Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.

Satellite-derived nighttime light data have been increasingly used for studying urbanization and socioeconomic dynamics, because there are notable quantitative relationships between anthropogenic nocturnal radiance and the degree of human activity over time and space at different scales. When considering the visible impacts of saturation and over-glow effects from original nighttime light images, several composite indexes, which mainly include the introduction of vegetation index, have been studied to improve the application of nighttime light data for investigating the spatial patterns in human settlements. To overcome the shortcomings of previous composite indexes, especially in areas of highly intensified human activity, such as urban, non-man-made surfaces, and low density human activity, such as in rural residential sites, we propose a new human settlement composite index (HSCI). The establishment of this proposed HSCI is based on a combination of three different remote sensing datasets: nighttime light brightness (derived from the Defense Meteorological Satellite Program, DMSP), the normalized difference vegetation index (NDVI, derived from the Moderate Resolution Imaging Spectroradiometer, MODIS), and the percent impervious surface area (PISA, derived from the GlobeLand30 land cover and land use dataset produced from Landsat data). We defined the calculation of HSCI as the arithmetic mean of the normalized difference urban index and normalized difference imperviousness index with respect to both the magnitude of socioeconomic activity and the distribution of artificial surface across human settlement, respectively. Analysis results clearly demonstrate the utility of HSCI in delineating spatial patterns for different kinds of human settlement, particularly for identifying non-man-made surfaces in urbanized areas, various densities of human activities in peripheral areas and small human settlements in rural and remote areas. Our method and findings provide an effective way to investigate human settlements with a nighttime brightness-based composite index, as well as valuable insights into further studies of the composite index related to nocturnal luminosity data.

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method’s ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents’ navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions.

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively.

The Health Data Space (HDS) is a promising platform for the secure health data sharing among entities including patients and healthcare providers. However, health data is highly sensitive and critical for diagnosis, and unauthorized access or destruction by malicious users can lead to serious privacy leaks or medical negligence. Thus, robust access control, privacy preservation, and data integrity are essential for HDS. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) supports secure sharing, it has limitations when directly applied to HDS. Many current schemes cannot simultaneously handle data integrity violations, trace and revoke malicious users, and protect against privacy leaks from plaintext access policies, with key escrow being another major risk. To overcome these issues, we put forward a Traceable and Revocable Privacy-Preserving Data Sharing (TRPPDS) scheme. Our solution uses a novel distributed CP-ABE with a large universe alongside data auditing to provide fine-grained, key-escrow-resistant access control over unbounded attributes and guarantee data integrity. It also features tracing-then-revocation and full policy hiding to thwart malicious users and protect policy privacy. Formal security analysis is presented for our proposal, with thorough performance assessment also demonstrates its feasibility in HDS.