A Smart Sub-Tile for Traffic Measurement

Marcel van der Horst, Rob Schaacke, Rick Kleijne, Robert van den Hoed

Research output: Contribution to conferencePosterOther research output

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The Amsterdam Sensor Lab is part of the Amsterdam University of Applied Sciences (AUAS) and its goal is to develop application specific sensor systems for applied research.

In order to (anonymously) measure, for instance, traffic without influencing people’s behaviour, a pressure sensing sub-tile is under development. It can be placed under a regular (0.3*0.3 m) tile in the pavement and, hence, cannot be seen by the public. Applications may range from evaluating the behaviour of pedestrians in crowds or on large open areas, to measuring the mechanical stress on bridges due to lorry traffic. The resulting data may be valuable to social scientists and municipal decision makers.

A preliminary demonstration model has been realized that can detect: weight (pressure), direction, and a speed estimate of pedestrians and cyclists, by measuring the direction and velocity of pressure changes. Data communication is wireless, e.g., via Bluetooth™, to a Raspberry Pi™ or computer for calibration and visualization of the data. The demonstration model has been working satisfactorily for about half a year in the corridors of the AUAS.

Pressure changes are measured with strain gauges using low-noise analogue instrumentation amplifiers and digitized with a 16 bit effective resolution. Current consumption is about 50 mA, the minimal detectable pressure is ca. 10 N and the maximal pressure ca. 1500 N. The data is refreshed every 2 ms.

New electronics for a second version of the sub-tile (under development) make it possible to detect the tiny signal of a 0.3 gram rubber object falling from a 10 cm height. Investigations and development are going on to increase the measurement range from this low-level (impulse) pressure up to a pressure of about 500 kN, and configuring multiple sub-tiles to a wireless sensor network, thus paving the way to a (smart) sensing pavement. Apart from that, possibilities to give an estimate of the kind of traffic using artificial intelligence will be investigated.
Original languageEnglish
Number of pages1
Publication statusPublished - 13 Dec 2017


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