期刊
APPLIED MATHEMATICAL MODELLING
卷 87, 期 -, 页码 546-570出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2020.06.020
关键词
Gray inertial prediction model; CFIGM; Following model; Short-term traffic flow
资金
- National Natural Science Foundation of China [71871174]
- Project of Humanities and Social Sciences Planning Fund of Ministry of Education of China [18YJA630022]
Real-time and accurate short-term traffic flow prediction results can provide real-time and effective information for traffic information systems. Based on classic car-following models, this paper establishes differential equations according to the traffic state and proposes a car-following inertial gray model based on the information difference of the differential and gray system, in combination with the mechanical characteristics of traffic flow data and the characteristics of an inertial model. Furthermore, analytical methods are used to study the parameter estimation and model solution of the new model, and the important properties, such as the original data, inertia coefficient and simulation accuracy, are studied. The effectiveness of the model is verified in two cases. The performance of the model is better than that of six other prediction models, and the structural design of the new model is more reasonable than that of the existing gray models. Moreover, the new model is applied to short-term traffic flow prediction for three urban roads. The results show that the simulation and prediction effects of the model are better than those of other gray models. In terms of the traffic flow state, an optimal match between short-term traffic flow prediction and the new model is achieved. (C) 2020 Elsevier Inc. All rights reserved.
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