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Evaluation of Clustering Algorithms for the Prediction of Trends in Bus Travel Time
Published in SAGE Publications Ltd
2018
Volume: 2672
   
Issue: 45
Pages: 242 - 252
Abstract
Providing accurate and reliable travel time information to travellers is essential to improve the quality of public transit systems. With the availability of the latest technologies, it has become possible to collect a large amount of traffic data to analyze and understand these systems better. Traffic in India is characterized by lack of lane discipline and the presence of vehicles of varying static and dynamic characteristics, which makes prediction of bus travel time especially challenging. The aim of this study is to identify both a prediction algorithm that can handle high variability and suitable inputs or regressors to be used. Earlier studies performed offline manual grouping considering the patterns observed, which leads to limitations for automated field implementations. The present study explores the use of data-driven approaches, primarily clustering, to address the challenges for the prediction of bus travel time trends. Discrete wavelet transform (DWT) was used to extract trends from the travel time measurements. Three popular clustering algorithms—k-means, hierarchical, and self organizing maps (SOM)—were used to identify patterns. Travel time trends were then predicted by searching for similar cluster patterns within the historical database using pattern sequence-based forecasting (PSF). A comparison of the performance of these algorithms was carried out based on prediction errors. The clustering +prediction framework developed was also compared with the case when no clustering was done on the regressor dataset. © National Academy of Sciences: Transportation Research Board 2018.
About the journal
JournalData powered by TypesetTransportation Research Record
PublisherData powered by TypesetSAGE Publications Ltd
ISSN03611981
Open AccessNo
Concepts (17)
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    Bus transportation
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    Buses
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    Conformal mapping
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    Discrete wavelet transforms
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    Forecasting
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    Mass transportation
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    Self organizing maps
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    Travel time
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    Data-driven approach
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    FIELD IMPLEMENTATION
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    Historical database
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    PATTERN SEQUENCES
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    Prediction algorithms
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    PUBLIC TRANSIT SYSTEMS
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    STATIC AND DYNAMIC CHARACTERISTICS
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    TRAVEL TIME INFORMATION
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    Clustering algorithms