A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Computational Modeling of Networked Infrastructures: Macroscopic Multivariate Approach
Infrastructure planners must perform many time-consuming simulations to prescreen numerous design alternatives. For example, transportation engineers must assess several alternatives to alleviate road transportation network congestion, such as constructing roads, changing organizations’ operating times, and changing shopping center locations. Evaluating alternatives requires considering changes in both structural and dynamic attributes of a network. A framework is proposed based on network science, computational science, and multivariate statistics to help planners train a model that can evaluate various alternatives efficiently. This model saves significant time and computational resources. The trained model takes structural and dynamic attributes as inputs and promptly returns macroscopic performance measures such as the averages and standard deviations of speed and volume over capacity. The model was trained using the Greater Philadelphia road network. Its average prediction error is 6%. Considering the time-saving advantages of the proposed model over traffic simulation models (e.g., fewer than 10 s are required compared with 2 days of traffic simulation using VISUM software), the model is a useful tool for the macroscopic assessment of many design alternatives.
Computational Modeling of Networked Infrastructures: Macroscopic Multivariate Approach
Infrastructure planners must perform many time-consuming simulations to prescreen numerous design alternatives. For example, transportation engineers must assess several alternatives to alleviate road transportation network congestion, such as constructing roads, changing organizations’ operating times, and changing shopping center locations. Evaluating alternatives requires considering changes in both structural and dynamic attributes of a network. A framework is proposed based on network science, computational science, and multivariate statistics to help planners train a model that can evaluate various alternatives efficiently. This model saves significant time and computational resources. The trained model takes structural and dynamic attributes as inputs and promptly returns macroscopic performance measures such as the averages and standard deviations of speed and volume over capacity. The model was trained using the Greater Philadelphia road network. Its average prediction error is 6%. Considering the time-saving advantages of the proposed model over traffic simulation models (e.g., fewer than 10 s are required compared with 2 days of traffic simulation using VISUM software), the model is a useful tool for the macroscopic assessment of many design alternatives.
Computational Modeling of Networked Infrastructures: Macroscopic Multivariate Approach
Nourzad, Seyed Hossein Hosseini (author) / Pradhan, Anu (author)
2016-04-19
Article (Journal)
Electronic Resource
Unknown
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