TRB 2016 Blue Ribbon Committee
16th National Transportation Planning Applications Conference

Application of High-Resolution Data to Determine the Impact of LOS on Driving Behavior on Arterials

Corresponding Author: Behzad Aghdashi, Institute for Transportation Research and Education

Presented By: Behzad Aghdashi, North Carolina State University


In recent years, as data collection technologies have emerged, the need to utilize big data for planning purposes and the associated challenges have expanded. Decisions regarding improving both mobility and safety aspects of surface streets primarily rely on higher-level observations of actual operation of the facilities. The aim of this ongoing research is to develop a framework to use big data sources in mobility improvement decision-making processes considering changes in driving behavior and safety.

Currently, with access to massive probe based data sources (e.g. INRIX, and etc), decisions on development investments solely are focused on improving the mobility performance measures or Levels of Service (LOS). However, improving LOS can change the attitude of a driver, and as a result safety risks vary. This research focuses on establishing a connection between LOS in arterials and driving behavior using massive data collected on mobility and safety.

The underlying technology for this research is i2D (intelligent to drive) devices that connect to vehicles via onboard diagnostic port. i2D devices collect second-by-second high-resolution vehicles trajectories along with a number of different attributes such as three-dimensional accelerations. These data are used to evaluate the driver’s aggressiveness. The research team has collected over 35 million seconds of trajectories with a fleet of 40 volunteer drivers.

More than 5 years of NPMRDS (National Performance Measure Research Data Set) data have been collected from probe based data sources that provide LOS information for all major arterial segments in a 5 minutes resolution. By comparing these aggressiveness estimates for a set of 40 volunteer drivers with mobility performance measures read from probe based data sources, the relationship between LOS and drivers behavior is constructed. The results can be used to estimate safety risks at the time of mobility-oriented investments to better evaluate the outcome of road construction in society.


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