To reveal the interaction and influence mechanism between emergency rescue entities, and to explore and optimize a cooperation mechanism of emergency rescue entities, a tripartite evolutionary game model of emergency rescue cooperation based on government rescue teams, social emergency organizations, and government support institutions was constructed. The stability of each game subject’s strategy choice was explored. Simulation analysis was applied to investigate the influence mechanism of key parameters on the evolution of the game subject’s strategy combination. The research results show that government rescue teams, social emergency organizations, and government support institutions have consistent political demands and rescue targets in emergency rescue cooperation. The game subjects are driving forces for each other to choose positive strategies. The game evolution process of the emergency cooperation model shows a “mobilization-coordination” feature. At the same time, the emergency capital stock formed based on trust relationships, information matching, and institutional norms between game subjects can promote the evolution of the game system toward (1,1,1). In addition, for government organizations with limited emergency resources, the average allocation of emergency resources is not the optimal solution for emergency rescue efficiency. However, it is easier to achieve the overall target of emergency rescue cooperation by investing limited emergency resources in key variables that match the on-site situation. On this basis, combined with the practice of emergency rescues in emergencies, countermeasures and solutions are proposed to optimize the mechanism and improve the efficiency of emergency rescue cooperation.

Natural disasters have obvious cross-regional and compound characteristics. Cross-regional emergency cooperation for natural disasters deepens the diversification of coordination relations and the complexity of interaction modes among emergency response organizations, including horizontal and vertical organizational interactions. In order to clarify the cooperation mechanism of emergency organizations during cross-regional emergency cooperation for natural disasters and to explore the key factors that affect the cooperative relationships of emergency organizations, in this study, a game model is constructed based on evolutionary game theory, which is composed of local, neighboring, and central governments. Then, the stability of the emergency game strategy is analyzed. On this basis, a numerical simulation is used to simulate the dynamic evolution trajectory of the game system. The results show that there is an embedded mutual promotion mechanism that evolves towards a positive emergency strategy combination among the game subjects. The selection strategies of the game subjects show the characteristics of consistency and the following: enhanced cooperation efficiency between local and neighboring governments, emergency capital stock, and shared resources, therefore, guiding social emergency forces to actively participate in emergency operations. Strengthening the emergency dispatching strength of the central government and the effectiveness of central–local emergency dispatching, can support the performance of cross-regional emergency cooperation for natural disasters. Furthermore, the efficiency of cooperation between local and neighboring governments will be enhanced.

Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating strategies. Most existing methods for analyzing urban congestion patterns are based on traditional traffic detector data, which is inflexible and expensive. Additionally, prior research primarily focused on the microscopic model, which simulated congestion propagation based on theoretical models and hypothetical networks. As such, most previous models and methods are difficult to apply to real case scenarios. Therefore, we investigated recurrent congestion patterns by mining historical taxi trajectory data that were collected in Harbin, China. A three-step method is proposed to reveal urban recurrent congestion evolution patterns. Firstly, a grid-based congestion detection method is presented by calculating the change in taxi global positioning system (GPS) trajectory patterns. Secondly, a customized cluster algorithm is applied to measure the recurrent congestion area. Finally, a series of indicators are proposed to reflect RC evolution patterns. A case study was competed in the Harbin urban area to evaluate the main methods. Finally, RC cause analysis and alleviating strategy are discussed. The results study are expected to provide a better understanding of urban RC evolution patterns.

Compound disasters are highly complex and can involve different types of disasters. Since the beginning of the COVID-19 pandemic, compound disasters of public health emergencies, accident disasters, and natural hazards have occurred frequently all over the world; therefore, it is important to establish effective compound disaster emergency collaboration networks. Thus, this study examined the 7 March building collapse in Quanzhou City as a case study. This case was a typical compound disaster involving a public health emergency and an accident disaster during COVID-19. Based on the network analysis, the overall response and dynamic characteristics of the emergency collaboration for compound disasters were examined in this study. A compound disaster emergency collaboration network (ECN) was constructed by identifying the interactional relationships between emergency organizations. After applying time slices, the dynamic evolution of network structure, organizational–functional relations, organizational attributes, and cross-organizational relationships were discussed. The research results showed the following: (1) The density and connectivity of the compound disaster ECN first decreased before increasing. Meanwhile, the evolution of the network structure followed a path from decentralized to concentrated and from being uneven to an equilibrium. (2) The characteristics and practices of compound disasters during different periods indicated varied emergency needs for emergency organizations. We found that the formation of emergency tasks not only involved the passive adaptation to match the practice for compound disasters, but also the active choices of emergency organizations when facing compound disasters according to their collective experiences and decisions. (3) The national emergency management departments, the government emergency rescue organizations, and the local governments were the core organizations of the ECN. Public health management departments and social organizations were also required to participate in the ECN to improve the diverse and heterogeneous distribution of resources. (4) With increased demands during a compound disaster emergency, the number of cross-organizational collaborative relationships gradually increased. This study explored compound disaster emergencies from the perspective of network analysis to improve our understanding of the current and developing organizational relationships and practices during a compound disaster event. The dynamic characteristics of compound disasters require efficient adaptation and improvements of the collaborative mechanisms involved during emergencies.

The aim of this paper is to compare Random Regret Minimization (RRM) and Random Utility Maximization (RUM) paradigms in emergency evacuation decision application. Three models including utility model, regret model, and hybrid utility-regret model are formulated, respectively. A Stated Preference (SP) survey on evacuation route choice under emergency was conducted to obtain the evacuees’ trade-off among average travel time, travel time uncertainty, perceived road damage probability, and perceived service level. The estimation reveals that regret-based model performs better than utility-based model. The differences between RRM and RUM in emergency context are substantial in model fit when compared with previous empirical results under non-emergency state. Specifically, the model also explains the influence of determining factors related to evacuees’ socio-demographics on their route decisions. Further, considering the road damage probability as a predominant factor, Value of Damage (VOD) is proposed to reveal the unobserved regret aversion psychology. Direct elasticity and cross elasticity are calculated for the hybrid utility-regret model. Regret increases in the emergency scenario, supporting that the advantage of RRM paradigm to explain evacuation route choices. The study is expected to give a better understanding of evacuation route choice behavior for emergency actions.

Facing the potential dangers from sudden disasters in urban cities, emergency administrators have to make an appropriate evacuation plan to mitigate negative consequences. However, little attention has been paid to evacuee real decision psychology when developing a strategy. The aim of this paper is to analyze evacuee mode choice behavior considering regret aversion psychology during evacuation. First, the utility-based and regret-based models are formulated to obtain evacuees’ preferences on travel mode choice, respectively. According to the data collected from the stated preference (SP) survey on evacuee mode choice, the estimation results show that the regret-based model performs better than the utility model. Moreover, based on the estimates from behavioral analysis, the elasticities of evacuee mode choices are calculated, and transit strategy simulation is undertaken to investigate the influence on evacuee mode switching from private automobile to public transit. The results are expected to help emergency administrators to make a transit-oriented strategy for a sustainable evacuation plan, especially for the benefit of carless people.