Urban road network vulnerability and resilience to large-scale attacks

2022 ◽  
Vol 147 ◽  
pp. 105575
Skanda Vivek ◽  
Hannah Conner
2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
De-Xin Yu ◽  
Zhao-Sheng Yang ◽  
Yao Yu ◽  
Xiu-Rong Jiang

Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

2017 ◽  
Vol 11 (7) ◽  
pp. 391-401 ◽  
Joshua Stipancic ◽  
Luis Miranda-Moreno ◽  
Aurélie Labbe ◽  
Nicolas Saunier

2021 ◽  
Jiawei Zhang ◽  
Maosi Geng ◽  
Jiangsa Gu ◽  
Xiqun (Michael) Chen

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