Annual Average Daily Traffic Estimation from Seasonal Traffic Counts

This paper presents an approach to estimation of the Annual Average Daily Traffic (AADT) from a one-week seasonal traffic count (STC) of a road section, with the aim of improving the interpretability of results with measures of non-specificity and discord. The proposed method uses fuzzy set theory to represent the fuzzy boundaries of road groups and measures of uncertainty. Neural networks are used to assign a road segment to one or more predefined road groups. The approach was tested with data obtained in the Province of Venice, Italy, for the period of the year in which STCs are taken. The method was found to produce accurate results.

[1]  Shinya Kikuchi,et al.  Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume , 2012 .

[2]  Luis M. Romero,et al.  Estimating Traffic Flow Profiles According to a Relative Attractiveness Factor , 2012 .

[3]  Monika Hanesch,et al.  The application of fuzzy C-means cluster analysis and non-linear mapping to a soil data set for the detection of polluted sites , 2001 .

[4]  Pawan Lingras,et al.  Statistical and Genetic Algorithms Classification of Highways , 2001 .

[5]  Fang Zhao,et al.  Application of Regression Analysis for Identifying Factors That Affect Seasonal Traffic Fluctuations in Southeast Florida , 2004 .

[6]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[7]  Shinya Kikuchi,et al.  Estimation of Annual Average Daily Truck Traffic Volume. Uncertainty Treatment and Data Collection Requirements , 2012 .

[8]  Stephen G. Ritchie.  A STATISTICAL APPROACH TO STATEWIDE TRAFFIC COUNTING , 1986 .

[9]  Fei Xu,et al.  Neural Networks as Alternative to Traditional Factor Approach of Annual Average Daily Traffic Estimation from Traffic Counts , 1999 .

[10]  Satish Sharma,et al.  IMPROVED METHOD OF GROUPING PROVINCEWIDE PERMANENT TRAFFIC COUNTERS , 1981 .

[11]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[12]  Gary A. Davis,et al.  BAYESIAN ASSIGNMENT OF COVERAGE COUNT LOCATIONS TO FACTOR GROUPS AND ESTIMATION OF MEAN DAILY TRAFFIC , 1996 .

[13]  Fang Zhao,et al.  Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree , 2006 .

[14]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[15]  Riccardo Rossi,et al.  Data Mining Methods for Traffic Monitoring Data Analysis: A Case study , 2011 .

[16]  Pawan Lingras,et al.  CLASSIFYING HIGHWAYS: HIERARCHICAL GROUPING VERSUS KOHONEN NEURAL NETWORKS , 1995 .

[17]  Ardeshir Faghri,et al.  Development of Integrated Traffic Monitoring System for Delaware , 1996 .

[18]  Gary A. Davis.  Accuracy of Estimates of Mean Daily Traffic: A Review , 1997 .

[19]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[20]  Joe Flaherty.  CLUSTER ANALYSIS OF ARIZONA AUTOMATIC TRAFFIC RECORDER DATA , 1993 .

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[22]  Arun Chatterjee,et al.  Estimation of Traffic Volume on Rural Local Roads , 2000 .

[23]  F. Zhao,et al.  Alternatives for Estimating Seasonal Factors on Rural and Urban Roads in Florida, Phase II , 2004 .

[24]  Ioannis Tsapakis,et al.  Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings , 2011 .

[25]  Fei Xu,et al.  Application of Neural Networks to Estimate AADT on Low-Volume Roads , 2001 .

[26]  Fei Xu,et al.  Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks , 2000 .

[27]  A. Faghri,et al.  Roadway seasonal classification using neural networks , 1995 .

[28]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[29]  Leo A. Goodman,et al.  Corrigenda: Measures of Association for Cross Classifications , 1957 .