Mining of Big Data to Analyze Scale Dependent Relationships between Travel Activities and Built Environment

Content introduction：

About the Talk 

The built environment has long been considered to have a significant impact on human activities and travel patterns. As a result, many public policies aim to protect and promote healthy travel behaviors by modifying the built environment. To better implement such policies, it is necessary to quantitatively verify the conceptual level linkages between build environment and travel behaviors. More recently, the increasing use of sensors is leading to the collection of large complex spatio-temporal datasets, often referred to as the Big Data, and to the prospect of discovering usable knowledge about travel behavior. So far most of the studies have emphasized on the technical aspect of the Big Data, such as increasing the speed of data handling. Studies utilizing the Big Data to solve research problems with a social context are generally lacking. My recent research focus has been on to develop new methods which allow the incorporation of contextual information in the processing of the Big Data. This talk will present some of the preliminary results on the study of the scale dependent dynamic relations between built environment and travel behaviors utilizing traffic data collected from a middle sized city in China. As indicated in the results, the Big Data provides an effective mean to understand and monitor traffic behaviors within a social context. The results show some interesting patterns in the relations between built environment and traffic behaviors. To my knowledge, the analysis in this research represents one of the first instances of using big data approach to understand the impact of built environment on travel behaviors.