Abstract Estimating travel speed is one of the most important steps to build an Intelligent Transportation System (ITS). In this paper, we propose a Travel Speed Estimation Model (TSEM) for Yangon City’s Road Network by analyzing GPS data from buses using Machine Learning Techniques. The GPS data are collected from buses that pass through the most congested area of Yangon City. The paper designs the Travel Speed Estimation model in four steps. The first step is GPS data collection and Preprocessing by removing outlier points, reducing features by dimension reduction methods and selecting important features of raw GPS data to get a well-structured data set. The second step is road network analysis and map matching that extracts POI features vector from nearby road side places and segments the bus route by bus stop positions along the bus route by using KNN model. The next step is estimating travel speed of every road segment from the matched trajectory points. The final step is to calculate speed factors for road all segments and to store in a matrix that can be used in different urban transport applications.
Building Travel Speed Estimation Model for Yangon City from Public Transport Trajectory Data
2018-06-07
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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