Ning Ding

Improving the efficiency of stair evacuation plays a crucial role in emergency management, which may be shaped by pedestrians’ lane-selection behavior. However, most existing studies describe pedestrians’ lane-selection preferences during stair evacuation, while the mechanisms behind these preferences are not yet well understood. To solve this issue, a stair evacuation observation experiment and a questionnaire survey were carried out to investigate pedestrian stair lane-selection preferences. Based on 1793 pieces of experimental data and 397 questionnaires, it is found that (1) pedestrians in the middle lane are more inclined to proactively change lanes based on their personal preference when sufficient space is available. (2) The primary factors influencing pedestrians’ lane-selection preferences are perceived safety, shortest path, and behavioral habit. (3) As the distance to the wall increases, the preference for the wall-side lane gradually decreases. Notably, the rate of decline accelerates at first, then slows down as the wall becomes farther away. This study deeply deconstructs pedestrians’ stair lane-selection preferences which helps understand the interactions among pedestrians, between pedestrians and their surroundings. It offers a basis for the optimization of evacuation strategies, the design of emergency evacuation plans, and the calibration of evacuation simulation models.

In recent years, attacks against crowded places such as campuses and theaters have frequently and negatively impacted social security and stability. In such an event, the crowd will be subjected to higher psychological stress, and their emotion will spread to others rapidly. This paper establishes the Attack-Evacuation Evacuation Simulation Model (AEES-SFM) based on the social force model to consider emotion spreading under attack. In this model, (1)Attack-Escape Derive Force was considered for the interaction between attackers and evacuees and (2) emotion spreading among the evacuees was considered to modify the value of psychological forces. To validate the simulation, several experiments were carried out in a university in China. Comparing the simulation and experimental results, it is found that the simulation results are similar to the experimental results when considering emotion spreading. Therefore, the AEES-SFM proved to be effective. By comparing the results of the evacuation simulation without emotion spread, the emotion spread model reduces the evacuation time and the number of casualties by about 30%, which is closer to the real experimental results. And the results are still applicable in the case of a 40-person evacuation. This paper provides theoretical support and practical guidance for campus response to violent attacks.

Terrorist attacks are a catastrophic man-made emergent phenomenon. The current situation of international violent terrorism is severe. Public places with high-density crowds are more likely to be attacked by violent terrorists. To effectively reduce the number of casualties caused by violent terrorist incidents, it is crucial to take preventive management at the beginning of the incident. This paper analyzed the characteristics of the self-organization of crowds and terrorists in public places during a suicide bombing attack and conducted simulation experiments with an agent-based model. The simulation results show that unobstructed escape routes and controlling external sites will improve the evacuation efficiency of the crowd when a violent terrorist incident occurs; the pedestrian flow in public places affects the overall evacuation time; and timely and effective control of violent terrorists can reduce the number of casualties among the crowd. The optimized strategies obtained through experiments will help public security organs prevent violent terrorist incidents in public places and improve the efficiency of early disposal.

Can eyes tell the truth? Can the analysis of human eye-movement data reveal psychological activities and uncover hidden information? Lying is a prevalent phenomenon in human society, but research has shown that people’s accuracy in identifying deceptive behavior is not significantly higher than chance-level probability. In this paper, simulated crime experiments were carried out to extract the eye-movement features of 83 participants while viewing crime-related pictures using an eye tracker, and the importance of eye-movement features through interpretable machine learning was analyzed. In the experiment, the participants were independently selected into three groups: innocent group, informed group, and crime group. In the test, the eye tracker was used to extract a total of five categories of eye-movement indexes within the area of interest (AOI), including the fixation time, fixation count, pupil diameter, saccade frequency, and blink frequency, and the differences in these indexes were analyzed. Building upon interpretable learning algorithms, further investigation was conducted to assess the contribution of these metrics. As a result, the RF-RFE suspect identification model was constructed, achieving a maximum accuracy rate of 91.7%. The experimental results further support the feasibility of utilizing eye-movement features to reveal inner psychological activities.

Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes.