Pedestrian traffic monitoring using Wi-Fi technology
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The study of pedestrian traffic has been investigated by many organizations such as governments, city councils and businesses. The knowledge of pedestrian traffic behaviour can be useful for planning better services for citizens. To perform the surveys for obtaining this knowledge, a number of methods were developed for pedestrian monitoring, such as, hand counting, video camera counting and infrared counting. However each of these methods has its application limitations such as excessive human interaction, narrow observable region, limited applicable conditions and false detection. With the development of Wi-Fi Positioning System, the Wi-Fi technology has been introduced into pedestrian traffic monitoring. The Wi-Fi enabled smartphone which is carried by most of the pedestrian periodically transmits Wi-Fi packets non-intrusively. A number of essential information describing pedestrian traffic behaviours can be extracted from the Wi-Fi packets according to the unique MAC address of each device and received signal strength. This thesis described a mobile tracking system for tracking smartphones, based on these extracted information. This system uses sniffing stations which integrate with Wi-Fi devices, 3G devices, processors and sensors to capture the Wi-Fi packets and transmits data to remote server for storage. In order to evaluate the system performance and optimize the system efficiency, a series of tests and experiments were designed by considering the three aspects: hardware configuration, channel hopping methods, and pedestrian behaviours. Based on the results of tests and experiments, the optimized system configuration has been determined to ensure a good ability of packets capturing. Given the nature of outdoor environment condition, the received signal strength measurements are highly inconsistent and are subject to change depending on the devices, human and environment factors. It is a challenge that using received signal strength to localize a smartphone. To address this issue, an EEMD based localization method is proposed in this thesis. Based on the field test result, the EEMD based localization method can smooth out captured data of the received signal strength and improve the localization accuracy. In the intersection of a street, the pedestrian can move either in north-south direction or east-west direction, in different speeds and carry various brands of smartphones. The deployment of mobile tracking in such a complex environment becomes an interesting topic. A simulation platform is developed in this thesis and the simulation platform consists of a number of models for describing the street environment under different conditions. Moreover, the intersection of a street is divided into central area and boundary area. Deployment scenarios were simulated to determine the best representable deployment scenario. Based on the simulation results, the sniffing station deployed at the central area has better system performance in terms of the capability of capturing the packet (number of captured packet). In addition, sniffing stations should be well placed to ensure the relative distance between each other is fallen into the classification determined by the expecting localization accuracy.
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