The thesis presents a generic methodology able to generate optimal controlled dynamic prescriptive route guidance to be disseminated by means of variable message signs (VMS). The methodology is generic in the sense it can be used on any network topology, with any number of VMS's, for different scenario's (e.g. recurrent congestion or accidents), based on a flexible user defined objective function and will work as long as feasible route alternatives exist. i The methodology uses a new and for this purpose developed macroscopic model called DSMART (dynamic, 1^^ order, macroscopic, single user class, probit route choice, split vectors) and a customized parallel implemented evolutionary algorithm (EA). By using simulation data from the DSMART model in combination with customized mutation operators in the EA, VMS settings are generated in a smart way to increase convergence speed. A prototype ofthe methodology has been developed in Matlab and applied in a case study to the city of Rotterdam in the Netherlands by using a network representation (approx. 500 links) of the motorway with six different VMS's and the connected urban network. The case study illustrated the generic nature by successfully optimizing all three different scenarios ranging from the: everyday recurrent morning congestion, a simulated accident and an extreme event (football final match) by generating a dynamic set of VMS settings for all six instances. As a measure for optimization, the objective function was utilized to express tlie generalized gross travei time \N[)\ch led to a system optimal assignment. To implement the methodology, a rolling horizon control framework is suggested including a crude scenario manager This suggested type of scenario management could be able to reduce the usually astronomical high number of scenarios needed in other systems, to a workable amount. Based upon expected efficiency increase when professionally implemented in the suggested rolling horizon implementation, it is expected that optimal controlled VMS settings could be calculated online for the case study network.
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