Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

References

• Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. doi: 10.1016/S1389-1286(01)00302-4
   Web of Science ®Google Scholar
• Andrews, M. (1996). Introduction to Chaos Theory [Online]. Google. Retrieved December 12, 2012, from http://www.gweep.net/~rocko/sufficiency/node10.html
   Google Scholar
• Bell, M. C. (1983). A survey of the methods used to define and change signal-plans in the UK (Research Report 50). Newcastle: Transport Operations Research Group.
   Google Scholar
• Bell, M. C., & Galatioto, F. (2013). Novel wireless pervasive sensor network to improve the understanding of noise in street canyons. Applied Acoustics, 74(1), 169–180. doi: 10.1016/j.apacoust.2012.07.007
   Web of Science ®Google Scholar
• Bell, M. C., Galatioto, F., Hill, G., & Namdeo, A. (2009). Modelling environmental impacts of traffic using a new generation of pervasive sensors. 16th world congress for ITS systems and services. Stockholm: Intelligent Transport Systems.
   Google Scholar
• Blogg, M., Semler, C., Hingorani, M., & Troutbeck, R. (2010). Travel time and origin-destination data collection using Bluetooth MAC address readers. Australasian transport research forum 2010. Canberra: Australasian Transport Research.
   Google Scholar
• Bloomberg. (2011). Traffic cost U.S. $114.8 billion in 2009, Texas Institute Says [Online]. Retrieved September 13, 2012, from www.bloomberg.com/news/2011-01-20/traffic-jams-cost-us-114-8-billion-in-time-fuel-in-09-institute-says.html
   Google Scholar
• Bourner, M. (1984). SCOOT experience in Kent Institution of Electrical Engineers Colloquium Digest No. 1984 (58), London.
   Google Scholar
• Busch, F. (1996). Traffic telematics in Urban and regional environments inter-traffic conference. Amsterdam: PTRC Education and Research Services Limited.
   Google Scholar
• Butler, A. (1990). A methodological approach to chaos: Are economists missing the point? Federal Reserve Bank of St. Louis, 72(13), 36–48.
   Google Scholar
• Centre for Economics and Business Research. (2014). The future economic and environmental cost of gridlock in 2030. An Assessment of the direct and indirect economic and environmental costs of idling in road traffic congestion to households in the UK, France, Germany and the USA. London: Cebr Consultancy.
   Google Scholar
• Cheslow, M., Hatcher, S. G., & Patel, V. M. (1992). An initial evaluation of alternative intelligent vehicle highway systems architectures. MTR 92W0000063. Bedford, MS: MITRE Corporation.
   Google Scholar
• Cragg, S. (2013). Bluetooth detection – cheap but challenging. Online. Edinburgh: Transport Scotland.
   Google Scholar
• Dendrinos, D. S. (1994). Traffic-flow dynamics: A search for chaos. Pergamon: Chaos, Solitons & Fractals, 4(4), 605–617.
   Web of Science ®Google Scholar
• Department for Transport. (1999). The “SCOOT” urban traffic control system. Traffic advisory leaflet, 7(99). London: DfT Publications.
   Google Scholar
• Department for Transport. (2011). Transport energy and environment statistics 2011. Statistical release. London: DfT Publications.
   Google Scholar
• Department for Transport. (2013a). Annual road traffic estimates: Great Britain 2012 road traffic statistics statistical release. London: DfT Publications.
   Google Scholar
• Department for Transport. (2013b). Road length in great Britain: 2012 road length statistics. Statistical release. London: DfT Publications.
   Google Scholar
• Donati, F., Mauro, V., Roncolini, G., & Vallauri, M. (1984). A hierarchical decentralisated traffic light control system – the first realisation. 9th international FAC world congress, 1984 Budapest. Federation of automotive control.
   Google Scholar
• Downes, I., Rad, L. B., & Aghajan, H. (2006, March 16). Development of a mote for wireless image sensor networks. Proc. of COGnitive Systems with Interactive Sensors (COGIS). Paris.
   Google Scholar
• Dreyer, K., & Hickey, F. R. (1991). The route to chaos in a dripping water faucet. American Journal of Physics, 59(7), 619–627. doi: 10.1119/1.16783
   Web of Science ®Google Scholar
• Farges, J., Khoudour, I., & Lesort, J. (1990, May 1). PRODYN: On site evaluation. 3rd international conference on Road Traffic Control 1990 Stevenage. IEE Publications (pp. 62–66).
   Google Scholar
• Frazier, C., & Kockelman, K. M. (2004). Chaos Theory and transportation systems: An instructive example 83rd annual meeting of the transportation research board. Washington, DC: TRB Publications.
   Google Scholar
• Galas, J. (2010). Traffic data collection with Bluetooth [Online]. Illinois Department of Transport. Retrieved August 13, 2013, from http://www.conferences.uiuc.edu/traffic/2010PDF/File_10_Galas.pdf
   Google Scholar
• Galatioto, F., & Bell, M. C. (2013). Exploring the processes governing roadside pollutant concentrations in urban street canyon. Environmental Science and Pollution Research, 20(7), 4750–4765. doi: 10.1007/s11356-012-1428-5
   PubMed Web of Science ®Google Scholar
• Gleick, J. (1988). Chaos – making a new science. London: Great Britain William Heinemann.
   Google Scholar
• Goodwin, P. (2004). The economic costs of road traffic congestion a discussion paper. University College London, London. Retrieved from http://discovery.ucl.ac.uk/1259/1/2004_25.pdf
   Google Scholar
• Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 43(Part 1), 50–64. doi: 10.1016/j.trc.2014.02.006
   Web of Science ®Google Scholar
• Hamilton, A., Waterson, B., Cherret, T. A. R., & Snell, I. (2012). Urban traffic control evolution 44th universities transport studies group conference. Aberdeen: Universities Transport Studies Group.
   Google Scholar
• Han, C., & Song, S. (2003, October 12–15). A review of some main models for traffic flow forecasting. IEEE international conference on intelligent transport systems, Institute of Electrical and Electronic Engineers, Shanghai (pp. 216–219).
   Google Scholar
• Horton, M., & Suh, J. (2005, June 12–17). A vision for wireless sensor networks. Microwave symposium digest, 12th-17th June 2005 IEEE MTT-S International, California.
   Google Scholar
• Hounsell, N. B., & McDonald, M. (2001). Urban network traffic control. Journal of Systems and Control Engineering: Part I Proceedings of the Institution of Mechanical Engineers, 215(4), 325–334.
   Google Scholar
• Hounsell, N. B., Shrestha, B. P., Piao, J., & McDonald, M. (2009). Review of urban traffic management and the impacts of new vehicle technologies. Intelligent Transport Systems, Institution of Engineering and Technology, 3(4), 419–428. doi: 10.1049/iet-its.2009.0046
   Web of Science ®Google Scholar
• Huang, S., & Sadek, A. W. (2009). A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting. Transportation Research Part C: Emerging Technologies, 17(5), 510–525. doi: 10.1016/j.trc.2009.04.006
   Web of Science ®Google Scholar
• Jianming, H., Chunguang, Z., Jingyan, S., Zuo, Z., & Jiangtao, R. (2003). An applicable short-term traffic flow forecasting method based on chaotic theory. IEEE international conference on intelligent transport systems, Institute of Electrical and Electronic Engineers, Shanghai (pp. 608–613).
   Google Scholar
• Károlyi, G., Pattantyús-Ábrahám, M., & Józsa, J. (2010). Finite-size Lyapunov exponent: A new tool for lake dynamics. Engineering and Computational Mechanics, 163(EM4), 251–259.
   Google Scholar
• Kay, A., & Jackson, P. (2012, May 12). An appraisal of emerging bluetooth traffic survey technology. Scottish Transport and Applications Research conference, The Lighthouse, Glasgow.
   Google Scholar
• Kesting, A., & Treiber, M. (2008). How reaction time, update time, and adaptation time influence the stability of traffic flow. Computer-Aided Civil and Infrastructure Engineering, 23(2), 125–137. doi: 10.1111/j.1467-8667.2007.00529.x
   Web of Science ®Google Scholar
• Kirby, H. R., & Smith, M. J. (1991, January). Can chaos theories have transport applications? 23rd Universities Transport Studies Group conference, Universities Transport Studies Group, Nottingham.
   Google Scholar
• Krese, B., & Govekar, E. (2013). Analysis of traffic dynamics on a ring road-based transportation network by means of 0–1 test for chaos and Lyapunov spectrum. Transportation Research Part C: Emerging Technologies, 36, 27–34. doi: 10.1016/j.trc.2013.08.001
   Web of Science ®Google Scholar
• Lan, L. W., Lin, F. Y., & Kuo, A. Y. (2003). Testing and prediction of traffic flow dynamics with chaos. Journal of the Eastern Asia Society for Transport Studies, 5, 1975–1990.
   Google Scholar
• Lee, J. F. J., Kwok, P. K., & Williams, J. (2014). Heterogeneity among motorists in traffic-congested areas in Southern California. Transportation Research Part A: Policy and Practice, 70, 281–293.
   Web of Science ®Google Scholar
• Lorenz, E. N. (1993). The essence of chaos. London: UCL Press.
   Google Scholar
• Lowrie, P. R. (1982, March 30–April 1). The Sydney co-ordinated adaptive traffic system: Principles, methodology, algorithms. IEE international conference on Road Traffic Signalling, Institution of Electrical Engineers, London.
   Google Scholar
• Mandelbrot, B. B. (1983). The fractal geometry of nature. New York, NY: W. H. Freeman and Company.
   Google Scholar
• Manley, E., & Cheng, T. (2010, April 6–9). Understanding road congestion as an emergent property of traffic networks. International multiconference on Complexity, Informatics and Cybernetics, Orlando (pp. 109–114).
   Google Scholar
• Meehan, D. (2003). We know it's good, but just how good is it? Traffic Engineering and Control, 44(8), 295–298.
   Google Scholar
• Mirchandani, P., & Head, L. (2001). A real-time traffic signal control system: Architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies, 9(6), 415–432. doi: 10.1016/S0968-090X(00)00047-4
   Web of Science ®Google Scholar
• Nair, A. S., Liu, J. C., Rilett, L., & Gupta, S. (26–29 August 2001). Non-linear analysis of traffic flow. IEEE intelligent transportation systems conference, Institution of Electric and Electronic Engineers, Oakland, CA.
   Google Scholar
• Narh, A. T., Thorpe, N., Bell, M. C., & Hill, G. A. (2014). Using Chaos Theory to examine the dynamical states of road traffic in signalised urban networks. 46th Universities Transport Studies Group. Newcastle upon Tyne: Universities Transport Studies Group.
   Google Scholar
• Narh, A. T., Thorpe, N., Bell, M. C., & Hill, G. A. (2015). Can Chaos Theory be used to manage traffic in signalised urban networks. 47th Universities Transport Studies Group. London: Universities Transport Studies Group.
   Google Scholar
• North, R., Cohen, J., Wilkins, S., Richards, M., Hoose, N., Polak, J., … Hill, G. (2009). Field deployments of the MESSAGE system for environmental monitoring. Traffic Engineering and Control, 50(11), 484–488.
   Google Scholar
• O'Flaherty, C. A. (2005). Traffic planning strategies. In C. A. O'Flaherty (Ed.), Transport planning and traffic engineering, (pp. 132–153). Oxford: Butterworth-Heinemann.
   Google Scholar
• Ortuzar, J. D. D., & Willumsen, L. G. (2006). Assignment modelling transport (3rd ed.). London: John Wiley & Sons.
   Google Scholar
• Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., & Yibing, W. (2003). Review of road traffic control strategies. Proceedings of Insitution of Electrical and Electronic Engineers, 91(12), 2043–2067. doi: 10.1109/JPROC.2003.819610
   Web of Science ®Google Scholar
• Qi, Y., & Ishak, S. (2014). A hidden Markov model for short term prediction of traffic conditions on freeways. Transportation Research Part C: Emerging Technologies, 43(Part 1), 95–111. doi: 10.1016/j.trc.2014.02.007
   Web of Science ®Google Scholar
• Safanov, L. A., Tomer, E., Strygin, V. V., Ashkenazy, Y., & Havlin, S. (2002). Delayed-induced chaos with multi-fractal attractor in a traffic flow model. Europhysics Letters, 57, 151–157. doi: 10.1209/epl/i2002-00555-0
   Google Scholar
• Sessler, P. (2007). Traffic on the roads and links to the Chaos Theory [Online]. Helium. Retrieved October 16, 2012, from http://www.helium.com/items/717680-traffic-on-the-roads-and-links-to-the-chaos-theory.
   Google Scholar
• Shang, P., Wan, M., & Kama, S. (2007). Fractal nature of highway traffic data. Computers and Mathematics with Applications, 54, 107–116. doi: 10.1016/j.camwa.2006.07.017
   Web of Science ®Google Scholar
• Shih, E., Cho, S. H., Ickes, N., Min, R., Sinha, A., Wang, A., & Chandrakasan, A. (2001, August 16–21). Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. 7th annual international conference on mobile computing and networking, Association of Computing Machinery, Rome (pp. 272–287).
   Google Scholar
• Shirer, H. N., Fosmire, C. J., Wells, R., & Suciu, L. (1997). Estimating the correlation dimension of atmospheric time series. Journal of the Atmospheric Sciences, 54(1), 211–230. doi: 10.1175/1520-0469(1997)054<0211:ETCDOA>2.0.CO;2
   Web of Science ®Google Scholar
• Simmonite, B. (2005). An introduction to traffic signals. Bristol: JCT Consultancy.
   Google Scholar
• Sipress, A. (1999, August 5). Lab studying science behind traffic patterns. Washington Post.
   Google Scholar
• Takens, F. (1981). Detecting strange attractors in turbulence. Dynamic Systems and Turbulence: Lecture Notes in Mathematics, 898, 366–81.
   Google Scholar
• The Smith Group. (1999). Clearing the way – practical solutions for urban road user charging. London: Author.
   Google Scholar
• Transport Research Laboratory. (2013). SCOOT. Split cycle offset optimisation technique. Wokingham: Transport Research Laboratory.
   Google Scholar
• Uittenbogaard, A. (2011). Chaos Theory for beginners [Online]. Retrieved December 12, 2012, from http://www.abarim-publications.com/ChaosTheoryIntroduction.html
   Google Scholar
• US Census Bureau. (2005). Trends in vehicle miles travelled [Online]. Retrieved September 9, 2012, from www.pewclimate.org/global-warming-basics/facts_and_figures/us_emissions/vmt.cfm
   Google Scholar
• Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2005). Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C: Emerging Technologies, 13(3), 211–234. doi: 10.1016/j.trc.2005.04.007
   Web of Science ®Google Scholar
• Wang, J., Shi, Q., & Lu, H. (2005, June 6–8). The study of short term traffic flow forecasting based on theory of chaos. Intelligent vehicles symposium, Institute of Electrical and Electronic Engineers, Nevada, USA.
   Google Scholar
• Xu, M., & Gao, Z. (2008). Nonlinear analysis of road traffic flows in discrete dynamical system. Journal of Computational and Nonlinear Dynamics, 3(2), 0212061–0212066. doi: 10.1115/1.2833905
   Web of Science ®Google Scholar
• Xue, J., & Shi, Z. (2008). Short-time traffic flow prediction based on chaos time series theory. Journal of Transportation Systems Engineering and Information Technology, 8(5), 68–72. doi: 10.1016/S1570-6672(08)60040-9
   Google Scholar
• Yasin, A. M., Karim, M. R., & Abdullah, A. S. (2009). Travel time measurement in real-time using automatic number plate recognition for Malaysian environment. Journal of the Eastern Asian Society for Transportation Studies, 8, 1738–1751.
   Google Scholar
• Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of art. International Journal of Forecasting, 14, 35–62. doi: 10.1016/S0169-2070(97)00044-7
   Web of Science ®Google Scholar
• Zhang, J., & Liu, J. (2007, August 18–21). Non-linear characteristics of short-term traffic flow and their influences to forecasting. IEEE international conference on Automation and Logistics, Institute of Electrical and Electronic Engineers, Jinan (pp. 847–851).
   Google Scholar
• Zheng, P., & McDonald, M. (2007). Optimal traffic data archive scheme. Intelligent Transport Systems, IET, 1(20), 144–149. doi: 10.1049/iet-its:20060082
   Web of Science ®Google Scholar
• Zhu, C., Xu, X., & Yan, C. (2010, December 4–6). The research of method of short-term traffic flow forecast based on GA-BP neural network and Chaos Theory. 2nd International Conference on Information Science and Engineering (ICISE), Institute of Electrical and Electronic Engineers, Hangzhou (pp. 1617–1620).
   Google Scholar