| Abstract [eng] |
The aim of this work was to analyze the data which was gotten from accelerometer mounted in mobile device during the test drives through the city together with GPS (Global Positioning System) coordinates, to detect and report the surface conditions of roads as well as to find the way, how it could be represented in the map. The research was started by analyzing oscillation data from accelerometer. We had to keep in mind that there can be road bumps, pit holes, speed bumps and other road anomalies, car can accelerate quickly and break sharply or even crash into something, what would cause a sudden stop. In order to recognize events, different detection filters were applied on data. In addition to this, neuron network was used to recognize pit holes and speed bumps from all event flow. The results of event detection algorithms were compared with other scientist’s works. In order to represent results clearly, database was created holding coordinates of the road events and other information like time, etc. The results were represented using an application programming interface made-up by Google, which was really suitable solution in our case. The whole system was programmed using Java servlets, which allowed to gather data from database using SQL (Structured Query Language) queries. While trying to represent accelerometer data, we faced difficulties in representing these road events on the map, as GPS each time returned answer with small variation of coordinates. In this case, we had to find a way, how to represent data clearly without overloading the map with information. After analyzing algorithms for objects clustering, the decision was made to use K-means algorithm for this objective as the most efficient and precise. In our research we proved that accelerometer could really help to monitor traffic alteration by evaluating our algorithms on data and show it can successfully detect these traffic anomalies as well as reduce suspicious detections with the help of clustering mechanism. |
