Mobile Device Identifier Data Collection and Analysis for Transportation Intelligence Purposes: Applications, Uncertainty and Privacy

Abstract

Travel evaluation metrics have been historically biased towards motorized modes, which dominate land transportation choices and are partially responsible for numerous environmental and health issues facing our society today. Encouraging active travel solutions is seen as a means of improving sustainability, health and cohesiveness of a community. Unfortunately, information regarding volume, trip origin and destination, travel time and personal interactions is difficult to obtain due to a lack of sensor infrastructure and unrestricted movement of these modes. Therefore, information is often limited to annual surveys and model estimates which are insufficient to address the increasing needs of sustainable planning and large scale behavior studies. The ubiquity of mobile devices, coupled with their need to communicate wirelessly, provides a wealth of data that, if properly handled, can be used to quickly enhance understanding and recognition of transportation patterns. This data provides an opportunity to create a very low maintenance sensor infrastructure that is readily scalable and is easy to deploy and use for a number of transportation purposes, from long-term city planning to day to day traffic operations. Of particular interest are the spatial and temporal patterns that evolve as a result of daily human activity in very dense urban cores and campuses, where non-motorized modes dominate and mobile devices are highly prevalent. Traditional sensing approaches have often failed to capture non-motorized travel movements, resulting in data bias. Mobile device data has been viewed as a potential solution to these bias issues. The research conducted within this dissertation focuses on discussing and developing Bluetooth Media Access Control (MAC) based travel data collection approaches and their implications. The challenges of working with opportunistically collected data and the resulting uncertainties are discussed and a number of approaches for mitigating them are proposed. Specifically, the work contained provides a MAC data collection analysis framework, develops algorithms and techniques for the reduction of uncertainty in MAC address-based travel data collection approaches and proposes and evaluates a novel pedestrian data collection approach using an app-based MAC sensing approach.