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The digital highway data vehicle (DHDV) was utilized for data collection, in which three Euler angles, driving speed, and acceleration of survey vehicle were measured with an inertial measurement unit (IMU).
These are commonly accepted characteristics of a slope failure, and such rules obtained from the constructed decision tree from Korean highway data suggested that the analyzed data and the methodology were statistically valid.
Transportation professionals still face the onerous task of organizing highway data into suitable forms to support decisions concerning highway maintenance, rehabilitation, traffic control, highway monitoring, and projects prioritization.
These issues have given rise to a surge in the demand for effective practices and tools that can integrate, manage, and analyze highway data (Parida and Aggarwal 2005).
Accordingly, visualization of highway data has been expedited by current advances in information technology and data collection technologies (Khattak and Shamayleh 2005).
Abellan et al. [12] developed various decision trees to extract different decision rules for different trees to analyze two-lane rural highway data of Spain.
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For simulations using smooth terrain surfaces, such as racetracks or highways, data can be down-sampled through a mean interpolation method to smooth the artefacts of inertial errors.
Additionally, future work may also include the integration of highway conditions data, accidents data, and traffic information to provide a central source of information for the district to aid in project planning.
The sources used could be broadly grouped as GIS shapefiles, highway inventory data, DCIS projects data, and COMPASS projects data.
The Management Center (MC) plays a role as centralized data center for highway information computing, storage, and monitoring.
For instance, along with highway performance data from the Federal Highway Administration and speed data collected by companies, the 2015 Urban Mobility Scorecard has been reported [2].
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