Fusion of weigh-in-motion and global positioning system data to estimate truck weight distributions at traffic count sites

Abstract Truck weight data is needed for a wide range of applications including but not limited to pavement design, weight enforcement, traffic monitoring, and freight transportation planning. Unfortunately, the low spatial resolution of weight sensors along the transportation network can limit these and other potential applications. The main contribution of this paper is a methodology to estimate gross vehicle weight (GVW) distributions at traffic count sites, which collect traffic volumes but currently do not have the ability to directly measure vehicle weight. This paper presents a method for estimating GVW distributions of five-axle tractor-trailers (“3-S2”) at traffic count sites by fuzing weight data from weigh-in-motion (WIM) sites with position data from global positioning system (GPS) equipped trucks. Truck travel patterns derived from a truck GPS database were used to determine the degree to which a WIM and traffic count site are spatially related. A GVW distribution was then estimated by combining Gaussian mixture models (GMM) estimated at WIM sites defined to be spatially related to the traffic count site. A leave-one-out cross validation framework allowed for comparisons of estimated and measured GVW distributions at each WIM site. Coincidence ratios and two-sample Kolmogorov-Smirnov (KS) tests were used as comparison metrics for a case study of 112 WIM sites in California. The proposed methodology provided better goodness-of-fit between observed and estimated GVW distributions compared to a baseline approach which defined the spatial relation between sites using great circle distances (GCD).

[1]  Imad L. Al-Qadi,et al.  LTBP Program’s Literature Review on Weigh-in-Motion Systems , 2016 .

[2]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[3]  Indrajit Chatterjee,et al.  A statistical process control approach using cumulative sum control chart analysis for traffic data quality verification and sensor calibration for weigh-in-motion systems , 2017, J. Intell. Transp. Syst..

[4]  Shin-Ting Jeng,et al.  Wavelet–k Nearest Neighbor Vehicle Classification Approach with Inductive Loop Signatures , 2013 .

[5]  Mecit Cetin,et al.  Numerical Characterization of Gross Vehicle Weight Distributions from Weigh-in-Motion Data , 2007 .

[6]  Stephen G. Ritchie,et al.  Determining optimal sensor locations under uncertainty for a truck activity monitoring system on California freeways , 2019 .

[7]  Kevin D Hall,et al.  Development and Influence of Statewide Axle Load Spectra on Flexible Pavement Performance , 2007 .

[8]  Curtis Dahlin.  PROPOSED METHOD FOR CALIBRATING WEIGH-IN-MOTION SYSTEMS AND FOR MONITORING THAT CALIBRATION OVER TIME , 1992 .

[9]  P. Deb.  Finite Mixture Models , 2008 .

[10]  Xuegang Ban,et al.  Identifying multiclass vehicles using global positioning system data , 2018, J. Intell. Transp. Syst..

[11]  Henry F. Inman,et al.  The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities , 1989 .

[12]  Stephen G. Ritchie,et al.  Integration of Weigh-in-Motion (WIM) and inductive signature data for truck body classification , 2016 .

[13]  Chih-Sheng Chou,et al.  Recommended Procedure to Adjust Inaccurate Weigh-in-Motion Data , 2016 .

[14]  Michael M Marti,et al.  QUALITY CONTROL OF WEIGH-IN-MOTION SYSTEMS USING STATISTICAL PROCESS CONTROL , 1995 .

[15]  H. Akaike.  Likelihood of a model and information criteria , 1981 .

[16]  Stephen G. Ritchie,et al.  Long-Distance Truck Tracking from Advanced Point Detectors Using Selective Weighted Bayesian Model , 2017 .

[17]  Maliha S. Nash,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 2001, Technometrics.

[18]  Shin-Ting Jeng,et al.  Weigh-In-Motion Station Monitoring and Calibration Using Inductive Loop Signature Technology , 2015 .

[19]  Jeffrey Short,et al.  Estimation of Statewide Origin–Destination Truck Flows from Large Streams of GPS Data , 2015 .

[20]  Michel Ghosn,et al.  Protocols for Collecting and Using Traffic Data in Bridge Design , 2008 .

[21]  Jorge A Prozzi,et al.  Effect of Weigh-in-Motion System Measurement Errors on Load-Pavement Impact Estimation , 2007 .

[22]  Hesham Rakha,et al.  Field Evaluation of Truck Weigh Station Operations , 2006, J. Intell. Transp. Syst..

[23]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[24]  Christopher M. Monsere,et al.  Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data , 2011, J. Intell. Transp. Syst..

[25]  Dar-Hao Chen,et al.  Traffic volume and load data measurement using a portable weigh in motion system: A case study , 2016 .

[26]  Sarah Hernandez,et al.  Estimation of Average Payloads from Weigh-in-Motion Data , 2017 .

[27]  Stephen G. Ritchie,et al.  Truck Body Configuration Volume and Weight Distribution , 2015 .

[28]  Darcy M. Bullock,et al.  Quality Control Procedures for Weigh-in-Motion Data , 2004 .