M2S2 – Measuring the on-board weighting of heavy vehicles

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Author

László Ketskeméty

Mini Project ID

BMEMPLOAD2

Description

The case: Illegal overweighting of heavy goods vehicles is a major problem throughout Europe. Heavy goods vehicles, buses and coaches transporting goods in Europe must comply with certain rules on weights and dimensions for road safety reasons, and to avoid damage to roads, bridges and tunnels.

The aim of the research is to build a European load measurement network that is suitable for estimating the load on the entire road network, which is important information in scheduling road network maintenance. The development of the measuring network also helps to filter out overweight transport vehicles. A complete measuring network to be installed on all road sections is very expensive and can only be implemented in a long time. Therefore, the goal is a strategic scheduling of deployment that will result in a pattern that is well representative the statistical population of the entire European road sections.

Sector

VET

Data

A database on the population of European road sections is to be created, in which, in addition to the geographical phi and lambda coordinates of the road sections, the following data is available: data describing the quality of the road section, traffic data, weight load data and that whether the road section is equipped with a WIM (Weighting In-motion) measuring point and what are the parameters of this measurement station (transit speed, capacity, measurement accuracy, date of last calibration, etc.).

Model

N/A

Calculation

In order to design a representative sample, the following data are required: (after by segmenting the population of European road sections into homogeneous parts by clustering) the cluster identification code and the number of elements of each cluster, the number of already installed measuring stations in each cluster.

Solution

Road sections are clustered using a suitable d metric. Due to the large population, a dynamic method seems to be advantageous. The algorithms are grouped in such a way that according to d the sections with small “distance” are placed in a group (cluster), while the elements of different groups have a large “distance” according to d. Metrics can be edited / selected experimentally. You have to choose from several options that reproduce an expert separation (the so-called training set) with the utmost precision.

Presentation

N/A

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