Transportation system has time-varying, coupling and nonlinear dynamic characteristics. Traffic flow forecast
is one of the key technologies of traffic guidance. It is very difficult to accurately forecast them effectively. This paper has
analyzed the complexity and the evaluation index of urban transportation network and has proposed the forecasting model
of the hybrid algorithm based on chaos immune knowledge. First of all, the chaos knowledge is introduced into the topology
structure of immune network, so as to obtain the matching predictive values and knowledge base. Secondly, this algorithm
can dynamically control and adjusted the regional search speed and can fuse the information obtained by the chaos
and immune algorithm, in order to realize the short-term traffic flow forecast. Finally, the simulation experiment shows
that the traffic flow forecasting error obtained by the method is small, feasible and effective and can better meet the needs
of the traffic guidance system.
Traffic Flow Forecast of Road Networks With Recurrent Neural Networks
