A GIS-based methodology to observe driver behaviors and infrastructure characteristics through operating speeds

The degree of influence of operating conditions on traffic safety is difficult to assess because safety can be affected by all those factors constituting the road system. Today, mobility analytics and traffic monitoring rely on smart vehicles equipped with advanced technological devices that can provide georeferenced and real-time information. This study proposes a methodology divided into two steps to observe and interpret driver behaviors and infrastructure characteristics. The first one involves identifying probe vehicles along a sample of suburban roads, dividing the data in the two travel directions. It was decided to select just the high sampling frequency (< 4 s) floating car data which has run at least 90% of the entire route. Then, each probe vehicle, identified through a specific ID, was imported into the QGIS software to evaluate the user's actual behavior directly and qualitatively, especially by comparing speed drops of multiple drivers on specific road sections. In this way, the proposed study aims to identify in a GIS environment the critical sections along a road infrastructure by investigating the causes and factors that can influence the operating speeds.