Effects of Revised Toll-by-Weight Policy on Truck Overloading Behavior and Bridge Infrastructure Damage Using Weigh-in-Motion Data: A Comparative Study in China

Since 2000, overloaded trucks have caused more than 50 bridges to collapse in China. In an effort to ensure the structural safety and extend the service life of the highway infrastructure, the Chinese government has proposed a series of policies in the past decade to mitigate truck overloading. This study aimed at investigating the effects of China’s recently revised toll-by-weight policy on truck overloading behavior and bridge infrastructure damage using weigh-in-motion data that spanned seven years (January 2011 to March 2018) and two successive toll-by-weight policies (with the new one implemented from August 2016), wherein truck data were measured from a typical national freeway segment. We first compared truck traffic volumes, compositions, and weight distributions under the initial and revised toll-by-weight policies. Next, we compared bridge infrastructure performance with respect to safety and fatigue based on the overloaded truck traffic observed under the initial and revised toll-by-weight policies. The results indicated that the revised toll-by-weight policy, which uses a stepwise incremental fee structure based on vehicle weight, was more effective at controlling truck overloading behavior and reducing bridge infrastructure damage than the initial toll-by-weight policy. Under the current policy, average daily truck volumes, overloaded truck proportions, and maximum truck weights decreased significantly. Concurrently, extreme and equivalent load effects for safety and fatigue assessments, respectively, decreased by an average of 20% for small- to medium-span bridges. Despite these noted improvements, overloaded truck traffic persisted, with loads often exceeding bridge design levels. This study’s findings can support future efforts by the Chinese government to further refine their toll-by-weight policies and subsequently ensure a safe and viable transportation network.

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Creation of Truck Axle Load Spectra Using Weigh-in-Motion Data

Journal of the Transportation Research Forum ◽

10.5399/osu/jtrf.47.4.1121 ◽

2010 ◽

Vol 47 (4) ◽

Author(s):

Yi Jiang ◽

Shuo Li ◽

Tommy Nantung ◽

Kirk Mangold ◽

Scott A. MacArthur

Keyword(s):

Design Method ◽

Pavement Design ◽

Smooth Transition ◽

Traffic Information ◽

Truck Traffic ◽

Motion Data ◽

Load Spectra ◽

Weigh In Motion ◽

Adjustment Factors ◽

Axle Loads

To assure a smooth transition from the existing pavement design methods to the new mechanistic-empirical design method in the Indiana Department of Transportation, a study was conducted to create truck traffic inputs and axle load spectra of major interstate and state-owned highways in Indiana. The existing pavement design method is based on the equivalent single-axle loads (ESAL), which converts wheel loads of various magnitudes and repetitions to an equivalent number of "standard" or "equivalent" axle loads. The new design method uses axle load spectra as the measure of vehicle loads on pavements. These spectra represent the percentage of the total axle applications within each load interval for single, tandem, tridem, and quad axles. In this study, the truck traffic and axle load spectra were developed based on the historical traffic data collected at 47 sites with weigh-in-motion technology. The truck traffic information includes hourly, daily, and monthly distributions of various types of vehicles and corresponding adjustment factors, the distributions of the number of axles of each type of truck, the weights of the axles, the spaces between the axles, the proportions of vehicles on roadway lanes, and the proportions of vehicles in driving directions. This paper presents the truck traffic and axle load spectra generated from the weigh-in-motion sites as required by the new pavement design method.

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The Impact of Cold and Snow on Weekday and Weekend Highway Total and Passenger Cars Traffic Volumes

The Open Transportation Journal ◽

10.2174/1874447801408010062 ◽

2014 ◽

Vol 8 (1) ◽

pp. 62-72 ◽

Cited By ~ 6

Author(s):

Hyuk-Jae Roh ◽

Satish Sharma ◽

Sandeep Datla

Keyword(s):

Statistical Significance ◽

Accurate Estimation ◽

Passenger Cars ◽

Passenger Car ◽

Motion Data ◽

Dummy Variable ◽

Weigh In Motion ◽

Traffic Volumes ◽

Adjustment Factors ◽

The Impact

Presented in this paper is an investigation of the impact of cold and snow on daily traffic volumes of total traffic and passenger cars. It is based on a detailed case study of five years of Weigh-In-Motion data recorded continuously at a highway site in Alberta, Canada. Dummy-variable regression models are used to relate daily traffic volumes with snowfall and categorized cold variables. The importance of all the independent variables used in the model are established by conducting tests of statistical significance. The total traffic and passenger car volumes are influenced by both the snowfall and the cold categories. Plots of the partial effect of each independent variable on the dependent variable are generated. It is found that a daily snowfall of 10 cm may cause a 25% reduction in the daily volume of passenger cars, and temperatures below -25°C may reduce the passenger car volumes by 10% or more. It is believed that the developed traffic-weather models of this study can benefit highway agencies in developing more advanced imputation method or identifying weather adjustment factors for accurate estimation of AADT from short duration traffic counts.

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Machine learning of Truck Traffic Classification groups from Weigh-in-Motion data

Machine Learning with Applications ◽

10.1016/j.mlwa.2021.100178 ◽

2021 ◽

pp. 100178

Author(s):

Narges Tahaei ◽

Jidong J. Yang ◽

Mi Geum Chorzepa ◽

S. Sonny Kim ◽

Stephan A. Durham

Keyword(s):

Machine Learning ◽

Traffic Classification ◽

Truck Traffic ◽

Motion Data ◽

Weigh In Motion

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Mapping of truck traffic in New Jersey using weigh-in-motion data

IET Intelligent Transport Systems ◽

10.1049/iet-its.2018.0055 ◽

2018 ◽

Vol 12 (9) ◽

pp. 1053-1061

Author(s):

Sami Demiroluk ◽

Kaan Ozbay ◽

Hani Nassif

Keyword(s):

New Jersey ◽

Truck Traffic ◽

Motion Data ◽

Weigh In Motion

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Comparative Accuracy Analysis of Truck Weight Measurement Techniques

Applied Sciences ◽

10.3390/app11020745 ◽

2021 ◽

Vol 11 (2) ◽

pp. 745

Author(s):

Sylwia Stawska ◽

Jacek Chmielewski ◽

Magdalena Bacharz ◽

Kamil Bacharz ◽

Andrzej Nowak

Keyword(s):

Measurement Techniques ◽

Highway Traffic ◽

Highway Infrastructure ◽

Measurement Systems ◽

Motion System ◽

Weigh In Motion ◽

Comparative Accuracy ◽

Bridge Weigh In Motion ◽

Rational Management ◽

Truck Weight

Roads and bridges are designed to meet the transportation demands for traffic volume and loading. Knowledge of the actual traffic is needed for a rational management of highway infrastructure. There are various procedures and equipment for measuring truck weight, including static and in weigh-in-motion techniques. This paper aims to compare four systems: portable scale, stationary truck weigh station, pavement weigh-in-motion system (WIM), and bridge weigh-in-motion system (B-WIM). The first two are reliable, but they have limitations as they can measure only a small fraction of the highway traffic. Weigh-in-motion (WIM) measurements allow for a continuous recording of vehicles. The presented study database was obtained at a location that allowed for recording the same traffic using all four measurement systems. For individual vehicles captured on a portable scale, the results were directly compared with the three other systems’ measurements. The conclusion is that all four systems produce the results that are within the required and expected accuracy. The recommendation for an application depends on other constraints such as continuous measurement, installation and operation costs, and traffic obstruction.

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Measuring heavy traffic with WIM-ROAD and WIM-BRIDGE systems in an urban environment

10.2749/ghent.2021.0331 ◽

2021 ◽

Author(s):

Maarten Soudijn ◽

Sebastiaan van Rossum ◽

Ane de Boer

Keyword(s):

Heavy Traffic ◽

Pressure Sensors ◽

Strain Sensors ◽

Traffic Load ◽

Structural Safety ◽

Special Focus ◽

The Road ◽

Weigh In Motion ◽

Set Up ◽

Calibration Program

<p>In this paper we present weight measurements of urban heavy traffic comparing two different Weigh In Motion (WIM) systems. One is a WIM-ROAD system using Lineas quartz pressure sensors in the road surface. The other is a WIM-BRIDGE system using optical fibre-based strain sensors which are applied under the bridge to the bottom fibre of a single span of the bridge deck. We have designed our tests to determine which system is most suited to Amsterdam. We put special focus on the accuracy that each system can achieve and have set up an extensive calibration program to determine this. Our ultimate goal is to draw up a realistic traffic load model for Amsterdam. This model would lead to a recommendation that can be used to re- examine the structural safety of existing historic bridges and quay walls, in addition to the current traffic load recommendations.</p>

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Estimation of Truck Traffic Volume from Single Loop Detectors with Lane-to-Lane Speed Correlation

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1856-11 ◽

2003 ◽

Vol 1856 (1) ◽

pp. 106-117 ◽

Cited By ~ 26

Author(s):

Jaimyoung Kwon ◽

Pravin Varaiya ◽

Alexander Skabardonis

Keyword(s):

Real Time ◽

Time Estimation ◽

The United States ◽

Truck Traffic ◽

Single Loop ◽

Loop Detectors ◽

Time Estimates ◽

Surveillance Technology ◽

Traffic Volumes ◽

Urban Freeway

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

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