I am a graduate student in Transportation Engineering. I know basic Statistics but have never done any time-series/ spatio-temporal data analysis.
I conducted an experiment on a driving simulator which produced vehicle trajectory data on a test road network. These data (for each driver) contain time frames, vehicle position (x, y, z Cartesian coordinates), distance to lead vehicle, etc. I use R language for analysis.
I want to find similarities in the vehicle trajectories. Let's say, I have 10 trajectories, each representing the journey completed by an individual driver. I want to see how many trajectories are "similar" in terms of time and space. The road network consists of various ramps, so I expect that driver behavior is likely to be different close to ramps than on the highway. Additionally, speeds/accelerations of different drivers might be (dis)similar at different locations.
I have extensively searched similarity/clustering/segmentation algorithms for time-series and spatio-temporal data. In time-series examples, I find that mostly seasonality and decomposition is discussed. And the data contain date/time stamp. However, my data only contain the time frames.
In spatio-temporal data examples, I find latitude, longitude variables. In my case, I only know about proximity to ramps, Cartesian coordinates, distance to lead vehicle, etc. Therefore, my questions are: Can I treat my data as spatio-temporal data? What properties of time-series are applicable to trajectory data sets? What similarity measures can be used on these multi-variate data? Please help with links/examples/implementations in R. Thanks in advance.