With the acceleration of urbanization, the increasing frequency of building fires has caused a large number of deaths and economic losses. In order to delve into the evacuation route selection behaviors seen in library fires and analyze the impacts of individual evacuation psychologies on route decisions during escaping, based on practical survey data from the library on the Boda campus of Xinjiang University, this study built a mixed Logit (ML) model irrespective of latent psychological variables and a hybrid choice model (HCM) considering the latent variables of adaptive evacuation psychologies to investigate the internal formation mechanism of evacuees’ route decisions. The results indicate that evacuees’ non-adaptive conformity psychology, adaptive altruism psychology, and environmental familiarity have significant impacts on their route decisions. The stronger the evacuees’ non-adaptive inertia psychology, the more they lean towards the shortest route. Meanwhile, altruistic adaptive evacuation psychology has a significant negative impact on the probability of choosing the longest route. The stronger the evacuees’ environmental familiarity, the more they tend to choose the evacuation route with good emergency lighting. Personal socio-economic attributes have varying impacts on peoples’ evacuation route decisions. The findings of our study provide theoretical support for sustainable planning, preparedness, and the design of fire evacuations. This contribution aids in advancing sustainable practices for emergency responses.

As a critical component of urban public transport systems, taxi services have attracted increasing scholarly attention because their supply and demand are often spatially and temporally imbalanced. Understanding the spatiotemporal patterns and drivers of taxi supply-demand imbalance is important for improving residents' travel well-being. Using 2023 taxi origin-destination (OD) records from Urumqi and multi-source data, including points of interest (POIs), this study constructs a regional attractiveness model. With the supply-demand ratio as the core indicator, the study examines the spatiotemporal distribution of taxi supply-demand imbalance across workdays, weekends, and holidays. A multinomial logistic regression (MLR) model is then used to identify the factors associated with taxi supply-demand states. The results show that taxi supply-demand imbalance zones in Urumqi largely coincide with areas of high regional attractiveness and are closely associated with commercial centers, transport hubs, and densely populated residential zones. In 2023, daily changes in taxi supply-demand conditions were consistent with the tourism calendar in Xinjiang. At daily and weekly scales, the imbalance was mainly shaped by regional functions and residents' travel behavior. For example, taxi demand increased markedly in residential areas during the morning peak, whereas the supply-demand state around Urumqi Railway Station was closely related to train arrival and departure schedules. Public transport stops and related facilities were the dominant factors influencing taxi supply-demand imbalance during weekday commuting hours. During weekends and holidays, shopping and catering facilities became the major attractors of taxi demand, indicating that residents' daily activities substantially influence taxi demand dynamics. These findings deepen the understanding of the mechanisms linking urban taxi demand with multiple influencing factors and provide a reference for urban transport planning, taxi operation management, and residents' travel decisions.