An Approach to Dynamic Classification of Traffic Flow Patterns

Computer-Aided Civil and Infrastructure Engineering

Volumn 28, Issue 4, 2013, Pages 273-288

  Celikoglu, Hilmi Berk ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION METHODS; DENSITY MAPPING; DENSITY MEASURES; DETECTION AND CONTROLS; DYNAMIC APPROACHES; DYNAMIC CLASSIFICATION; FLOW CONSERVATION; FLOW MODELING; FLOW PERFORMANCE; FUNDAMENTAL DIAGRAM; INTELLIGENT MANAGEMENT; MACROSCOPIC TRAFFIC FLOW MODEL; MODELING PROCESS; NONRECURRENT CONGESTION; PATTERN VARIATION; REAL TIME; REAL-TIME MAPPING; SIMULATION TIME; SUDDEN CHANGE; TIME VARYING; TRAFFIC DENSITIES; TRAFFIC FLOW; TRAFFIC FLOW MODELING; TRAFFIC FLOW PATTERNS;

ABSTRACTING; APPROXIMATION THEORY; FLOW CONTROL; TIME VARYING NETWORKS; TRAFFIC CONTROL;

FLOW PATTERNS;

ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; FLOW MODELING; PERFORMANCE ASSESSMENT; ROAD TRANSPORT; TRAFFIC CONGESTION; TRAFFIC MANAGEMENT;

EID: 84874702163     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/j.1467-8667.2012.00792.x     Document Type: Article

References (71)

1. Adeli, H. (2001), Neural networks in civil engineering: 1989-2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 26-42.
2. Adeli, H. & Karim, A. (2000), Fuzzy-wavelet RBFNN model for freeway incident detection, Journal of Transportation Engineering, 126(6), 464-71.
3. Adeli, H. & Samant, A. (2000), An adaptive conjugate gradient neural network-wavelet model for traffic incident detection, Computer-Aided Civil and Infrastructure Engineering, 15(4), 251-60.
4. Bhaskar, A., Chung, E. & Dumont, A. G. (2011), Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks, Computer-Aided Civil and Infrastructure Engineering, 26(6), 433-50.
5. Bianchini, A. & Bandini, P. (2010), Prediction of pavement performance through neuro-fuzzy reasoning, Computer-Aided Civil and Infrastructure Engineering, 25(1), 39-54.
6. Boto-Giralda, D., Díaz-Pernas, F. J., González-Ortega, D., Díez-Higuera, J. F., Antón-Rodríguez, M., Martínez-Zarzuela, M. & Torre-Díez, I. (2010), Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks, Computer-Aided Civil and Infrastructure Engineering, 25(7), 530-45.
7. Celikoglu, H. B. (2007), A dynamic network loading model for traffic dynamics modeling, IEEE Transactions on Intelligent Transportation Systems, 8(4), 575-83.
8. Coifman, B. (2003), Estimating density and lane inflow on a freeway segment, Transportation Research Part A: Policy and Practice, 37(8), 689-701.
9. Cybenko, G. (1989), Approximation by superpositions of a sigmoidal function, Mathematics of Control Signals Systems, 2, 303-14.
10. Daganzo, C. F. (1994), The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B: Methodological, 28(4), 269-87.
11. Daganzo, C. F. (1997), Fundamentals of Transportation and Traffic Operations, Pergamon, Oxford, UK .
12. Dailey, D. J. (1999), A statistical algorithm for estimating speed from single loop volume and occupancy measurements, Transportation Research Part B: Methodological, 33(5), 313-22.
13. Flood, I. & Kartam, N. (1994), Neural networks in civil engineering. II: systems and application, Journal of Computing in Civil Engineering, 8(2), 149-62.
14. Ghodrati, Amiri G., Abdolahi, Rad A. & Khorasani, M. (2012), Generation of near-field artificial ground motions compatible with median predicted spectra using PSO-based neural network and wavelet analysis, Computer-Aided Civil and Infrastructure Engineering, 27(9), 711-30.
15. Ghosh-Dastidar, S. & Adeli, H. (2003), Wavelet-clustering-neural network model for freeway incident detection, Computer-Aided Civil and Infrastructure Engineering, 18(5), 325-38.
16. Ghosh-Dastidar, S. & Adeli, H. (2006), Neural network-wavelet micro-simulation model for delay and queue length estimation at freeway work zones, Journal of Transportation Engineering, 132(4), 331-41.
17. Graf, W., Freitag, S., Kaliske, M. & Sickert, J. U. (2010), Recurrent neural networks for uncertain time-dependent structural behavior, Computer-Aided Civil and Infrastructure Engineering, 25(5), 322-33.
18. Graf, W., Freitag, S., Sickert, J. U. & Kaliske, M. (2012), Structural analysis with fuzzy data and neural network-based material description, Computer-Aided Civil and Infrastructure Engineering, 27(9), 640-54.
19. Haykin, S. (1998), Neural Networks: A Comprehensive Foundation, Prentice Hall, Englewood Cliffs, NJ.
20. Heilmann, B., El Faouzi, N. E., de Mouzon, O., Hainitz, N., Koller, H., Bauder, D. & Antoniou, C. (2011), Predicting motorway traffic performance by data fusion of local sensor data and electronic toll collection data, Computer-Aided Civil and Infrastructure Engineering, 26(6), 451-63.
21. Herrera, J. C. & Bayen, A. M. (2010), Incorporation of Lagrangian measurements in freeway traffic pattern estimation, Transportation Research Part B: Methodological, 44(4), 269-87.
22. Hooshdar, S. & Adeli, H. (2004), Toward intelligent variable message signs in freeway work zones: a neural network approach, Journal of Transportation Engineering, 130(1), 83-93.
23. Hornik, K., Stinchcombe, M. & White, H. (1989), Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-66.
24. Hung, S. L. & Jan, J. C. (1999), Machine learning in engineering analysis and design: an integrated fuzzy neural network learning model, Computer-Aided Civil and Infrastructure Engineering, 14(3), 207-19.
25. Jia, Z., Chen, C., Coifman, B. & Varaiya, P. (2001), The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors, in Proceedings of the 2001 IEEE Intelligent Transportation Systems Conference, Oakland, CA, 536-41.
26. Jiang, X. & Adeli, H. (2003), Freeway work zone traffic delay and cost optimization model, Journal of Transportation Engineering, 129(3), 230-41.
27. Jiang, X. & Adeli, H. (2004a), Object-oriented model for freeway work zone capacity and queue delay estimation, Computer-Aided Civil and Infrastructure Engineering, 19(2), 144-56.
28. Jiang, X. & Adeli, H. (2004b), Wavelet packet-autocorrelation function method for traffic flow pattern analysis, Computer-Aided Civil and Infrastructure Engineering, 19(6), 324-37.
29. Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering, 131(10), 771-79.
30. Karim, A. & Adeli, H. (2002a), Comparison of the fuzzy-wavelet RBFNN freeway incident detection model with the California algorithm, Journal of Transportation Engineering, 128(1), 21-30.
31. Karim, A. & Adeli, H. (2002b), Incident detection algorithm using wavelet energy representation of traffic patterns, Journal of Transportation Engineering, 128(3), 232-42.
32. Karim, A. & Adeli, H. (2003), Radial basis function neural network for work zone capacity and queue estimation, Journal of Transportation Engineering, 129(5), 494-503.
33. Kerner, B. S. (2004), The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory, Springer, Heidelberg .
34. Kerner, B. & Rehborn, H. (1996), Experimental properties of complexity in traffic flow, Physical Review E, 53(5), R4275-R78.
35. Lighthill, M. J. & Whitham, G. B. (1955), On kinematic waves II. A theory of traffic flow on long crowded roads, in Proceedings of the Royal Society of London Part A, 229(1178), 317-45.
36. Mei, X., Gunaratne, M., Lu, J. J. & Dietrich, B. (2004), Neural network for rapid depth evaluation of shallow cracks in asphalt pavements, Computer-Aided Civil and Infrastructure Engineering, 19(3), 223-30.
37. Mihaylova, L., Boel, R. & Hegyi, A. (2007), Freeway traffic estimation within particle filtering framework, Automatica, 43(2), 290-300.
38. Muñoz, L., Sun, X., Horowitz, R. & Alvarez, L. (2003), Traffic density estimation with the Cell Transmission Model, in Proceedings of the 2003 American Control Conference, Denver, Colorado June 4-6, 2003, Vol. 5, 3750-55.
39. Newell, G. F. (1993), A simplified theory of kinematic waves in highway traffic, part I: general theory, Transportation Research Part B: Methodological, 27(4), 281-97.
40. Osornio-Rios, R. A., Amezquita-Sanchez, J. P., Romero-Troncoso, R. J. & Garcia-Perez, A. (2012), MUSIC-neural network analysis for locating structural damage in truss-type structures by means of vibrations, Computer-Aided Civil and Infrastructure Engineering, 27(9), 687-98.
41. Panakkat, A. & Adeli, H. (2009), Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators, Computer-Aided Civil and Infrastructure Engineering, 24(4), 280-92.
42. Pande, A. & Abdel-Aty, M. (2008), A computing approach using probabilistic neural networks for instantaneous appraisal of rear-end crash risk, Computer-Aided Civil and Infrastructure Engineering, 23(7), 549-59.
43. Poggio, T. & Girosi, F. (1990), Networks for approximation and learning, in Proceedings of IEEE, 78(9) 1481-97.
44. Richards, P. I. (1956), Shock waves on the highway, Operations Research, 4(1), 42-51.
45. Rumelhart, D. E., Hinton, G. E. & Williams, J. E. (1986), Learning internal representations by error propagation, in D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing-Explorations in the Microstructure of Cognition, Vol. 1, Foundations MIT Press, Cambridge, MA .
46. Samant, A. & Adeli, H. (2001), Enhancing neural network incident detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
47. Schönhof, M. & Helbing, D. (2007), Empirical features of congested traffic states and their implications for traffic modeling, Transportation Science, 41(2), 1-32.
48. Specht, D. F. (1991), A general regression neural network, IEEE Transactions on Neural Networks, 2, 568-76.
49. Stathopoulos, A., Dimitriou, L. & Tsekeris, T. (2008), Fuzzy modeling approach for combined forecasting of urban traffic flow, Computer-Aided Civil and Infrastructure Engineering, 23(7), 521-35.
50. Sumalee, A., Zhong, X., Pan, L. & Szeto, Y. (2011), Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic pattern surveillance and assignment, Transportation Research Part B: Methodological, 45(3), 507-33.
51. Sun, X., Muñoz, L. & Horowitz, R. (2003), Highway traffic pattern estimation using improved mixture Kalman filters for effective ramp metering control, in Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, HI, Vol. 6, 6333-38.
52. Szeto, W. Y. & Sumalee, A. (2011), A cell-based model for multi-class doubly stochastic dynamic traffic assignment, Computer-Aided Civil and Infrastructure Engineering, 26(8), 595-611.
53. Tillema, F., Van Zuilekom, K. M. & Van Maarseveen, M. F. A. M. (2006), Comparison of neural networks and gravity models in trip distribution, Computer-Aided Civil and Infrastructure Engineering, 21(2), 104-19.
54. Transportation Research Board (TRB) (2010), Highway Capacity Manual 2010, Transportation Research Board, Washington DC .
55. Treiber, M. & Helbing, D. (1999), Macroscopic simulation of widely scattered synchronized traffic states, Journal of Physics A: Mathematical and General, 32(1), L17-L23.
56. Treiber, M. & Helbing, D. (2002), Reconstructing the spatio-temporal traffic dynamics from stationary detector data, Cooper@tive Transport@tion Dyn@mics, 1(3), 3.1-3.24.
57. Treiber, M., Hennecke, A. & Helbing, D. (2000), Congested traffic states in empirical observations and microscopic simulations, Physical Review E, 62(2), 1805-24.
58. Treiber, M., Kesting, A. & Helbing, D. (2006), Understanding widely scattered traffic flows, the capacity drop, and platoons as effects of variance-driven time gaps, Physical Review E, 74(1), 016123.1-016123.10.
59. Treiber, M., Kesting, A. & Helbing, D. (2010), Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts, Transportation Research Part B: Methodological, 44(8-9), 983-1000.
60. Treiber, M., Kesting, A. & Wilson, R. E. (2011), Reconstructing the traffic state by fusion of heterogeneous data, Computer-Aided Civil and Infrastructure Engineering, 26(6), 408-19.
61. US DOT (Department of Transportation) FHWA (Federal Highway Administration) (2006), Traffic Detector Handbook, 3rd edn, Vol. I, Publication No. FHWA-HRT-06-108.
62. Van Lint, J. W. C. (2004), Reliable travel time prediction for freeways. Ph.D. thesis, Trail Thesis Series No. T2004/3, The Netherlands TRAIL Research School, Delft University of Technology.
63. Van Lint, J. W. C., Hoogendoorn, S. P. & Van Zuylen, H. J. (2002a), Freeway travel time prediction with state-space neural networks-modeling state-space dynamics with recurrent neural networks, Transportation Research Record, 1811, 30-39.
64. Van Lint, J. W. C., Hoogendoorn, S. P. & Van Zuylen, H. J. (2002b), State space neural networks for freeway travel time prediction, in Proceedings of the International Conference on Artificial Neural Networks (ICANN), Lecture Notes in Computer Science, Madrid, Vol. 2415, 1043-48.
65. Vlahogianni, E. I., Golias, J. C. & Karlaftis, M. G. (2004), Short-term forecasting: overview of objectives and methods. Transport Reviews, 24(5), 533-57.
66. Vlahogianni, E. I., Karlaftis, M. G. & Golias, J. C. (2008), Temporal evolution of short-term urban traffic flow: a nonlinear dynamics approach, Computer-Aided Civil and Infrastructure Engineering, 23(7), 536-48.
67. Wagner, C., Hoffmann, C., Sollacher, R., Wagenhuber, J. & Schürmann, B. (1996), Second-order continuum traffic flow model, Physical Review E, 54(5), 5073-85.
68. Wahba, G. (1990), Spline models for observational data, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 59, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA.
69. Wang, Y. & Papageorgiou, M. (2005), Real-time freeway traffic pattern estimation based on extended Kalman filter: a general approach, Transportation Research Part B: Methodological, 39(2), 141-67.
70. Wang, Y., Papageorgiou, M. & Messmer, A. (2007), Real-time freeway traffic pattern estimation based on extended Kalman filter, Transportation Science, 41(2), 167-81.
71. Williamson, R. C. & Hemke, U. (1995), Existence and uniqueness results for neural network approximations, IEEE Transactions on Neural Networks, 6, 2-13.