Open AccessDynamic traffic demand uncertainty prediction using radio‐frequency identification data and link volume data
Author(s) -
Liu Yu,
Liu Zhao,
Li Xiugang,
Huang Wei,
Wei Yun,
Cao Jinde,
Guo Jianhua
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5317
Subject(s) - computer science , kalman filter , radio frequency identification , volume (thermodynamics) , traffic generation model , identification (biology) , variance (accounting) , real time computing , data mining , physics , botany , computer security , quantum mechanics , artificial intelligence , biology , accounting , business
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.