  Vehicle volume serves as a critical metric and the fundamental basis for
traffic signal control, transportation project prioritization, road maintenance
plans and more. Traditional methods of quantifying vehicle volume rely on
manual counting, video cameras, and loop detectors at a limited number of
locations. These efforts require significant labor and cost for expansions.
Researchers and private sector companies have also explored alternative
solutions such as probe vehicle data, while still suffering from a low
penetration rate. In recent years, along with the technological advancement in
mobile sensors and mobile networks, Mobile Device Location Data (MDLD) have
been growing dramatically in terms of the spatiotemporal coverage of the
population and its mobility. This paper presents a big-data driven framework
that can ingest terabytes of MDLD and estimate vehicle volume at a larger
geographical area with a larger sample size. The proposed framework first
employs a series of cloud-based computational algorithms to extract multimodal
trajectories and trip rosters. A scalable map matching and routing algorithm is
then applied to snap and route vehicle trajectories to the roadway network. The
observed vehicle counts on each roadway segment are weighted and calibrated
against ground truth control totals, i.e., Annual Vehicle-Miles of Travel
(AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is
implemented on the all-street network in the state of Maryland using MDLD for
the entire year of 2019. Results indicate that our proposed framework produces
reliable vehicle volume estimates and also demonstrate its transferability and
the generalization ability.
