A New Methodology To Infer Travel Behavior Using Floating Car Data

With the widespread use of location sensing technologies, huge volumes of vehicle trajectory data are increasingly generated. This opens up new opportunities for performing more sophisticated analyses for transportation systems. In this paper, a six-day dataset of floating car data (FCD) from Munich City is used attempting to infer drivers’ travel behavior. First, trips are spatially clustered such that each cluster contains trips associated with travel from one specific zone to another. Next, an innovative tool, called Relative Deviation Area (RDA), is introduced to help in understanding travel behavior in the resulting clusters. It aims to find the area by which a given trajectory is deviating from a referential trajectory (fastest route in this paper). RDA is computed for each trip in each cluster. This is followed by investigating the relationship between RDA and trip average speed (V). The resulting curves are found sensible and consistent, which indicates a potential association between the two variables. In addition, it is found that speed values at peak periods are lower than those at off-peak periods, for the same value of RDA. The results also show that RDA values for private cars are higher than those for all vehicle types.