Influence of floating car data quality on congestion identification

Abstract

This paper explores the usability of floating car data (FCD) of mixed quality in congestion analysis on motorways. The specific data quality aspects that we are investigating are the number and density of trajectories, the GPS interval, and the fleet representativeness. We use a dataset provided by the German Automobile Club ADAC covering the Tyrolean road network in 2016. From this dataset, trajectories along the A12 motorway were extracted for congestion analysis. These data are characterized by high GPS time interval, low number of trajectories, and are not representative for total traffic due to overrepresentation of trucks. The influence of these quality parameters on congestion identification is explored by analyzing the parameter distribution among different congestion types. In addition, we validate the results by comparing them with congestion incidents obtained from the stationary detector data (SDD) and examining the impact of quality parameters on the validation results. We find that the given data set does not allow short-term congestion patterns to be identified due to quality flaws. Especially the low number of trajectories proved problematic, whereas the influence of other parameters was less distinct. Despite these flaws, for large-scale congestion incidents, floating car data provide outcomes similar to those derived from stationary detectors.