International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 05 | May -2017
p-ISSN: 2395-0072
www.irjet.net
Analysis of GPS Trajectories Using Pearson and Spearman Correlation Sharmila J1 , Anolya Anette Avila2.E, Deepa. N3, Prabhakaran V.S4 1,2,3,4 School
of Information Technology and Engineering, VIT University,Vellore, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract- The dataset has been feed by Android app called Go Track. Go! Track is an application derived from a research project. The purpose of the application is to provide the user with a set of functionalities useful for daily traffic: 1- Suggestion of routes; 2- Forecast of time of arrival and departure of bus to a certain point / shelter; 3- Forecast of travel time; 4- Forecast of arrival time; 5- Forecast of congestion; 6- among several others Keyword: Pearson Correlation, Spearman Correlation and control chart. Introduction The effectiveness of these features, however, depends on data collected by the application as users intensify their use. Some of the data collected are: Average speed, Distance traveled, Latitude and longitude (points along the route), Time, Mode of transport (car or bus), Course evaluation (good, bad, regular) , Sunny), Bus evaluation (full, normal, empty).In the current version of the application, the only functionality present is the ratio of all existing bus stops in the city of Aracaju.To use GO! Track, simply activate the location device (GPS) of your smartphone and choose the means of transport you will use at the moment: car or bus. After choosing the transport mode, tracking will start immediately after the start button is activated. Pearson model is used to find the relationship between the attributes x and y based on the value of r. Pearson helps us to find how closely an attribute is associated with other attributes Based on the analysis of data we can decide which attribute can be considered and which attribute can be neglected. For instance, in Pearson method if the value of r is more than 0.5 then the attributes are considered to be strongly related and if it is below 0.3 the attributes are sparsely related.
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