  Traffic congestion has lead to an increasing emphasis on management measures
for a more efficient utilization of existing infrastructure. In this context,
this paper proposes a novel framework that integrates real-time optimization of
control strategies (tolls, ramp metering rates, etc.) with guidance generation
using predicted network states for Dynamic Traffic Assignment systems. The
efficacy of the framework is demonstrated through a fixed demand dynamic toll
optimization problem which is formulated as a non-linear program to minimize
predicted network travel times. A scalable efficient genetic algorithm is
applied to solve this problem that exploits parallel computing. Experiments
using a closed-loop approach are conducted on a large scale road network in
Singapore to investigate the performance of the proposed methodology. The
results indicate significant improvements in network wide travel time of up to
9% with real-time computational performance.
