Abstract
Road traffic flow forewarning and control model with the slope of the change rate
Highlights
Traffic jams in urban areas have attracted widespread attention in a global context, with the development of socio-economic and transport industry [1, 2]
The research on traffic flow prediction methods is mainly focused on the traditional methods based on statistical theory, the method based on neural network, the method based on nonlinear theory and the method based on combined algorithm [6]
Artificial neural network technology was born in the 1940s, and it was used for long-term traffic flow prediction by Chin in 1992, Dougherly and Clark, who Tehnički vjesnik 24, Suppl. 1(2017), 185-191 respectively used neural network technology for shortterm traffic flow forecasting in 1993 and 1994 [8]
Summary
Traffic jams in urban areas have attracted widespread attention in a global context, with the development of socio-economic and transport industry [1, 2]. Nonlinear prediction mainly in chaos theory [9, 10], dissipative structure theory, self-organization theory and other theories as theoretical foundation, uses the concept of chaotic attractor, fractal concept phase space reconstruction methods to build predictive models. This model includes many parameters, and is not easy to determine. Sun et al [16, 17] introduced a Bayesian method to forecast traffic flows where a certain period of historical data is missing for some links of the transportation network.
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