Abstract
Traffic flow monitoring using magnetic wireless sensor networks in chaotic cities of developing countries represents an emergent technology. One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. The proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). The novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. The improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). The performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. The results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state.
Highlights
Wireless sensor networks (WSNs) have been deployed in various sensing tasks in ambiguous conditions where wired sensors are not cost effective
Scanning and Decision Algorithm II (SDA-II) is a signalprocessing algorithm improvement of the SDA algorithm [4]. e new algorithm is based on a moving-average model operating in the time domain
Timer Block. e timer block generates vehicular travelled time (TT) which is considered as the time used by the passing vehicle at the sensor observability zone
Summary
One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. E proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). E novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. E improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). E performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. E results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). e performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. e results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state
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