Urban Road Network Emergency: An Integrative Vulnerability Identification Method

Abstract

The vulnerability of an urban road network is affected by many factors, such as internal road network layout, network structure strength, and external destructive events, which have great uncertainty and complexity. Thus, there is still no unified and definite vulnerability analysis scheme available to cities. This paper proposes an integrative vulnerability identification method for urban road networks, which mainly relates to the vulnerability connotation and characteristics analysis of urban road networks during emergency, and vulnerability comprehensive evaluation indices design based on urban road network connectivity, traffic efficiency and performance, and an empirical study on a vulnerability identification method of an urban road network. In the empirical case, a real road network and traffic operation data were used from Science and Technology Park of Shenzhen City, China. In the context of one certain emergency scenario, the stated preference survey method and maximum likelihood method are used to solve the road users’ random travel choice behavior parameters; subsequently, based on the traffic equilibrium distribution prediction, the traffic vulnerability identification methods of the road network in this region were verified before and after the emergency. The method presented here not only considers the impact of network topology changes on road network traffic function during emergency but also considers the impact of dynamic changes in road network traffic demand on vulnerability; therefore, it is closer to the actual distribution of urban road network traffic vulnerability.

Analysis of cut vertex in the control of complex networks

“…[22] Because the final goal of studying complex networks is to control and regulate them, many works have explained the inner relationship between structural characteristics and network control. [23][24][25][26][27][28][29] Furthermore, for practical problems in real control, control strategies, including energy requiring, [30][31][32][33][34] control modes, [35] and optimal control, [36][37][38] have also been considered. Reference [37] demonstrated that the control energy is involved with Gramian matrix and proposed optimal algorithm for driver nodes selection.…”