Meta-heuristics for optimizing heterogeneous urban traffic network
With the development of modern cities and the gradual improvement of transportation facilities, the development of urban transportation networks and the skyrocketing rate of vehicle ownership have brought about increasingly serious traffic congestion problems, which have greatly increased the hidden...
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2021
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sg-ntu-dr.10356-1494082023-07-04T17:01:37Z Meta-heuristics for optimizing heterogeneous urban traffic network Xin, Jiaming Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems With the development of modern cities and the gradual improvement of transportation facilities, the development of urban transportation networks and the skyrocketing rate of vehicle ownership have brought about increasingly serious traffic congestion problems, which have greatly increased the hidden dangers of traffic accidents on busy road sections. To settle this problem, we use mathematical modeling of the two main objects contained in the urban transportation network: vehicles and pedestrians, including network input and output in the transportation system, vehicle turning rate, pedestrian violation rate, the mental state of the pedestrian and vehicle driver. After completing the mathematical modeling, we use a metaheuristic algorithm to settle the model, and finally find the optimal solution or approximate optimal solution that can synchronize the waiting time of vehicles and pedestrians through iteration. Finally, we will compare the model we presented and algorithm with the traditional traffic light control approach and DHS algorithm to verify the viability and validity of our algorithm and model. Master of Science (Computer Control and Automation) 2021-05-19T04:36:15Z 2021-05-19T04:36:15Z 2021 Thesis-Master by Coursework Xin, J. (2021). Meta-heuristics for optimizing heterogeneous urban traffic network. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149408 https://hdl.handle.net/10356/149408 en D-255-20211-02359 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Computer hardware, software and systems Xin, Jiaming Meta-heuristics for optimizing heterogeneous urban traffic network |
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With the development of modern cities and the gradual improvement of transportation facilities, the development of urban transportation networks and the skyrocketing rate of vehicle ownership have brought about increasingly serious traffic congestion problems, which have greatly increased the hidden dangers of traffic accidents on busy road sections. To settle this problem, we use mathematical modeling of the two main objects contained in the urban transportation network: vehicles and pedestrians, including network input and output in the transportation system, vehicle turning rate, pedestrian violation rate, the mental state of the pedestrian and vehicle driver. After completing the mathematical modeling, we use a metaheuristic algorithm to settle the model, and finally find the optimal solution or approximate optimal solution that can synchronize the waiting time of vehicles and pedestrians through iteration. Finally, we will compare the model we presented and algorithm with the traditional traffic light control approach and DHS algorithm to verify the viability and validity of our algorithm and model. |
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Su Rong |
author_facet |
Su Rong Xin, Jiaming |
format |
Thesis-Master by Coursework |
author |
Xin, Jiaming |
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Xin, Jiaming |
title |
Meta-heuristics for optimizing heterogeneous urban traffic network |
title_short |
Meta-heuristics for optimizing heterogeneous urban traffic network |
title_full |
Meta-heuristics for optimizing heterogeneous urban traffic network |
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Meta-heuristics for optimizing heterogeneous urban traffic network |
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Meta-heuristics for optimizing heterogeneous urban traffic network |
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meta-heuristics for optimizing heterogeneous urban traffic network |
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Nanyang Technological University |
publishDate |
2021 |
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https://hdl.handle.net/10356/149408 |
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1772825900467879936 |