Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering
Published Online:22 Feb 2016https://doi.org/10.1287/trsc.2015.0653
References
- (1973) Cluster Analysis for Applications (Academic Press, New York).Google Scholar
- (1984) Pattern Recognition: Applications to Large Data Set Problems (Marcel Dekker, New York).Google Scholar
- (2007a) A dynamic network loading process with explicit delay modeling. Transportation Res. Part C: Emerging Tech. 15(5):279–299.Crossref, Google Scholar
- (2007b) A dynamic network loading model for traffic dynamics modelling. IEEE Trans. Intelligent Transportation Systems 8(4):575–583.Crossref, Google Scholar
- (2013a) An approach to dynamic classification of traffic flow patterns. Computer-Aided Civil Infrastructure Engrg. 28(4):273–288.Crossref, Google Scholar
- (2013b) Flow-based freeway travel-time estimation: A comparative evaluation within dynamic path loading. IEEE Trans. Intelligent Transportation Systems 14(2):772–781.Crossref, Google Scholar
- (2013c) Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram. Eur. J. Oper. Res. 228(2):457–466.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2007) Mesoscopic simulation of a dynamic link loading process. Transportation Res. Part C: Emerging Tech. 15(5):329–344.Crossref, Google Scholar
- (2001) Use of sequential learning for short-term traffic flow forecasting. Transportation Res. Part C: Emerging Tech. 9(5):319–336.Crossref, Google Scholar
- (1989) Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems 2(4):303–314.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (1997) Short-term inter-urban traffic forecasts using neural networks. Internat. J. Forecasting 13(1):21–31.Crossref, Google Scholar
- (1987) How many clusters are best? An experiment. Pattern Recognition 20(6):645–663.Crossref, Google Scholar
- (1975) Traffic flow theory: A monograph. Special report no. 165, Transportation Research Board, National Research Council, Washington, DC.Google Scholar
- (2010) Incorporation of Lagrangian measurements in freeway traffic pattern estimation. Transportation Res. Part B: Methodological 44(4):269–287.Crossref, Google Scholar
- (1988) Algorithms for Clustering Data (Prentice-Hall, Upper Saddle River, NJ).Google Scholar
- (1999) Data clustering: A review. ACM Comput. Surveys 31(3):264–323.Crossref, Google Scholar
- (1955) On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. London, Ser. A 229(1178):317–345.Crossref, Google Scholar
- (2001) A cell-based traffic control formulation: Strategies and benefits of dynamic timing plans. Transportation Sci. 35(2):148–164.Link, Google Scholar
- (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.Google Scholar
- (1996) A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans. Neural Networks 7(1):16–29.Crossref, Google Scholar
- (1990) Traffic Flow Fundamentals (Prentice-Hall, Upper Saddle River, NJ).Google Scholar
- (2007) Freeway traffic estimation within particle filtering framework. Automatica 43(2):290–300.Crossref, Google Scholar
- (2003) Traffic density estimation with the cell transmission model. Proc. 2003 Amer. Control Conf., Denver, Vol. 5 (IEEE, Piscataway, NJ), 3750–3755.Crossref, Google Scholar
- (2004) Methodological calibration of the cell transmission model. Proc. 2004 Amer. Control Conf., Vol. 1 (IEEE, Piscataway, NJ), 798–803.Crossref, Google Scholar
- (1970) River flow forecasting through conceptual models: Part 1–A discussion of principles. J. Hydrology 10(3):282–290.Crossref, Google Scholar
- (1993) A simplified theory of kinematic waves in highway traffic, part I: General theory. Transportation Res. Part B: Methodological 27(4):281–297.Crossref, Google Scholar
- (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.Link, Google Scholar
- (1990) Networks for approximation and learning. Proc. IEEE 78(9):1481–1497.Crossref, Google Scholar
- (1956) Shock waves on the highway. Oper. Res. 4(1):42–51.Link, Google Scholar
- (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.Google Scholar
- (1991) A general regression neural network. IEEE Trans. Neural Networks 2(6):568–576.Crossref, Google Scholar
- (2006) Neural networks for real-time traffic signal control. IEEE Trans. Intelligent Transportation Systems 7(3):261–272.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (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.Google Scholar
- Transportation Research Board (TRB) (2010) Highway Capacity Manual 2010 (Transportation Research Board, Washington, DC).Google Scholar
- (2002) Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooperative Transportation Dynamics 1(3):3.1–3.24.Google Scholar
- (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.Crossref, Google Scholar
- (2002) A note on the entropy solutions of the hydrodynamic model of traffic flow. Transportation Sci. 36(4):435–446.Link, Google Scholar
- (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.Google Scholar
- (1990) Spline Models for Observational Data, CBMS-NSF Regional Conf. Series Applied Math., Vol. 59 (SIAM, Philadelphia).Crossref, Google Scholar
- (2005) Real-time freeway traffic pattern estimation based on extended Kalman filter: A general approach. Transportation Res. Part B: Methodological 39(2):141–167.Crossref, Google Scholar
- (2007) Real-time freeway traffic pattern estimation based on extended Kalman filter. Transportation Sci. 41(2):167–181.Link, Google Scholar

