Long-range dependence of traffic flow and speed of a motorway: Dynamics and correlation with historical incidents

Transportation Research Record

Volumn 2616, Issue 1, 2017, Pages 49-57

  Chand, Sai ^a   Aouad, Gregory ^b   Dixit, Vinayak V ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b Parsons Brinckerhoff Australia   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

FRACTALS; TRAFFIC CONGESTION;

AUSTRALIA; CATALYSE; FRACTAL THEORY; HURST EXPONENTS; LONG RANGE DEPENDENCE; TIMES SERIES; TRAFFIC FLOW; TRAFFIC SPEED; TRANSPORT SYSTEMS; TWO-LANE;

SPEED;

EID: 85040007698     PISSN: 03611981     EISSN: 21694052     Source Type: Journal    
DOI: 10.3141/2616-06     Document Type: Article

References (30)

1. Nair, A. S., J.-C. Liu, L. Rilett, and S. Gupta. Non-Linear Analysis of Traffic Flow. In Proceedings of IEEE Conference on Intelligent Transportation Systems, Oakland, Calif., IEEE, New York, 2001, pp. 681–685. https://doi.org/10.1109/ITSC.2001.948742.
2. Breslin, M. C., and J. A. Belward. Fractal Dimensions for Rainfall Time Series. Mathematics and Computers in Simulation, Vol. 48, No. 4–6, 1999, pp. 437–446. https://doi.org/10.1016/S0378-4754(99)00023-3.
3. Mandelbrot, B. How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension. Science, Vol. 156, No. 3775, 1967, pp. 636–638. https://doi.org/10.1126/science.156.3775.636.
4. Bo, Q., and K. Rasheed. Hurst Exponent and Financial Market Predictability. Presented at Conference on Financial Engineering and Applications, Cambridge, Mass., International Association of Science and Technology for Development, Calgary, Alberta, Canada, 2004.
5. Chand, S., V. Dixit, and S. T. Waller. Evaluation of Fluctuating Speed and Lateral Movement of Vehicles: Comparison Between Mixed Traffic and Homogeneous Traffic. Transportation Research Record: Journal of the Transportation Research Board, No. 2581, 2016, pp. 104–112. https://dx.doi.org/10.3141/2581-13.
6. Hurst, H. E. Long-Term Storage Capacity of Reservoirs. Transactions of the American Society of Engineers, Vol. 116, 1951, pp. 770–799.
7. Fu, H., J. Xu, and L. Xu. Traffic Chaos and Its Prediction Based on a Nonlinear Car-Following Model. Journal of Control Theory and Applications, Vol. 3, No. 3, 2005, pp. 302–307. https://doi.org/10.1007/s11768-005-0053-y.
8. Hurst, H. E. Methods of Using Long-Term Storage in Reservoirs. Proceedings – Institution of Civil Engineers, Vol. 5, No. 5, 1956, pp. 519–543.
9. Matos, J. A. O., S. M. A. Gama, H. J. Ruskin, A. A. Sharkasi, and M. Crane. Time and Scale Hurst Exponent Analysis for Financial Markets. Physica A, Vol. 387, No. 15, 2008, pp. 3910–3915. https://doi.org/10.1016/j.physa.2008.01.060.
10. Eom, C., S. Choi, G. Oh, and W.-S. Jung. Hurst Exponent and Prediction Based on Weak-Form Efficient Market Hypothesis of Stock Markets. Physica A, Vol. 387, No. 18, 2008, pp. 4630–4636. https://doi.org/10.1016/j.physa.2008.03.035.
11. Török, J., and J. Kertész. The Green Wave Model of Two-Dimensional Traffic: Transitions in the Flow Properties and in the Geometry of the Traffic Jam. Physica A, Vol. 231, No. 4, 1996, pp. 515–533. https://doi.org/10.1016/0378-4371(96)00144-6.
12. Campari, E. G., and G. Levi. Self-Similarity in Highway Traffic. European Physical Journal B: Condensed Matter and Complex Systems, Vol. 25, No. 2, 2002, pp. 245–251. https://doi.org/10.1140/epjb/e20020028.
13. Belomestny, D., and H. Siegel. Stochastic and Self-Similar Nature of Highway Traffic Data. Zentrum für Technomathematik, Universität Bremen, Germany, 2003.
14. Shang, P., M. Wan, and S. Kama. Fractal Nature of Highway Traffic Data. Computers & Mathematics with Applications, Vol. 54, No. 1, 2007, pp. 107–116. https://doi.org/10.1016/j.camwa.2006.07.017.
15. Vojak, R., J. L. Vehel, and M. Danech-Pajouh. Multifractal Description of Road Traffic Structure. In Symposium on Transportation Systems: Theory and Application of Advanced Technology, International Federation of Automatic Control, Oxford, United Kingdom, 1995, Vol. 2.
16. Li, X., and P. Shang. Multifractal Classification of Road Traffic Flows. Chaos, Solitons, and Fractals, Vol. 31, No. 5, 2007, pp. 1089–1094. https://doi.org/10.1016/j.chaos.2005.10.109.
17. Meng, Q., and H. L. Khoo. Self-Similar Characteristics of Vehicle Arrival Pattern on Highways. Journal of Transportation Engineering, ASCE, Vol. 135, No. 11, 2009, pp. 864–872. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:11(864).
18. Perati, M. R., K. Raghavendra, H. K. R. Koppula, M. R. Doodipala, and R. Dasari. Self-Similar Behavior of Highway Road Traffic and Performance Analysis at Toll Plazas. Journal of Transportation Engineering, ASCE, Vol. 138, No. 10, 2012, pp. 1233–1238. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000427.
19. Thomas, K., and H. Dia. Comparative Evaluation of Freeway Incident Detection Models Using Field Data. IEEE Proceedings–Intelligent Transport Systems, Vol. 153, No. 3, 2006, pp. 230–241.
20. Ye, C., H. Li, and G. Xu. An Early Warning Model of Traffic Accidents Based on Fractal Theory. Presented at the 17th International Conference on Intelligent Transportation Systems, Qingdao, China, IEEE, New York, 2014.
21. Lin, L., Q. Wang, and A. Sadek. Short-Term Forecasting of Traffic Volume: Evaluating Models Based on Multiple Data Sets and Data Diagnosis Measures. Transportation Research Record: Journal of the Transportation Research Board, No. 2392, 2013, pp. 40–47. https://dx.doi.org/10.3141/2392-05.
22. Lan, L. W., F.-Y. Lin, and A. Y. Kuo. Testing and Prediction of Traffic Flow Dynamics with Chaos. Journal of the Eastern Asia Society for Transportation Studies, Vol. 5, 2003, pp. 1975–1990.
23. Haleem, K., P. Alluri, and A. Gan. Investigating Fractal Characteristics in Crash Trends for Potential Traffic Safety Prediction. Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C., 2016.
24. Rangarajan, G., and D. A. Sant. A Climate Predictability Index and Its Applications. Geophysical Research Letters, Vol. 24, No. 10, 1997, pp. 1239–1242. https://doi.org/10.1029/97GL01058.
25. Cooperative Phenomena Group. Projects and Methods: Self-Affine Fractals. Department of Physics, University of Oslo, Norway. http://laplace.ucv.cl/Patterns/coopphen_book97/boxes/selfAffineFractals.html. Accessed Oct. 25, 2015.
26. TomTom Traffic Index. https://www.tomtom.com/en_au/trafficindex. Accessed July 20, 2016.
27. Lee, C., B. Hellinga, and K. Ozbay. Quantifying Effects of Ramp Metering on Freeway Safety. Accident Analysis and Prevention, Vol. 38, No. 2, 2006, pp. 279–288. https://doi.org/10.1016/j.aap.2005.09.011.
28. Gettman, D., and L. Head. Surrogate Safety Measures from Traffic Simulation Models. Transportation Research Record: Journal of the Transportation Research Board, No. 1840, 2003, pp. 104–115. https://dx.doi.org/10.3141/1840-12.
29. Oh, C., and T. Kim. Estimation of Rear-End Crash Potential Using Vehicle Trajectory Data. Accident Analysis and Prevention, Vol. 42, No. 6, 2010, pp. 1888–1893. https://doi.org/10.1016/j.aap.2010.05.009.
30. Bagdadi, O. Assessing Safety Critical Braking Events in Naturalistic Driving Studies. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 16, 2013, pp. 117–126. https://doi.org/10.1016/j.trf.2012.08.006.