Abstract
Traffic congestion is a major concern in Macau. To alleviate the situation, this study aims to develop an effective and reliable real-time road traffic prediction system for Macau. In most existing studies, traffic prediction systems are developed based on moving-average models and using only historical traffic data. Considering that new arriving data usually contain the most updated and useful traffic information, this study proposes to construct the prediction model using a novel machine learning algorithm, namely extreme learning machine, which is capable of learning the data behavior in an extremely fast and online manner. To collect real-time traffic data, floating car data from public transportation are employed as the data source in this study. By performing online learning and real-time traffic prediction simultaneously, the proposed system is able to provide reliable real-time forecasting traffic information, even in the presence of undesired traffic changes. To evaluate the performance of the proposed system, a case study on the Macau Grand Prix event is conducted. During this event, many road sections are closed and more than half of the bus routes need to be diverted. The evaluation results show that the proposed system is effective for predicting future traffic conditions under the complicated traffic situation and different time frames. Based on the forecasting information, the traffic authorities will be able to make corresponding traffic management measures and provide optimal route guidance for the citizens.
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Chiang, Nv., Tam, Lm., Lai, Kh., Wong, Ki., Si Tou, Wm. (2019). Floating Car Data-Based Real-Time Road Traffic Prediction System and Its Application in Macau Grand Prix Event. In: Mine, T., Fukuda, A., Ishida, S. (eds) Intelligent Transport Systems for Everyone’s Mobility. Springer, Singapore. https://doi.org/10.1007/978-981-13-7434-0_21
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DOI: https://doi.org/10.1007/978-981-13-7434-0_21
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