A COMBINED SYSTEM OF STATIC TRAFFIC ASSIGNMENT AND ANN DELAY MODEL
SOLVING THE CONVERGENCE OF ITERATIVE OPTIMIZATION OF NON-MONOTONIC TRAFFIC ASSIGNMENT
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Transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes including themonotonicity of link travel time, which, in turn, leads to inability of planning models to arriveat a network solution based on travel costs that areconsistent with the intersection delays due to signalcontrols. Simultaneous optimization of trafficrouting and signal controls has not been accomplishedi...
Transportation planning models, which estimate
traffic volumes on transportation network links, are
often unable to realistically consider travel time
delays at intersections. Introducing signal
controls in models often result in significant and
unstable changes in network attributes including the
monotonicity of link travel time, which, in
turn, leads to inability of planning models to arrive
at a network solution based on travel costs that are
consistent with the intersection delays due to signal
controls. Simultaneous optimization of traffic
routing and signal controls has not been accomplished
in real-world applications of traffic assignment. A
delay
model dealing with five major types of intersections
has been developed using artificial neural networks
(ANN). The delay estimates by the ANN delay model
have satisfactory percentage root-mean-squared
errors (%RMSE). A combined system has also been
developed that includes the ANN delay model and a
user-equilibrium (UE) traffic assignment. The
combined system employs the Frank-Wolfe method to
achieve a convergent solution, although the global
optimum may not be guaranteed.
traffic volumes on transportation network links, are
often unable to realistically consider travel time
delays at intersections. Introducing signal
controls in models often result in significant and
unstable changes in network attributes including the
monotonicity of link travel time, which, in
turn, leads to inability of planning models to arrive
at a network solution based on travel costs that are
consistent with the intersection delays due to signal
controls. Simultaneous optimization of traffic
routing and signal controls has not been accomplished
in real-world applications of traffic assignment. A
delay
model dealing with five major types of intersections
has been developed using artificial neural networks
(ANN). The delay estimates by the ANN delay model
have satisfactory percentage root-mean-squared
errors (%RMSE). A combined system has also been
developed that includes the ANN delay model and a
user-equilibrium (UE) traffic assignment. The
combined system employs the Frank-Wolfe method to
achieve a convergent solution, although the global
optimum may not be guaranteed.
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