Incident Traffic Flow Models
In the trend toward civilization, transportation has always been considered as
an indisputable aspect. However, soon it turned out to be a dilemma in many
metropolitan areas how to assess the increased demand in transportation. The exigency for advanced alternative arises when constructing new roads and infrastructures lost its eligibility due to i.e. financial cost. It challenges scientists and traffic engineers then to elaborate more and more powerful intelligent transportation systems i.e. advanced road traffic management/supervision/control solutions.
The model-based analysis and synthesis of traffic system require mathematical abstractions of the real traffic in order to properly predict its behavior.
One of the most emerging direction is to create ITS solutions resilient
to off-nominal traffic conditions, i.e. to traffic incidents. To embed resilience
into road traffic control algorithms, proper modeling and reconstruction of traffic phenomenon are indispensable. Hence, the first part of this thesis focuses on proper description of incident modeling. Two different nominal traffic flow models namely Aw-Rascle and PW models are chosen. Within these modeling frameworks, incident parameters are properly introduced to describe the effect of traffic anomalies. To consolidate the idea, the microscopic interpretations of this parametrization has been presented. Simulation and real-measured traffic data based model validation is presented through joint state-parameter estimation scheme.
The second part of the thesis is devoted to synthesis of an appropriate control
strategy. We introduce scheduled robust optimization solution using ramp meter, which by encountering real-time incident parameter information, minimizes the effect of demand changes on predefined performance output.
macroscopic freeway traffic modeling