The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm.

A personalized point-of-interest (POI) recommender system is of great significance to facilitate the daily life of users. However, it suffers from some challenges, such as trustworthiness and data sparsity problems. Existing models only consider the trust user influence and ignore the role of the trust location. Furthermore, they fail to refine the influence of context factors and fusion between the user preference and context models. To address the trustworthiness problem, we propose a novel bidirectional trust-enhanced collaborative filtering model, which investigates the trust filtering from the views of users and locations. To tackle the data sparsity problem, we introduce temporal factor into the trust filtering of users as well as geographical and textual content factors into the trust filtering of locations. To further alleviate the sparsity of user-POI rating matrices, we employ a weighted matrix factorization fused with the POI category factor to learn the user preference. To integrate the trust filtering models and the user preference model, we develop a fused framework with two kinds of integrating methods in relation to the different impacts of factors on the POIs that users have visited and the POIs that users have not visited. Finally, we conduct extensive experiments on Gowalla and Foursquare datasets to evaluate our proposed POI recommendation model, and the results show that our proposed model improves by 13.87% at precision@5 and 10.36% at recall@5 over the state-of-the-art model, which demonstrates that our proposed model outperforms the state-of-the-art method.

With the widespread application of location‐based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast‐updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree‐indexed searching method is proposed to quickly find the answers to user‐defined queries on frequently updating road networks. First, a novel index structure, called the double tree‐hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index‐enhanced search algorithm is proposed to quickly find the k‐nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state‐of‐the‐art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

With the rapid advancement of intelligent transportation systems (ITS), secure processing of color traffic images has become critical for traffic management, safety monitoring, and flow analysis. However, ITS are increasingly susceptible to malicious attacks and data tampering. To address this, we propose an advanced visual encryption method specifically for color traffic images. Our approach employs discrete wavelet transform (DWT) to represent images sparsely, followed by dynamic three‐dimensional spiral disruption for encryption. Chaotic systems then compress the encrypted data, embedding it into a carrier image via least significant bit (LSB) techniques. Experimental results demonstrate robust visual security, achieving a peak signal‐to‐noise ratio (PSNR) above 42 dB, strong resilience against common attacks, and a substantial a key space of 2 ⁵¹² ×10 ⁶⁰ , effectively resisting brute‐force attacks. Neighboring pixel correlation coefficients drop below 0.15, compared to original images (>0.87), and under shear resistance attack with 128×128 data loss, PSNR remains above 23 dB. Additionally, employing P‐tensor product compressed sensing significantly reduces measurement matrix dimensionality, enhancing transmission efficiency. This method offers a viable solution for secure storage and transmission in modern ITS, significantly bolstering privacy and system robustness. The implementation is available at: https://gitee.com/zhangshuo‐aly/image‐encryption .