A system includes a sensor network comprising at least two optical fibers coupled to a pavement. Each optical fiber includes one or more optical sensors installed a predetermined distance from one or more adjacent optical fibers. The one or more optical sensors are configured to produce a wavelength shift signal. A processor is configured to determine one or both of one or more attributes of one or more objects travelling on the pavement and a traffic condition of the pavement based on the wavelength shift signal. A transmitter is configured to transmit the one or more attributes to a predetermined location.

A sensor network comprises at least one lateral optical fiber and at least one longitudinal optical fiber. The lateral fiber comprises optical sensors coupled to a pavement in a transverse orientation relative to a direction of vehicle travel along the pavement. The longitudinal fiber comprises optical sensors coupled to the pavement in a longitudinal orientation relative to the direction of vehicle travel. The optical sensors are configured to produce wavelength shift signals comprising one or more lateral strain signals associated with the lateral fiber and one or more tangential strain signals associated with the longitudinal fiber. A processor is operatively coupled to the sensor network and configured to determine a weight of vehicles moving along the pavement based on the lateral and tangential strain signals. A transmitter is operatively coupled to the processor and configured to transmit the weight of vehicles to a predetermined location.

A load meter includes a detector, a storage unit, and a load calculator. The detector detects, by using a captured image obtained by capturing a road and a vehicle present on the road, a displacement amount in the captured image, the displacement amount corresponding to displacement caused on the road by application of a load of the vehicle. The storage unit stores information indicating a relation between the load and the displacement amount. The load calculator calculates the load based on the displacement amount and the information.

A Weigh-In-Motion force transducer includes a housing profile and a piezoelectric measuring arrangement that generates electric polarization charges from a reaction force acting along a force introduction axis via the housing profile, which includes a tubular part internally defining a cavity containing the piezoelectric measuring arrangement under mechanical prestress along the force introduction axis. The tubular par is configured to be expanded along the force introduction axis by a mounting force acting along a mounting force axis and applied to the tubular part. The configuration of the tubular part in a cross-sectional plane defined by the force introduction axis and the mounting force axis is elliptical in shape with a major semiaxis extending along the mounting force axis and a minor semiaxis extending along the force introduction axis.

The invention relates to a WIM sensor for detecting loads of vehicles on a roadway segment when a wheel of a vehicle crosses the WIM sensor arranged flush with the roadway surface in the roadway segment. The WIM sensor is formed as an elongated profile along a longitudinal axis and defines a space therein. A force sensor configured to generate a force sensor signal corresponding to a dynamic ground reaction force when the wheel crosses the force sensor is arranged in the space. An electro-acoustic transducer is arranged in the space and configured to measure sound waves and accordingly generate a transducer signal.

A device for weighing aircraft includes a weighing platform configured to receive a undercarriage leg of the aircraft and to generate weighing signals, a first calculation unit configured to calculate weighing information from the weighing signals generated by the weighing platform, a communication unit configured to transmit to a central device and to receive signals including at least one signal representing the weighing information calculated by the calculation unit, and a ground rolling unit configured to move the weighing platform over a surface.

The present invention relates to a device for determining a weight of a vehicle, the device being configured to: obtain a set of weights derived from in-motion weighing of the vehicle on a weighing bridge, wherein each weight in the set of weights represents one or more axle weights of the vehicle; select one or more weights in the set of weights such that the one or more selected weights together represent all axles of the vehicle and each axle of the vehicle is only represented once; determine a total weight of the vehicle based on the one or more selected weights. Furthermore, the invention also relates to a system and corresponding methods.

A method for determining the weight G of a vehicle (1) while the vehicle is travelling on a section (3) of road (4) uses at least one weigh-in-motion (WIM) sensor (5) that is narrower than the length of the footprint of a wheel in the direction of vehicle travel. When the vehicle (1) travels along this section (3) of road (4) both the wheel loads F.sub.i(t) of all the wheels (2) or twin wheels i, and the speed v.sub.i(t) of the vehicle (1) during the entire passing are acquired as time functions, and during evaluation of the data for determining the weight G the speeds v.sub.i(t) and their change over time are used as weighting of the simultaneously determined wheel loads F.sub.i(t).

A method determines load values from strain values. The load values correspond to vertical loads exerted by wheels of vehicles traveling along a trafficway, wherein the trafficway has a surface layer arranged on a subconstruction. The method includes providing a model of at least the surface layer, conducting a training phase, and conducing a production phase. Further disclosed is a method for determining weights of vehicles in motion on a trafficway. A plurality of measured strain values is determined from a plurality of strain gauges. A plurality of load values is determined, based at least on the plurality of measured strain values. An indication of a weight of a vehicle is determined, based at least on the plurality of load values. A roadside processing unit, a computing system, and a machine-readable medium are also provided.

The present disclosure provides a real-time vehicle overload detection method based on a convolutional neural network (CNN). The present disclosure detects a road driving vehicle in real time with a CNN method and a you only look once (YOLO)-V3 detection algorithm, detects the number of wheels to obtain the number of axles, detects a relative wheelbase, compares the number of axles and the relative wheelbase with a national vehicle load standard to obtain a maximum load of the vehicle, and compares the maximum load with an actual load measured by a piezoelectric sensor under the vehicle, thereby implementing real-time vehicle overload detection. The present disclosure has desirable real-time detection, can implement no-parking vehicle overload detection on the road, and avoids potential traffic congestions and road traffic accidents.