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Predicting subway passenger flow and demand using big data and machine learning methods.

Authors: Xining Yang*, Eastern Michigan University, Yu Feng, University of Michigan, Hua Cai, Purdue University
Topics: Transportation Geography, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: human dynamic, big data, machine learning
Session Type: Paper
Presentation File: No File Uploaded

Understanding the quality and usage of public metro resources is important for schedule optimization in temporal dimension and route planning in spatial dimension. Reliable subway prediction is important for passengers, transit operators, and public agencies. This study proposes a novel machine learning methods to forecast the subway passenger flow and demand combined with the big transit fair transaction data. Geovisualization is conducted to effectively visualize the distribution of passenger flow over space and time. Empirical studies in Wuhan demonstrate that the propose prediction model can effectively forecast the human dynamic flow on utilizing the subway system under different external conditions.

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