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dc.contributor.authorLi Yeen_US
dc.contributor.authorPree Thiengburanathumen_US
dc.contributor.authorPoon Thiengburanathumen_US
dc.description.abstract© 2020 IEEE. Recently, the bus system has been deployed as a new option for public transportation in Chiang Mai. However, the reason why the people hesitate to take the bus because they are not confident about accuracy of the bus schedule. Predicting the bus arriving time at a certain bus station is a challenging problem, due to concerns with real-Time data pre-processing, numerous data inputs, and the level of predictive accuracy. Previous studies that related to bus time arrival prediction have applied statistical and machine-learning methods. However, the time series problem is rarely considered in the previous machine-learning prediction methods. Moreover, some models only analyze small amounts of data, which leads to poor accuracy and poor speed of prediction. This paper reviewed previous five years studies related to the prediction of bus arrival time. The paper highlights the current research gaps and applications of bus arrival time prediction. We proposed the research framework, as well as the possible research trends and challenges.en_US
dc.subjectArts and Humanitiesen_US
dc.subjectComputer Scienceen_US
dc.subjectSocial Sciencesen_US
dc.titleA Conceptual Framework for Bus Arrival Prediction Based on Spark Framework and Machine Learning Approachesen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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