Using Big Data to Uncover Travel Patterns: A Case Study on Fresno and Clovis Cities

Abstract: Understanding travel behavior, including mode choices and mobility patterns, is critical for planning and developing efficient transportation systems, for example, optimizing resource allocation over the different transportation modes to offer suitable and equitable alternative transportation options. Traditionally, large-scale survey data, such as household travel surveys, have been used to capture and model travel behavior. However, recent technological advances have allowed for novel sources of data. Data from novel sources (such as mobile location data and consumer and resident data) are not as commonly utilized, even though they can provide valuable insights into individual travel behaviors. These novel data sources offer high accuracy and relevance for transportation analysis. This research presents a case study utilizing one of these novel datasets to analyze and understand spatial and modal travel patterns in Fresno and Clovis, California. The used dataset was procured from Replica HQ. Replica HQ’s data are sourced from different sources, including mobile location data, consumer and resident data, the built environment, economic activity (such as payment network and merchant acquisition data), and ground truth data, and fused to offer a more comprehensive view of travel behaviors. The findings of our work provide critical insights into the strengths and limitations of utilizing these novel data sources to understand travel behaviors and patterns by different transportation modes. These insights provide valuable information for transportation planners and policymakers that can aid them in developing more efficient and well-adapted transportation systems in their cities and regions.