초록 ▼

A method and apparatus for model-free, real-time, system-wide signal timing for a complex road network is provided. It provides timings in response to instantaneous flow conditions while accounting for the inherent stochastic variations in traffic flow through the use of a simultaneous perturbation

A method and apparatus for model-free, real-time, system-wide signal timing for a complex road network is provided. It provides timings in response to instantaneous flow conditions while accounting for the inherent stochastic variations in traffic flow through the use of a simultaneous perturbation stochastic approximation (SPSA) algorithm. This is achieved by setting up several (M) parallel neural networks, each of which produces optimal controls (signal timings) for any time instant (within one of the M time periods) based on observed traffic conditions. The SPSA optimization technique is critical to the feasibility of the approach since it provides the values of weight parameters in each of the neural networks without the need for a model of the traffic flow dynamics.

대표청구항 ▼

A method for managing a complex transportation system, wherein a model governing the system dynamics and measurement process is unknown, to achieve optimal traffic flow by automatically adapting to both daily non-recurring events and to long-term changes in the system by approximating a controller f

A method for managing a complex transportation system, wherein a model governing the system dynamics and measurement process is unknown, to achieve optimal traffic flow by automatically adapting to both daily non-recurring events and to long-term changes in the system by approximating a controller for the system without having to first build the model therefor and without having, thereafter, to periodically and manually recalibrate the model, the method comprising the steps of: using a plurality of sensors to obtain traffic flow information about the system; inputting the traffic flow information into a data processing means; approximating the controller using the data processing means and the traffic flow information comprising the steps of: selecting a single function approximator to directly approximate the controller; estimating the unknown parameters of the single function approximator in the controller using a stochastic approximation algorithm that does not require the model for the system; and using the single function approximator to approximate the controller, wherein the controller is an output of the single function approximator; and using the controller to control traffic control means to achieve optimal traffic flow.