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• W2747732253 abstract "Traffic analysis road networks are extensively used in transportation planning and modelling practice. Due to computational complexity and burden, a traffic analysis road network is a subset network which usually selected from a full-size network. However, the process of subjectively choosing traffic analysis road network is problematic and may result in an unrepresentative road network which is useless for transportation analysis applications. This research targets on proposing a road network abstraction method that can scientifically and systematically select a representative road network from original full-size network to achieve both representativeness and computation efficiency in various transportation and traffic analysis applications. The road networks on dynamic traffic assignment and simulation model are the interests. At the same time, traffic analysis performance metrics, such as average travel time, vehicle routing choices, and volume, are chosen as the criteria to determine the abstracted network representativeness. A numeric experiment is conducted by implementing the method in a demonstrated Alexandria network scenario. The results indicate that the proposed method is very promising. The traffic analysis performance of the abstracted network is similar to the performance of the full-size network. However, the computational time of the abstracted network is significantly lower than that of the full-size road network." @default.
• W2747732253 created "2017-08-31" @default.
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• W2747732253 date "2017-09-01" @default.
• W2747732253 modified "2023-09-27" @default.
• W2747732253 title "Road network abstraction approach for traffic analysis: framework and numerical analysis" @default.
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• W2747732253 doi "https://doi.org/10.1049/iet-its.2016.0287" @default.
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