Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still poses a substantial challenge. To address this, our study introduces the Spatio Temporal Attention-based methodology for Target Vehicle Trajectory Prediction (STATVTPred). This approach integrates Global Positioning System(GPS) localization technology to track target movement and dynamically predict the vehicle’s future path using comprehensive spatio-temporal trajectory data. We map the vehicle trajectory onto a directed graph, after which spatial attributes are extracted via a Graph Attention Networks(GATs). The Transformer technology is employed to yield temporal features from the sequence. These elements are then amalgamated with local road network structure maps to filter and deliver a smooth trajectory sequence, resulting in precise vehicle trajectory prediction.This study validates our proposed STATVTPred method on T-Drive and Chengdu taxi-trajectory datasets. STATVTPred achieves an AMR of 73.07% on the Beijing dataset, surpassing the Transformer by 6.38% and the LSTM Encoder-Decoder by 37.45%, while also reducing Distance Error (DE) by 26.93% and 20.95% in Beijing and Chengdu, respectively, also much lower than the baseline results. This is expected to establish STATVTPred as a new approach for handling trajectory prediction of targets in logistics and transportation scenarios, thereby enhancing prediction accuracy.

Abstract In the field of traffic sign detection, effective data augmentation can improve the model's detection capacity, enabling the model to distinguish and locate traffic signs more precisely and enhancing driving safety. However, due to the small size and low representation of traffic signs in the dataset, standard common data augmentation techniques are not suitable for traffic sign detection. To address this issue, a novel data augmentation strategy called flexible cut and paste (FlexibleCP) is proposed. The overall enhancement approach is shifted from multi‐image fusion to target cropping and pasting. By introducing parameters to control the target pasting ratio and scaling ratio, the diversity of small target data and their size variations are enriched. Additionally, target size and type filters are added to enable targeted enhancement for different sizes and types of targets. This study, evaluates the proposed strategy using two representative traffic sign detection datasets, namely CTSD and GTSDB. The experimental results demonstrate a significant improvement in both detection and recognition performance of the model: on the CTSD dataset, the models trained with FlexibleCP data enhancement achieve 88.9% and 64.5% mAP0.5 and mAP0.5:0.95, respectively, which are 3.5% and 2.5% better than those trained with mosaic data enhancement; on the GTSDB dataset mAP0.5 and mAP0.5:0.95 reached 89.2% and 56.0%, respectively, an improvement of 4.0% and 3.9% over mosaic.

An intelligent manhole cover management system (IMCS) is one of the most important basic platforms in a smart city to prevent frequent manhole cover accidents. Manhole cover displacement, loss, and damage pose threats to personal safety, which is contrary to the aim of smart cities. This paper proposes an edge computing-based IMCS for smart cities. A unique radio frequency identification tag with tilt and vibration sensors is used for each manhole cover, and a Narrowband Internet of Things is adopted for communication. Meanwhile, edge computing servers interact with corresponding management personnel through mobile devices based on the collected information. A demonstration application of the proposed IMCS in the Xiasha District of Hangzhou, China, showed its high efficiency. It efficiently reduced the average repair time, which could improve the security for both people and manhole covers.