Developing Wireless Sensing Methods and Technologies for Enhanced Transit Rider and Non-Motorized Traffic Data

Thesis (Ph.D.)--University of Washington, 2020Real-time traffic data is essential for the advancement of emerging data-driven transportation technologies, e.g. deep learning-based traffic modeling approaches, autonomous vehicles, and urban computing. The existing sensing technologies work properly well for identifying the mobility patterns of motorized vehicles. However, it is still a big challenge for transportation agencies to obtain reliable data of transit riders and non-motorized travelers in today’s practice with existing traffic sensing technologies. To fulfill the data needs of understanding and modelling the mobility of transit riders and non-motorized traffic (bicycling, and walking), device-based wireless sensing methods and technologies have been developed to acquire relevant data. The basic idea of device-based wireless sensing technology is to capture the Media Access Control (MAC) address of Wi-Fi or Bluetooth enabled mobile devices. The MAC address can be used as a global unique identifier to re-identify mobile devices at different sensing locations, and thus travelers can be detected by identifying their mobile devices instead of detecting travelers directly. Such data acquisition method certainly provides a novel means for transit riders and non-motorized traffic data collection. Nevertheless, the limitations still exist for wireless sensing technologies due to the uncertainties caused by the sensing mechanism, including traffic mode uncertainty, localized spatial uncertainty, and population uncertainty. Such uncertainties bring considerable errors which can generate significant biases in the extracted traffic parameters from wireless sensing data. The major objective of this dissertation is to mitigate the impacts of the uncertainties based on the proposed wireless sensing methods and technologies for transit rider and non-motorized traffic data acquisition. large-scale field tests are conducted to evaluate the efficiency and accuracy of the proposed methodology. Besides the proposed methodology, a general method for establishing a wireless sensing system is presented for guiding implementations. This dissertation fills up the gap about effective traffic data acquisition methods for transit rider and non-motorized traffic, and thus supporting the transportation systems with reliability, equality, and sustainability