Comparing connected vehicle and mobile phone data for urban mobility analysis: Examining mobility diversity and spatial interaction across income groups

Source abstract

Connected vehicle (CV) data are increasingly available and widely used in transportation engineering for traffic monitoring, safety analysis, and infrastructure planning. However, the representativeness of CV data in general urban mobility analysis remains underexplored, raising concerns about potential biases between observed mobility patterns in CV data and actual travel behaviors, particularly across different demographic and socioeconomic groups. This study compares Wejo connected vehicle (CV) data with SafeGraph mobile phone records covering boarder population to reveal the representativeness of connected vehicle data in the context of urban mobility analysis. Using entropy-based measures of destination income diversity and normalized inter-neighborhood interaction strength, we examine how each dataset reflects mobility structures across income groups in San Antonio, Texas. Results show that SafeGraph data capture more multimodal and socially integrative travel behaviors, particularly among low-income communities, while Wejo data primarily reflect routine, vehicle-based movements concentrated among higher-income users. Interaction patterns in the CV data are more spatially clustered, with stronger flows observed within affluent neighborhoods. These differences underscore the behavioral and demographic selectivity embedded in CV data and point to important limitations when using such data to analyze mobility-based segregation. The findings contribute to ongoing efforts to evaluate the representativeness of emerging mobility datasets and their implications for urban spatial analysis.