The development of a real estate economy is beneficial to urban stability. A method of real estate price prediction based on transport accessibility is proposed. The method adds bus accessibility and metro accessibility into the model, which has higher prediction accuracy than the traditional model. Firstly, bus accessibility and metro accessibility are calculated according to the space syntax theory. Then, four models, the traditional hedonic price model (HPM) with transport accessibility, the traditional hedonic price model without transport accessibility, the random forest (RF) model with transport accessibility, and the random forest model without transport accessibility, are introduced. Finally, the four models are compared and analyzed in terms of precision and importance of index contributions. Taking Xi ’an, China, as an example, the experimental results show that the transport accessibility calculated based on space syntax can accurately represent the transport convenience in an urban space structure. Furthermore, it has a great influence on the contribution of indexes in the model. With the introduction of bus accessibility and metro accessibility, the accuracy of the real estate price prediction model is greatly improved.

Based on the symmetrical public transportation network data of Xi’an, China obtained by geographic information system (GIS) technology in 2019, three urban public transportation indexes of walking accessibility, bus accessibility, and metro accessibility were established, and a real estate price prediction model was built by using several machine learning algorithms to predict and analysis the housing price in Xi’an, China. Firstly, the symmetrical road network data and real estate property data of Xi’an were collected and preprocessed, secondly, the spatial syntax theory and distance calculation method were applied to establish three indexes of traffic accessibility; finally, taking the house property data and the calculated traffic accessibility indexes as the characteristic index, the real estate price prediction model of Xi’an was constructed by using the random forest algorithm (RF), lightweight gradient lift algorithm (LGBM), and gradient lifting regression tree algorithm (GBDT). The prediction accuracy of the final model is 89.2% and the root-mean-square error is 1761.84. The results show that the accessibility of bus and metro to some extent represent the convenience of public transportation in different areas of urban space. The higher the accessibility index is, the more convenient the traffic is. The real estate price model has high prediction accuracy and can reflect the real situation of urban real estate price. The importance of the three accessibility features to the real estate price prediction model are nearly more than 20%, which indicates that the accessibility of urban public transportation has an important impact on the change of urban real estate price, and the development of urban public transportation plays an important role in the real estate economy.

Unmanned aerial vehicles (UAVs) are rapidly being used for surveillance, environmental monitoring, disaster response, and intelligent transportation systems. However, protecting their connection remains a significant issue due to open wireless channels, severe energy limits, and the requirement for low-latency computing. This paper proposes a lightweight certificateless aggregate signcryption scheme exclusively designed for UAV networks. The proposed technique provides ciphertext aggregation, online/offline computation, and decreased communication cost, making it ideal for UAV situations with limited bandwidth and resources. To achieve strong security guarantees with a lower computation and communication cost, the approach uses hyper-elliptic curve cryptography, which offers much smaller key sizes than elliptic curve or pairing-based systems while maintaining equivalent security strength. The approach combines signcryption, key management, and ciphertext aggregation into a single framework that enables multi-UAV message compression before transmission to the ground station. The security of the proposed scheme is analyzed informally under the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HECDLP), providing heuristic arguments for confidentiality, unforgeability, authentication, integrity, and non-repudiation against both Type-I and Type-II adversaries. A thorough performance analysis, including computational cost, communicational overhead, and running time, shows that the proposed scheme outperforms previous systems. The experimental results indicate significant reductions in computational and communication overhead, making the proposed technique ideal for UAV-enabled edge intelligent IoT environments and confirming its effectiveness as a secure and efficient cryptographic foundation for next-generation UAV swarm communication.

Traffic information analysis plays an essential role in urban signalized intersection control. Wi-Fi technology can be used in multiple scenarios. It is effective to use a Wi-Fi data acquisition device to detect traffic information. This paper aims to study a traffic information detection method at a single intersection based on Wi-Fi data, determine the architecture design of the Wi-Fi data acquisition system, design the Wi-Fi data processing process, and then realize the acquisition of Wi-Fi data at a single intersection. K-means clustering algorithms and the LSTM neural network prediction model are used to obtain the space mean speed and vehicle steering ratio of the intersection sections. This method can be used to obtain and forecast various types of traffic information at intersections. The traffic information of an intersection in Xi’an is detected and predicted by using Wi-Fi technology. The experimental results show that the proposed method has high prediction accuracy.