Application of Wireless Signal Scanning in Traffic Studies

With advances in communication technologies, innovative traffic data collection approaches have been developed. A means for this purpose is wireless technology which is sufficiently widespread among road users. Traffic data may be collected using signal scanners detecting wireless signals in the vicinity of them. As the data provided by signal scanners involve no direct information about the position of the signal source, the applications of this technology in traffic studies have been limited to finding some parameters in certain situations. The purpose is developing the applications of wireless signal scanning in traffic studies which require positional data of road users. This is achieved utilizing the potentials of received signal strength indicator (RSSI) of wireless signals transmitted by personal smart devices. Wi-Fi, Bluetooth Classic and Bluetooth Low Energy are three widespread signal modes, transmitted by popular beacons used in daily life. A comparative study of the field performance of these signal modes is conducted, investigating their characteristics important in gathering traffic flow parameters whenever positional data of road users are required. This provides the possibility of selecting the most suitable signal mode for the intended applications of the technology based on the requirements of the methods. A technique for positioning of beacons based on their transmitted signals, applicable in transportation studies is developed. This technique provides the possibility of positioning in intersections and their surrounding areas as well as congested road segments. The technique is based on the strength of signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors. The procedure is optimized, and the accuracy and functionality of the technique is improved via modification of the system arrangement and application of proper filtering algorithms. A method for detection and classification of turning movements applicable in small urban intersections is developed based on wireless signals. The method utilizes the time profiles of the RSSI values of the signals emitted by beacons carried by turning vehicles. The signals are collected by an array of signal scanners carefully located on the intersection approaches. Turning movements are classified comparing signature points of the RSSI-time profiles and their occurrence moments.