National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

Abstract

Along with the development of information and positioning technologies, there emerges passively collected location data that contain location observations with time information from various types of mobile devices. Passive location data are known for their large sample size and continuous behavior observations. However, they also require careful and comprehensive data processing and modeling algorithms for privacy protection and practical applications. In the meantime, the travel demand estimation of origin–destination (OD) tables is fundamental in transportation planning and analysis. There is a lack of national OD estimation that provides time-dependent travel behaviors for all travel modes. Passively collected location data appeal to researchers for their potential of serving as the data source for estimation and monitoring of large-scale multimodal travel demand. This research proposes a comprehensive set of methods for passive location data processing including data cleaning, activity location and purpose identification, trip-level information identification, social demographic imputation, sample weighting and expansion, and demand validation. For each task, the paper evaluates the state-of-the-practice and state-of-the-art algorithms and develops an applicable method jointly considering different features of various passive location data sources, imputation accuracy, and computation efficiency. The paper further examines the viability of the method kit in a national-level case study and successfully derives the multimodal national-level OD estimates with additional data products, such as trip rate and vehicle miles traveled, at different geographic levels and temporal resolutions.