Abstract
A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions. By analyzing individuals' mobility patterns revealed from their mobile phones, researchers and practitioners are now equipped to derive the largest trip samples for a region. Because of its ubiquitous use, extensive coverage of telecommunication services and high penetration rates, travel demand can be studied continuously in fine spatial and temporal resolutions. The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origin-destination (OD) matrices. The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling, integrated corridor management and online traffic simulations.
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