Potentialities of Data-Driven Nonparametric Regression in Urban Signalized Traffic Flow Forecasting
作者： Byoungjo Yoon , Hyunho Chang
DOI: 10.1061/(ASCE)TE.1943-5436.0000662
关键词: Nonparametric regression 、 Field (computer science) 、 Computer science 、 Intelligent transportation system 、 Transport engineering 、 Traffic flow 、 Key (cryptography) 、 Data-driven 、 Obstacle 、 Data management
摘要: AbstractSingle-interval forecasting of traffic variables plays a key role in modern intelligent transportation systems (ITSs). Despite the achievements advanced ITS literature, forecast modeling urban signalized flow, which shows rapid-intensive fluctuations associated with nonlinear and nonstationary behavior temporal evolution, is still one its big challenges. From perspective field experts, mathematical complexity an model also renewal obstacle practice. On other hand, accessibility large volumes historical data concurrent management used to access them provide data-driven nonparametric regression opportunity In order address these problems effectively, this paper proposes k nearest neighbor (KNN-NPR) methodology be tested against vast quantities real volume collected from arterials. Th...
sci-hub.se 参考文章(30)
Dominique Guégan, Justin Leroux, Forecasting chaotic systems: The role of local Lyapunov exponents Chaos Solitons & Fractals. ,vol. 41, pp. 2401- 2404 ,(2009) , 10.1016/J.CHAOS.2008.09.017
Brian L. Smith, R. Keith Oswald, MEETING REAL-TIME TRAFFIC FLOW FORECASTING REQUIREMENTS WITH IMPRECISE COMPUTATIONS Computer-aided Civil and Infrastructure Engineering. ,vol. 18, pp. 201- 213 ,(2003) , 10.1111/1467-8667.00310
M.G. Karlaftis, E.I. Vlahogianni, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights Transportation Research Part C-emerging Technologies. ,vol. 19, pp. 387- 399 ,(2011) , 10.1016/J.TRC.2010.10.004
Billy M. Williams, Lester A. Hoel, Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results Journal of Transportation Engineering-asce. ,vol. 129, pp. 664- 672 ,(2003) , 10.1061/(ASCE)0733-947X(2003)129:6(664)
A. Stathopoulos, M. Karlaftis, Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas Transportation Research Record. ,vol. 1768, pp. 135- 140 ,(2001) , 10.3141/1768-16
N. H. Packard, J. P. Crutchfield, J. D. Farmer, R. S. Shaw, Geometry from a Time Series Physical Review Letters. ,vol. 45, pp. 712- 716 ,(1980) , 10.1103/PHYSREVLETT.45.712
Francis J. Mulhern, Robert J. Caprara, A nearest neighbor model for forecasting market response International Journal of Forecasting. ,vol. 10, pp. 191- 207 ,(1994) , 10.1016/0169-2070(94)90002-7
Hongbin Yin, S.C. Wong, Jianmin Xu, C.K. Wong, Urban traffic flow prediction using a fuzzy-neural approach Transportation Research Part C-emerging Technologies. ,vol. 10, pp. 85- 98 ,(2002) , 10.1016/S0968-090X(01)00004-3
Joe Whittaker, Simon Garside, Karel Lindveld, Tracking and predicting a network traffic process International Journal of Forecasting. ,vol. 13, pp. 51- 61 ,(1997) , 10.1016/S0169-2070(96)00700-5
Antony Stathopoulos, Loukas Dimitriou, Theodore Tsekeris, Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow Computer-aided Civil and Infrastructure Engineering. ,vol. 23, pp. 521- 535 ,(2008) , 10.1111/J.1467-8667.2008.00558.X