Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/185626
Title: Optimizing Traffic Signal Settings in Smart Cities
Authors: Zhiyi Li;Mohammad Shahidehpour;Shay Bahramirad;Amin Khodaei
Year: 2017
Publisher: IEEE
Abstract: Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers' average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation-based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems, which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration of solution process in large-area traffic network applications.
URI: http://localhost/handle/Hannan/185626
volume: 8
issue: 5
More Information: 2382,
2393
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7438878.pdf406.65 kBAdobe PDF
Title: Optimizing Traffic Signal Settings in Smart Cities
Authors: Zhiyi Li;Mohammad Shahidehpour;Shay Bahramirad;Amin Khodaei
Year: 2017
Publisher: IEEE
Abstract: Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers' average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation-based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems, which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration of solution process in large-area traffic network applications.
URI: http://localhost/handle/Hannan/185626
volume: 8
issue: 5
More Information: 2382,
2393
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7438878.pdf406.65 kBAdobe PDF
Title: Optimizing Traffic Signal Settings in Smart Cities
Authors: Zhiyi Li;Mohammad Shahidehpour;Shay Bahramirad;Amin Khodaei
Year: 2017
Publisher: IEEE
Abstract: Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers' average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation-based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems, which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration of solution process in large-area traffic network applications.
URI: http://localhost/handle/Hannan/185626
volume: 8
issue: 5
More Information: 2382,
2393
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7438878.pdf406.65 kBAdobe PDF