Please use this identifier to cite or link to this item:
Full metadata record
|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.subject||Arts and Humanities||en_US|
|dc.title||A Conceptual Framework for Bus Arrival Prediction Based on Spark Framework and Machine Learning Approaches||en_US|
|article.title.sourcetitle||2020 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 2020||en_US|
|article.stream.affiliations||Chiang Mai University||en_US|
|Appears in Collections:||CMUL: Journal Articles|
Files in This Item:
There are no files associated with this item.
Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.