Extension of traffic flow pattern dynamic classification by a macroscopic model using multivariate clustering

Transportation Science

Volumn 50, Issue 3, 2016, Pages 966-981

  Celikoglu, Hilmi Berk ^a   Silgu, Mehmet Ali ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Kinematic waves; Simulation; State estimation; Traffic flow

Indexed keywords

CLUSTER ANALYSIS; STATE ESTIMATION;

CELL TRANSMISSION MODEL; DYNAMIC CLASSIFICATION; INTELLIGENT MANAGEMENT; KINEMATIC WAVES; SIMULATION; TRAFFIC FLOW; TRAFFIC FLOW MODELING; TRAFFIC FLOW PATTERNS;

FLOW PATTERNS;

EID: 84984874883     PISSN: 00411655     EISSN: 15265447     Source Type: Journal    
DOI: 10.1287/trsc.2015.0653     Document Type: Article

References (44)

1. Anderberg MR (1973) Cluster Analysis for Applications (Academic Press, New York).
2. Bow ST (1984) Pattern Recognition: Applications to Large Data Set Problems (Marcel Dekker, New York).
3. Celikoglu HB (2007a) A dynamic network loading process with explicit delay modeling. Transportation Res. Part C: Emerging Tech. 15(5):279-299.
4. Celikoglu HB (2007b) A dynamic network loading model for traffic dynamics modelling. IEEE Trans. Intelligent Transportation Systems 8(4):575-583.
5. Celikoglu HB (2013a) An approach to dynamic classification of traffic flow patterns. Computer-Aided Civil Infrastructure Engrg. 28(4):273-288.
6. Celikoglu HB (2013b) Flow-based freeway travel-time estimation: A comparative evaluation within dynamic path loading. IEEE Trans. Intelligent Transportation Systems 14(2):772-781.
7. Celikoglu HB (2013c) Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram. Eur. J. Oper. Res. 228(2):457-466.
8. Celikoglu HB (2014) Dynamic classification of traffic flow patterns simulated by a switching multimode discrete cell transmission model. IEEE Trans. Intelligent Transportation Systems 15(6):2539-2550.
9. Celikoglu HB, Dell'Orco M (2007) Mesoscopic simulation of a dynamic link loading process. Transportation Res. Part C: Emerging Tech. 15(5):329-344.
10. Chen H, Grant-Muller S (2001) Use of sequential learning for short-term traffic flow forecasting. Transportation Res. Part C: Emerging Tech. 9(5):319-336.
11. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems 2(4):303-314.
12. Daganzo CF (1994) The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Res. Part B: Methodological 28(4):269-287.
13. Dougherty M, Cobbett M (1997) Short-term inter-urban traffic forecasts using neural networks. Internat. J. Forecasting 13(1):21-31.
14. Dubes RC (1987) How many clusters are best? An experiment. Pattern Recognition 20(6):645-663.
15. Gerlough DL, Huber MJ (1975) Traffic flow theory: A monograph. Special report no. 165, Transportation Research Board, National Research Council, Washington, DC.
16. Herrera JC, Bayen AM (2010) Incorporation of Lagrangian measurements in freeway traffic pattern estimation. Transportation Res. Part B: Methodological 44(4):269-287.
17. Jain AK, Dubes RC (1988) Algorithms for Clustering Data (Prentice-Hall, Upper Saddle River, NJ).
18. Jain AK, Myrty MN, Flynn PJ (1999) Data clustering: A review. ACM Comput. Surveys 31(3):264-323.
19. Lighthill MJ, Whitham GB (1955) On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. London, Ser. A 229(1178):317-345.
20. Lo HK (2001) A cell-based traffic control formulation: Strategies and benefits of dynamic timing plans. Transportation Sci. 35(2):148-164.
21. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Le Cam LM, Neyman J, eds. Proc. 5th Berkeley Sympos. Math. Statist. Probab. (University of California Press, Berkeley, CA), 281-297.
22. Mao J, Jain AK (1996) A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans. Neural Networks 7(1):16-29.
23. May AD (1990) Traffic Flow Fundamentals (Prentice-Hall, Upper Saddle River, NJ).
24. Mihaylova L, Boel R, Hegyi A (2007) Freeway traffic estimation within particle filtering framework. Automatica 43(2):290-300.
25. Muñoz L, Sun X, Horowitz R, Alvarez L (2003) Traffic density estimation with the cell transmission model. Proc. 2003 Amer. Control Conf., Denver, Vol. 5 (IEEE, Piscataway, NJ), 3750-3755.
26. Muñoz L, Sun X, Sun D, Gomes G, Horowitz R (2004) Methodological calibration of the cell transmission model. Proc. 2004 Amer. Control Conf., Vol. 1 (IEEE, Piscataway, NJ), 798-803.
27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: Part 1-A discussion of principles. J. Hydrology 10(3):282-290.
28. Newell GF (1993) A simplified theory of kinematic waves in highway traffic, part I: General theory. Transportation Res. Part B: Methodological 27(4):281-297.
29. Pavlis Y, Recker W (2009) A mathematical logic approach for the transformation of the linear conditional piecewise functions of dispersion-and-store and cell transmission traffic flow models into linear mixed-integer form. Transportation Sci. 43(1):98-116.
30. Poggio T, Girosi F (1990) Networks for approximation and learning. Proc. IEEE 78(9):1481-1497.
31. Richards PI (1956) Shock waves on the highway. Oper. Res. 4(1):42-51.
32. Rumelhart DE, Hinton GE, Williams JE (1986) Learning internal representations by error propagation. Rumelhart DE, McClelland JL, eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1 (MIT Press, Cambridge, MA), 318-362.
33. Specht DF (1991) A general regression neural network. IEEE Trans. Neural Networks 2(6):568-576.
34. Srinivasan D, Choy MC, Cheu RL (2006) Neural networks for real-time traffic signal control. IEEE Trans. Intelligent Transportation Systems 7(3):261-272.
35. 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 Res. Part B: Methodological 45(3):507-533.
36. Sun X, Muñoz L, Horowitz R (2003) Highway traffic state estimation using improved mixture Kalman filters for effective ramp metering control. Proc. 42nd IEEE Conf. Decision Control, Maui, Hawaii, Vol. 6 (IEEE, Piscataway, NJ), 6333-6338.
37. Transportation Research Board (TRB) (2010) Highway Capacity Manual 2010 (Transportation Research Board, Washington, DC).
38. Treiber M, Helbing D (2002) Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooperative Transportation Dynamics 1(3):3.1-3.24.
39. 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 Res. Part B: Methodological 44(8-9):983-1000.
40. Velan S, Florian M (2002) A note on the entropy solutions of the hydrodynamic model of traffic flow. Transportation Sci. 36(4):435-446.
41. Vythoulkas PC (1993) Alternative approaches to short term traffic forecasting for use in driver information systems. Daganzo CF, ed. Proc. 12th Internat. Sympos. Theory Traffic Flow Transportation (Elsevier, New York), 485-506.
42. Wahba G (1990) Spline Models for Observational Data, CBMS-NSF Regional Conf. Series Applied Math., Vol. 59 (SIAM, Philadelphia).
43. Wang Y, Papageorgiou M (2005) Real-time freeway traffic pattern estimation based on extended Kalman filter: A general approach. Transportation Res. Part B: Methodological 39(2):141-167.
44. Wang Y, Papageorgiou M, Messmer A (2007) Real-time freeway traffic pattern estimation based on extended Kalman filter. Transportation Sci. 41(2):167-181.