2016Transportation research procediaOpen access

Investigation of Truck Weights and Dimensions Using WIM Data

Franziska Schmidt, Bernard Jacob, Frédéric Domprobst

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Abstract

This paper presents a study carried out with extensive weigh-in-motion (WIM) data collected on the French main road network, involving 3 millions of trucks. The data were collected by three WIM stations located on highly trafficked highways and motorways, continuously over a whole year. The data were analyzed using statistical software developed by the US National Science Foundation. The trucks of the traffic flow were classified into almost 20 categories, depending on the silhouette, number and spacing of axles, body configuration (trailer/semi-trailer), type of axles (steering, driving or standard axle) and wheel or tire (single, twin, wide). The data were analyzed by category. The location of the centre of gravity of the payload was calculated and the variability of its abscissa along the vehicle was analyzed in each category. The loading patterns and behavior were analyzed and the results reported here for 5-axle articulated trucks and 2-axle rigid truck with 2-axle trailer. Then, the axle load distributions were analyzed by axle rank and truck category, and modeled with multimodal Gaussian probability distribution functions. The modes have been determined using a maximum of likelihood method. These modes are useful to design endurance tests of truck tires. These investigations provide a better knowledge about truck loading, overloads and truck aggressiveness on infrastructure, and may lead to optimization policies. In this case, the results are used directly in the domain of tyre optimization.

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What this paper is about

This paper presents a study carried out with extensive weigh-in-motion (WIM) data collected on the French main road network, involving 3 millions of trucks. The data were collected by three WIM stations located on highly trafficked highways and motorways, continuously over a whole year. The data were analyzed using statistical software developed by the US National Science Foundation. The trucks of the traffic flow were classified into almost 20 categories, depending on the silhouette, number and spacing of axles, body configuration (trailer/semi-trailer), type of axles (steering, driving or standard axle) and wheel or tire (single, twin, wide). The data were analyzed by category. The location of the centre of gravity of the payload was calculated and the variability of its abscissa along the vehicle was analyzed in each category. The loading patterns and behavior were analyzed and the results reported here for 5-axle articulated trucks and 2-axle rigid truck with 2-axle trailer. Then, the axle load distributions were analyzed by axle rank and truck category, and modeled with multimodal Gaussian probability distribution functions. The modes have been determined using a maximum of likelihood method. These modes are useful to design endurance tests of truck tires. These investigations provide a better knowledge about truck loading, overloads and truck aggressiveness on infrastructure, and may lead to optimization policies. In this case, the results are used directly in the domain of tyre optimization.

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Available abstract

This paper presents a study carried out with extensive weigh-in-motion (WIM) data collected on the French main road network, involving 3 millions of trucks. The data were collected by three WIM stations located on highly trafficked highways and motorways, continuously over a whole year. The data were analyzed using statistical software developed by the US National Science Foundation. The trucks of the traffic flow were classified into almost 20 categories, depending on the silhouette, number and spacing of axles, body configuration (trailer/semi-trailer), type of axles (steering, driving or standard axle) and wheel or tire (single, twin, wide). The data were analyzed by category. The location of the centre of gravity of the payload was calculated and the variability of its abscissa along the vehicle was analyzed in each category. The loading patterns and behavior were analyzed and the results reported here for 5-axle articulated trucks and 2-axle rigid truck with 2-axle trailer. Then, the axle load distributions were analyzed by axle rank and truck category, and modeled with multimodal Gaussian probability distribution functions. The modes have been determined using a maximum of likelihood method. These modes are useful to design endurance tests of truck tires. These investigations provide a better knowledge about truck loading, overloads and truck aggressiveness on infrastructure, and may lead to optimization policies. In this case, the results are used directly in the domain of tyre optimization.

Key concepts: Truck, Axle, Trailer, Weigh in motion, Automotive engineering, Axle load, Engineering, Structural engineering

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