wireless Sensor Network. Second International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp.112-117.

11. Alazzawi, L.K.; Elkateeb, AM.; Ramesh, A (2008) Scalability Analysis for Wireless Sensor Networks Routing Protocols. 22nd International Conference on Advanced Information Networking and Applications – Workshops (AINAW), pp.139-144.

12. Collotta, M.; Lo Bello, 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, Vol. 42, Issue 13, pp. 5403-5415.

13. Huck, R.C.; Havlicek, J.P.; Sluss, J.J.; Stevenson, AR. (2005) A low-cost distributed control architecture for intelligent transportation systems deployment in the State of Oklahoma. IEEE Proceedings Intelligent Transportation Systems, pp.919-924.

14. 802.15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR- WPANs) – June 2006 IEEE standard for information technology. Part 15.4.

15. Bluetooth Specification Version 4.0; Bluetooh SIG 2010.

16. Mandal, K.; Sen, A; Chakraborty, A; Roy, S.; Batabyal, S.; Bandyopadhyay, S. (2011) Road traffic congestion monitoring and measurement using active RFID and GSM technology, 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1375-1379.

17. Zotos, N.; Stergiopoulos, C.; Anastasopoulos, K.; Bogdos, G.; Pallis, E.; Skianis, C. (2012) Case study of a dimmable outdoor lighting system with intelligent management and remote control, International Conference on Telecommunications and Multimedia (TEMU), pp.43-48.

18. Megalingam, Rajesh Kannan; Mohan, V.; Mohanan, A; Leons, P.; Shooja, R. (2010) Wireless Sensor Network for Vehicle Speed Monitoring and Traffic Routing System, 2nd International Conference on Mechanical and Electrical Technology (ICMET), pp.631-635.

19. Brahmi, H.I; Djahel, S.; Murphy, J. (2013) Improving emergency messages transmission delay in road monitoring based WSNs, 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), pp.1-8.

20. Collotta, M.; Pau, G.; Salerno, V.M.; Scatà, G. (2012) A Novel Road Monitoring Approach Using Wireless Sensor Networks, Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp.376-381.

21. Collotta, M.; Messineo, A.; Nicolosi, G.; Pau, G. (2014) A self-powered Bluetooth Network for Intelligent Traffic light Junction Management, WSEAS Transactions on Information Science and Applications, Vol. 11, pp. 12-23.

22. Tsubota, T.; Bhaskar, A.; Chung, E.; Billot, R. (2011) Arterial traffic congestion analysis using Bluetooth Duration data, Australasian Transport Research Forum Proceedings, pp. 1-14.

23. Jie, L.; van Zuylen, H.; Chunhua, L.; Shoufeng, L. (2011) Monitoring travel times in an urban network using video, GPS and Bluetooth, Procedia of Social and Behavioral Sciences, pp. 630-637.

24. Araghi, B.N.; Christensen, L.T.; Lahrmann, H. (2013) Accurate Travel Time Estimation and Incident Detection Using Bluetooth and WiFi Technologies, Best Practices on Development of Monitoring Technologies and Services, VTT Technical Research Centre of Finland.

25. Bachmann, C.; Roorda, M.J.; Abdulhai, B.; Moshiri, B. (2013) Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation, Intelligent Transportation Systems, Vol. 17, no. 2, pp. 153-164.

26. Collotta, M.; Pau, G.; Scatà, G. (2013) Deadline-aware scheduling perspectives in industrial wireless networks: A comparison between IEEE 802.15.4 and Bluetooth, International Journal of Distributed Sensor Networks, 2013, art. no. 602923.

27. Collotta, M.; Lo Bello, L.; Mirabella, O. (2007) Deadline-Aware Scheduling Policies for Bluetooth Networks in Industrial Communications, International Symposium on Industrial Embedded Systems (SIES), pp.156,163.

28. Collotta, M.; Lo Bello, L.; Mirabella, O. (2010) An innovative frequency hopping management mechanism for Bluetooth-based industrial networks, International Symposium on Industrial Embedded Systems (SIES), pp.45,50.

29. Horn, W. (1974) Some Simple Scheduling Algorithms, Naval Research Logistics Quaterly, 21.

30. OMNeT++: Discrete Event Simulator (2014), version 4.6, http://www.omnetpp.org/.

31. Network Simulator: NS-2 (2013), version 2.35, http://www.cs.uc.edu/~cdmc/ucbt/.

Transport and Telecommunication Journal

The Journal of Transport and Telecommunication Institute

Journal Information


Cite Score 2017: 1.21

SCImago Journal Rank (SJR) 2017: 0.294
Source Normalized Impact per Paper (SNIP) 2017: 1.539

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 236 230 20
PDF Downloads 136 133 8