NDLI: Dynamic traffic demand uncertainty prediction using radio-frequency identification data and link volume data

Content Provider	IET Digital Library
Author	Liu, Yu Liu, Zhao Li, Xiugang Huang, Wei Wei, Yun Cao, Jinde Guo, Jianhua
Abstract	Dynamic traffic demand is crucial for developing effective strategies and algorithms for real-time traffic management and control. The uncertainty of traffic demand provides additional information while its prediction is very complicated and is inadequately investigated in the existing literature. Recently, radio-frequency identification (RFID) technology has been deployed to monitor the condition of traffic. In this study, the authors propose a modelling system to predict dynamic traffic demand uncertainty using the RFID data and the link traffic volume. The modelling system includes an optimisation model using the RFID data to calculate the historical traffic demand in terms of origin–destination matrix, a state-space model solved by Kalman filter to predict the traffic demand, and a GARCH model to capture the conditional variance of the predicted traffic demand. Performance measures are applied to evaluate prediction accuracy. The case study in Nanjing, China shows that the modelling system has the desirable performance to successfully predict uncertainties of dynamic traffic demand.
Starting Page	1309
Ending Page	1317
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 8, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5317
Journal	IET Intelligent Transport Systems
Publisher Date	2019-04-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algorithm Control Dynamic Traffic Demand Uncertainty Prediction Effective Strategy Historical Traffic Demand Kalman Filter Link Traffic Volume Link Volume Data Matrix Algebra Modelling System Optimisation Optimisation Technique Predicted Traffic Demand Prediction Accuracy Radio-frequency Identification Data Radio-frequency Identification Technology Radiofrequency Identification Real-time Traffic Management RFID Data RFID System Road Traffic State-Space Model Statistics System Theory Application in Transportation Telecommunication Traffic
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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