Vehicle electrification, an important method for reducing carbon emissions from road transport, has been promoted globally. In this study, we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure. Considering the varying travel costs associated with electric and fuel vehicles, we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city. A spatial equilibrium is developed to model the interactions between urban density, vehicle age and vehicle type. An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types, considering vehicle ages and competition for public charging piles. Key findings from our proposed models show that the proportion of electric vehicles (EVs) peaks at over 50% by the end of the first scrappage period, accompanied by more than a 40% increase in commuting distance and time compared to the scenario with only fuel vehicles. Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion. Furthermore, households with EVs tend to be located farther from the city center, and an increase in EV ownership contributes to urban expansion. Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes. It offers a novel perspective on the dynamic interactions between EV adoption and urban development.

As the scale of urban rail transit (URT) networks expands, the study of URT resilience is essential for safe and efficient operations. This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research. First, URT resilience is defined by three primary abilities: absorption, resistance, and recovery, and four properties: robustness, vulnerability, rapidity, and redundancy. Then, the metrics and assessment approaches for URT resilience were summarized. The metrics are divided into three categories: topology-based, characteristic-based, and performance-based, and the assessment methods are divided into four categories: topological, simulation, optimization, and data-driven. Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods, such as conventional complex network analysis and operations optimization theory, with new techniques like big data and intelligent computing technology, to accurately assess URT resilience. Finally, five potential trends and directions for future research were identified: analyzing resilience based on multisource data, optimizing train diagram in multiple scenarios, accurate response to passenger demand through new technologies, coupling and optimizing passenger and traffic flows, and optimal line design.