PROBE VEHICLE LANE IDENTIFICATION AT SIGNALIZED INTERSECTION USING SUPPORT VECTOR MACHINE METHOD
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Transportation problems in Indonesia, especially in urban areas, can be solved by applying Intelligent Transportation Systems (ITS), one of which is by using a Global Positioning System (GPS) based probe vehicle system. This system is growing because smartphones can be used as instruments for investigators' vehicles. This system provides information on the location and speed of all investigator vehicles every second, making it possible to estimate the real time traffic condition on a road segment passed by the probe vehicle. The implementation of this system in the city of Manado can help improve traffic conditions because so many motorists carry smartphones while driving. However, the problem is that the GPS on this smartphone has a low accuracy of 3 to 15 meters, so this GPS data cannot be used to solve transportation problems at the micro level such as at intersections. The purpose of this study was to obtain a method for identifying probe vehicle lanes through GPS data from smartphones. The steps in achieving the research goal are 1) building a microscopic transportation model using software to obtain data 2) building a data processing algorithm for vehicle lane identification using traffic flow theory and machine learning methods which in this case is Support Vector Machine, and 3) perform model validation. The results showed that the individual queue shock wave from each probe vehicle could be used to identify the lane of the probe vehicle. This study also confirms that the Support Vector Machine method can be used to predict the lane position of the probe vehicle.
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