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Chemical accidents are major causes of environmental losses and have been debated due to the potential threat to human beings and environment. Compared with the single statistical analysis, co-word analysis of chemical accidents illustrates significant traits at various levels and presents data into a visual network. This study utilizes a co-word analysis of the keywords extracted from the Web crawling texts of environmental loss-related chemical accidents and uses the Pearson's correlation coefficient to examine the internal attributes. To visualize the keywords of the accidents, this study carries out a multidimensional scaling analysis applying PROXSCAL and centrality identification. The research results show that an enormous environmental cost is exacted, especially given the expected environmental loss-related chemical accidents with geographical features. Meanwhile, each event often brings more than one environmental impact. Large number of chemical substances are released in the form of solid, liquid, and gas, leading to serious results. Eight clusters that represent the traits of these accidents are formed, including "leakage," "poisoning," "explosion," "pipeline crack," "river pollution," "dust pollution," "emission," and "industrial effluent." "Explosion" and "gas" possess a strong correlation with "poisoning," located at the center of visualization map.

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The automatic flap barrier gate system (AFBGS) plays a critical role in building security, but it is more vulnerable to natural hazards than common exits (including power failure, due to earthquakes, and delayed evacuation, due to safety certification, etc.). This article considers a dynamic decision-making process of evacuees during post-earthquake evacuation near an AFBGS. An interesting metaphor, broken windows (BW), is utilized to interpret people’s actual behavior during evacuation. A multi-stage decision-making mechanism of evacuees is developed to characterize the instantaneous transition among three defined stages: Habitual, mild, and radical states. Then, we build a modified three-layer social force model to reproduce the interaction between evacuees based on an actual post-earthquake evacuation. The simulations reveal that BW provides a contextualized understanding of emergency evacuation with a similar effect to the traditional metaphor. An earlier appearance of a mild rule breaker leads to a higher crowd evacuation efficiency. If evacuees maintain the state of broken windows behavior (BWB), the crowd evacuation efficiency can be improved significantly. Contrary to the criminological interpretation, the overall effect of mild BWB is positive, but the radical BWB is encouraged under the command of guiders.

Due to the highly developed rail transit over the past decades, the phenomena of complex individual self-organized behaviors and mass crowd dynamics have become a great concern in the train station. In order to understand passengers’ walking-edge effect and analyze the relationship between the layout and sustainable service abilities of the train station, a heuristics-based social force model is proposed to elaborate the crowd dynamics. Several evacuation scenarios are implemented to describe the walking-edge effect in a train station with the evacuation efficiency, pedestrian flow, and crowd density map. The results show that decentralizing crowd flow can significantly increase the evacuation efficiency in different scenarios. When the exits are far away from the central axis of the railway station, the walking-edge effect has little influence on the evacuation efficiency. Obstacles can guide the movement of passengers by channelizing pedestrian flows. In addition, a wider side exit of the funnel-shaped corridors can promote walking-edge effect and decrease the pressure among a congested crowd. Besides providing a modified social force model with considering walking-edge effect, several suggestions are put forward for managers and architects of the train station in designing sustainable layouts.

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