Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively

Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic

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