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.
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Acknowledgments
This research was supported by Research Fund of Yangon Technological University, Yangon, Myanmar. We would like to extend our special thanks to all colleagues and friends who enthusiastically share their ideas and suggestions during research progress. Finally, we would like to thank YBS bus no. 21 for providing valuable data for our research.
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Kyaw, T., Oo, N.N., Zaw, W. (2019). Building Travel Speed Estimation Model for Yangon City from Public Transport Trajectory Data. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_28
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DOI: https://doi.org/10.1007/978-981-13-0869-7_28
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