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. 2018 Oct 25;18(11):3623.
doi: 10.3390/s18113623.

A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Shivam Gupta  1 , Albert Hamzin  2 , Auriol Degbelo  3
Affiliations

A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Shivam Gupta et al. Sensors (Basel). .

Abstract

Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios-low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)-demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.

Keywords: WiFi signals; low cost sensors; open hardware; smart cities; traffic counter; traffic monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustration of deployment plan for the proposed hardware system.
Figure 2
Figure 2
Experimental setup: Scenario 1 (Low traffic road: Heisenbergstraße).
Figure 3
Figure 3
Experimental setup: Scenario 2 (Heavy traffic road: Steinfurter Straße).
Figure 4
Figure 4
Illustration of the web-application developed for ground-truth data video stream analysis.
Figure 5
Figure 5
Overall flow of analysis.
Figure 6
Figure 6
Illustration of time window and associated signal fluctuation pattern identification (Units: Time = Milliseconds, Strength = dBm).
Figure 7
Figure 7
Flow chart of algorithm for vehicle detection.
Figure 8
Figure 8
Parameters summary statistics for Heisenbergstrasse.
Figure 9
Figure 9
Parameters summary statistics for Steinfurter Straße.
See this image and copyright information in PMC

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