The transportation networks involve complex operations that are not easy to capture with simple mathematical equations. They are highly unbalanced both in space and time leading to low and high peak of traffic. The lack of adequate traffic measurements where needed contributes to the problem. Lack of coordination among traffic generators and users adds to the difficulty.
Balancing the loads across the transportation networks aims to capture hidden capacities, provide better control actions and optimize decisions by taking advantage of emerging and maturing sensor technologies as well as the availability of fast computations and software tools. We will talk about how to use simulation models for state prediction in a feedback control/optimization loop in order to achieve load balancing. We present a vision how personalization can be integrated in load balancing in order to develop a more coordinated system that would provide more effective network control and better services to the users. A multimodal freight routing example will be used to demonstrate the approach. An example of how personalization can be integrated in traffic management and control systems will also be presented.
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