Abstract
The most city dwellers are concerned with the urban traffic issues very much. To provide a self awareness and adaptive facilities in traffic signal control system is become more and more urgent. In this paper, we improved the video surveillance and self-adaptive urban traffic signal control system to achieve the development trend in intelligent transportation system (ITS). A self awareness and adaptive urban traffic signal control (TSC) system that could provide both the video surveillance and the traffic surveillance as smart hyperspace. We investigated the vision-based surveillance and to keep sight of the unpredictable and hardly measurable disturbances may perturb the traffic flow. We integrated and performed the vision-based methodologies that include the object segmentation, classify and tracking methodologies to know well the real time measurements in urban road. According to the real time traffic measurement, we derived a grid Agent Communication and the Adaptive Traffic Signal Control strategy to adapt the traffic signal time automatically. By comparing the experimental result obtained by traditional traffic signal control system which improved the traffic queuing situation, we confirmed the efficiency of our vision based smart TSC approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ma, J., Yang, L.T., Apduhan, B.O., Huang, R., Barolli, L., Takizawa, M.: Towards a Smart World and Ubiquitous Intelligence: A Walkthrough from Smart Things to Smart Hyperspaces and UbicKids. Journal of Pervasive Computing and Communication 1, 53–68 (2005)
Ma, J.: Ubiquitous Intelligence - The Intelligence Revolution. ID People Magazine (2005)
Chong, C.-Y., Kumar, S.P.: Sensor Networks: Evolution, Opportunities, and Challenges. Proceedings of tha IEEE 8, 1248–1256 (2003)
10 emerging technologies that will change the world, Technol. Rev., 106(1), 33–49 (2003)
Deng, L.Y., Shih, T.K., Huang, T.-S., Huang, C.-H., Chen, R.-X., Chang, Y.-L.: Universal Access for Roaming User via Mobile Agent Technology. The International Journal of Tamkang Journal of Science and Engineering 5(3), 175–186 (2002)
Zhang, L.-J., Li, H., Lam, H.: Toward a business process grid for utility computing. IT Professional 6(5), 62–64 (2004)
Papageoggius, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y.: Review of Road Traffic Control Strategies. Proceedings of the IEEE 91(12) (2003)
Giusto, D.D., Massidda, F., Perra, C.: A fast algorithm for video segmentation and object tracking, Digital Signal Processing, 2002. In: 14th International Conference on DSP 2002, vol. 2, pp. 697–700 (2002)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: real-time surveillance of people and their activi-ties. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)
Hsu, W.L., Liao, H.Y.M., Jeng, B.S.: Fan: Real-time vehicle tracking on highway. In: The Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, pp. 909–914 (2003)
Dailey, D.J., Li, L.: An algorithm to estimate vehicle speed using uncalibrated cameras. In: Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation System, pp. 441–446 (1999)
Dubuisson, M.-P., Jain, A.K.: Object Contour Extraction Using Color And Motion. In: Proceedings of IEEE Computer society Conference on Computer Vision and Pattern Recognition, pp. 471–476 (1993)
Nakamishi, T., Ishii, K.: Automatic vehicle image extraction based on spatio-temporal image analysis. In: Proceedings of 11th IAPR International Conference on Pattern Recognition. Conference A: Computer Vision and Applications, vol. 1, pp. 500–504 (1992)
Mae, Y., Shirai, Y., Miura, J., Kuno, Y.: Object tracking in cluttered baced on optical flow and edges. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. I, pp. 196–200 (1996)
Koller, D., Weber, J., Malik, J.: Robust Multiple Car Tracking with Occlusion Reasoning, Technical Report UCB/CSD-93-780, University of Calibornia at Berkely (1993)
Matsushita, Y., Kamijo, S., Ikeuchi, K., Sakauchi, M.: Image Processing Based Incident De-tection at Intersections. In: Proceedings of the Fourth Asian Conference on Computer Vision 2000, pp. 520–527 (2000)
May, A.D.: Traffic Flow Fundamentals. Premtice Hall, Englewood Cliffs (1990)
Reports of urban traffic signal control in the Taiwan, the Transportation Institute of the Ministry of Communications (2001)
Van Vuren, T.: Signal control and traffic assignment. In: Papageorgiou (ed.) Concise Encyclopedia of Traffic and Transportation Systems, pp. 468–473. Pergamon, Oxford (1991)
Department, U.S.: of Transportation/ FWHA, Traffic Control System Handbook (1985)
May, A.D.: Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs (1990)
Webster, F.V.: Traffic signal settings, Road Research Technical Paper No. 39, Road Research Laboratory, Her Majesty’s Stationery Office, London, U.K (1958)
Special Report 209: Highway capacity manual, Transportation Research Board, National Research Council, Washington (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deng, L.Y., Lee, Dl. (2006). Self Awareness and Adaptive Traffic Signal Control System for Smart World. In: Yang, L.T., Jin, H., Ma, J., Ungerer, T. (eds) Autonomic and Trusted Computing. ATC 2006. Lecture Notes in Computer Science, vol 4158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839569_16
Download citation
DOI: https://doi.org/10.1007/11839569_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38619-3
Online ISBN: 978-3-540-38622-3
eBook Packages: Computer ScienceComputer Science (R0)