# Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure
## 4 Conclusions  
In this paper, we proposed a measure to compute the speed limit rate in each of the cells in which a region is segmented and in a specific temporal segment. The calculation is based on the historical data of trajectories as well as on the identification of slow segments according to a speed limit (_MaxSpeed_) given by the analyst.  
We present and formally define the concepts of _speed limit rate_ and (_maximal_) _slow segments_, which are the basic elements to determine the speed limit rate in a cell. Our proposal allows the analyst to segment the region of interest into cells of the desired size (\(l\)). Analogously, the time is also segmented. These two segmentations are the basis for developing the notion of a spatiotemporal scenario, a key concept of our proposal.  
Our algorithm was tested with historical data (10,485,760 trajectories), and the results stood to reason, e.g., weekdays were slower than weekends, 10 pm to 4 am were the fastest hours (this makes sense because there is usually less traffic during these days and hours, making it easier for vehicles to move faster), and in general, were consistent with the SPI. Although more experiments are needed, our experiments showed the feasibility of our proposal and its potential to identify (sub)regions with high or low-speed limit rates. This could be useful for traffic analysis, including traffic prediction ([PERSON] et al., 2013). For example, in our fourth experiment, we showed the evolution of the slow segments of a cell over the course of a day. This evolution can also be obtained for a week or a month and for each temporal segment. In this way, an analyst could detect, according to history, when (temporal segments) and where (roads) slower segments tend to appear in a cell. Another interesting analysis is to see how the slowness of a cell propagates to its adjacent cells in the next temporal segment. Thus, suppose that a driver is currently in cell A and needs to get to cell W. The driver can get to W by following adjacent cells F or L. As a result, if cell F has previously shown, in the next temporal segment, less slowness than cell L, the driver could select this cell because it is faster than cell L.  
As future work, we intend to enrich our speed limit rate measure as follows: For example, if only a few vehicles (e.g., less than 5) are passing through a cell and they are  
Figure 20: Slow segment evolution of a cell from 6 August 2013 to 13 August 2013 and from 8:00–10:00, 60 km/h.  
moving slowly (e.g., speed \(\leq\) 5 km/h), our speed limit rate measure reports the maximum value (speed limit rate = 1). This is consistent with our definition. However, we could include elements that try to explain why these vehicles are moving slowly, e.g., traffic lights, narrow or bad roads, speed bumps [26], a truck or a set of trucks (a cluster of moving objects) [27] that, e.g., every Monday morning, obstruct a certain road.  
Conceptualization, F.J.M.A. and G.G.; methodology, F.J.M.A., G.G., and S.Z.G.; software, F.J.M.A. and S.Z.G.; validation, F.J.M.A., G.G., and S.Z.G.; formal analysis, F.J.M.A. and G.G.; investigation, F.J.M.A., G.G., and S.Z.G.; resources, F.J.M.A. and S.Z.G.; data curation, F.J.M.A. and S.Z.G.; writing--original draft preparation, F.J.M.A. and G.G.; writing--review and editing, F.J.M.A. and G.G.; visualization, F.J.M.A. and S.Z.G.; supervision, F.J.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
* _MaxSpeed_ 10 km/hFigure 17 Results for the weekends, cell 6 with _MaxSpeed_ = 10 km/h.