NDLI: Neural networks based real-time transit passenger volume prediction

Content Provider	IEEE Xplore Digital Library
Author	Mo Yikui Su Yongyun
Copyright Year	2009
Description	Author affiliation: Design department, Shenzhen Urban Transport Planning Center, Hongli west Road 8009, China (Su Yongyun) \|\| College of Civil Engineering, Shenzhen University, Nanhai Road 3688, China (Mo Yikui)
Abstract	Real-time transit passenger volume prediction is the basis of establishing an intelligent public transportation dispatching system, and of great significance to improving the level of service of public transportation systems. In light of the characteristics of transit passenger flow, this paper suggests a solution for real-time transit passenger volume prediction based on neural network, with the input variables being the passenger flow volume, forecasting date, time and weather, and the output variable being the forecasting value of real-time passenger flow. In addition to presenting the structure and calculation method of the neural network model, this paper analyzes ways in collecting and managing related data information, examines the merits and weaknesses of such model and finally points out the orientation of the research efforts.
Starting Page	303
Ending Page	306
File Size	1309962
Page Count	4
File Format	PDF
ISBN	9781424445448
DOI	10.1109/PEITS.2009.5406782
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	algorithms Weather forecasting Demand forecasting forecasting Artificial neural networks neural networks Predictive models Intelligent transportation systems Road transportation Intelligent networks model structures Neural networks Economic forecasting Dispatching bus transportation
Content Type	Text
Resource Type	Article

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