Block-based construction worker trajectory prediction method driven by site risk

DATA-DRIVEN APPROACH TO HOLISTIC SITUATIONAL AWARENESS IN CONSTRUCTION SITE SAFETY MANAGEMENT

Non-probability sampling network based on anomaly pedestrian trajectory discrimination for pedestrian trajectory prediction

Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians’ implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models.

Pedestrian trajectory prediction can provide decision support for self-driving cars and robots to avoid potential collision risks. Modelling of social relationships and multimodal prediction are core challenges in pedestrian trajectory prediction. However, there are two problems with existing methods: (1) these methods usually focus on modelling pairwise or group interactions, ignoring the importance of high-order interactions; and (2) existing methods struggle to balance the performance between deterministic and multimodal predictions. To address these issues, we propose a higher-order graph attention network for pedestrian trajectory prediction. Specifically, we first construct spatial and temporal graphs to model social interactions and temporal dependencies. To achieve high-order interaction modelling, we design a higher-order graph attention module to capture pedestrian social and temporal interactions of different orders by stacking multiple layers of graph attention. Subsequently, we design a deformable spatial-temporal sparse module to adaptively eliminate social and temporal redundant interactions. To balance deterministic and multimodal prediction performance, we design a two-stage prediction strategy. The first stage predicts pedestrian deterministic trajectories, and the second stage refines pedestrian deterministic trajectories to complete both deterministic and multimodal predictions. Quantitative analysis on the ETH, UCY, and SDD datasets demonstrates that our method outperforms existing methods in both deterministic and multimodal predictions. Qualitative analysis shows that our method can effectively describe complex social relationships and accomplish reliable trajectory prediction.

Pedestrian trajectory prediction is a challenging problem. One of the aspects that makes it so challenging is the fact that the future positions of an agent are not only determined by its previous positions, but also by the interaction of the agent with its neighbors. Previous methods, like Social Attention have considered the interactions with all agents as neighbors. However, this ends up assigning high attention weights to agents who are far away from the queried agent and/or moving in the opposite direction, even though, such agents might have little to no impact on the queried agent's trajectory. Furthermore, trajectory prediction of a queried agent involving all agents in a large crowded scenario is not efficient. In this paper, we propose a novel approach for selecting neighbors of an agent by modeling its perception as a combination of a location and a locomotion model. We demonstrate the performance of our method by comparing it with the existing state-of-the-art method on publicly available datasets. The results show that our neighbor selection model overall improves the accuracy of trajectory prediction and enables prediction in scenarios with large numbers of agents in which other methods do not scale well.

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work and industrial applications. Most of the current advanced trajectory prediction methods incorporate context information such as pedestrian neighbourhood, labelled static obstacles, and the background scene into the trajectory prediction process. In contrast to these methods which require rich contexts, the method in our paper focuses on predicting a pedestrian's future trajectory using his/her observed part of the trajectory only. Our method, which we refer to as LVTA, is a Location-Velocity-Temporal Attention LSTM model where two temporal attention mechanisms are applied to the hidden state vectors from the location and velocity LSTM layers. In addition, a location-velocity attention layer embedded inside a tweak module is used to improve the predicted location and velocity coordinates before they are passed to the next time step. Extensive experiments conducted on three large benchmark datasets and comparison with eleven existing trajectory prediction methods demonstrate that LVTA achieves competitive prediction performance. Specifically, LVTA attains 9.19 pixels Average Displacement Error (ADE) and 17.28 pixels Final Displacement Error (FDE) for the Central Station dataset, and 0.46 metres ADE and 0.92 metres FDE for the ETH&UCY datasets. Furthermore, evaluation on using LVTA to generate trajectories of different prediction lengths and on new scenes without the need of retraining confirms that it has good generalizability.

Pedestrian trajectory prediction is important in various applications such as driverless vehicles, social robots, intelligent tracking systems and space planning. Existing methods focus on analysing the influence of neighbours but ignore the effect of the intended destinations of pedestrians which also plays a key role in route planning. In this paper, we propose a novel two- stage trajectory prediction method to yield multiple prediction trajectories with different probabilities towards different destination regions in the scene. Our method, which we refer to as Bi-Prediction, uses a bidirectional LSTM architecture to automatically classify trajectories into a small number of route classes before trajectory prediction. We have evaluated our method against two baseline methods and three state-of-art methods on two benchmark datasets. Our experimental results show that the extra classification stage improves the accuracy of the predicted trajectories.

This work remedies the drawbacks of existing methods in pedestrian trajectory prediction for video surveillance. A traditional two-stage approach, which first detects and tracks pedestrians before forecasting their subsequent trajectories, relies solely on historical trajectories to predict future movements, overlooking the rich contextual information within videos. Although some one-stage methods have been introduced to streamline this process, they still struggle with complex model architectures due to their reliance on pixel-wise flow estimation. To resolve these issues, we propose a simple yet effective Transformer-based one-stage model, called PredDETR, that directly anticipates the future trajectories of multiple pedestrians from videos. Following the end-to-end paradigm of DETR, our PredDETR model not only associates the bounding boxes of each pedestrian across subsequent frames but also predicts their future trajectories using an additional predictive decoder. Taking past raw video frames as input, the proposed PredDETR directly forecasts the future locations of each pedestrian in a non-autoregressive manner. The empirical results validate that, despite its simpler design, PredDETR is a compelling method compared to a previous one-stage approach.

Safety management is associated with the policies, objectives, procedures, methods, roles and functions that aim at controlling hazard and risk in socio-technical systems. Companies that have implemented effective OSH Management system have reported benefit from increase operational efficiency, reduction in insurance cost and improve in workers retention and satisfaction. Many accidents that occur at construction site are due inadequate adherence of workers to work procedures. The awareness and perception of workers toward safety, health and their working environment are important aspect to enhance the building construction to the better condition to the workers. Workers play an important role in accomplishment of the building construction. All these supporting evidence reflects on the needs to study building construction workers knowledge of safety management system. The study is a criteria – based study, in which 24 construction companies were selected for the study The respondent samples used in the study were drawn from the total population of permanent construction workers in the 24 construction companies selected. The total numbers of permanent construction workers in the 24 construction companies are 750 while 254 were selected for the study. The research questionnaires were administered on 254 permanent construction workers within the 24 construction companies in Abuja. The analysis of the questionnaires survey data was undertaken using the statistical package for social science (SPSS) version 20. A reliability test was conducted on the data in order to control error within the data. The Cronbach's Alpha of the two variable data are .853 and .863 respectively and make the data reliable. Correlation analysis was conducted in order to determine the relationship between worker's general knowledge and worker's safety management knowledge and the result was found to be significant (P less then 0.005). This relationship was modelled using simple linear regression and from the model the result shows that improvement on the worker's safety management system knowledge practice on sites will improves general knowledge among construction workers. Therefore, workers' knowledge of safety management systems significantly influences the overall benefits of safety management system on the construction sites. As such there is need for potential improvement on the knowledge or awareness of the workers to safety management system as perceived from the analysis in order to bring about the expected high performance standard on construction sites.

Pedestrian trajectory prediction via physical-guided position association learning

Pedestrian path prediction is a very challenging problem because scenes are often crowded or contain obstacles. Existing state-of-the-art long short-term memory (LSTM)-based prediction methods have been mainly focused on analyzing the influence of other people in the neighborhood of each pedestrian while neglecting the role of potential destinations in determining a walking path. In this article, we propose classifying pedestrian trajectories into a number of route classes (RCs) and using them to describe the pedestrian movement patterns. Based on the RCs obtained from trajectory clustering, our algorithm, which we name the prediction of pedestrian paths by LSTM (PoPPL), predicts the destination regions through a bidirectional LSTM classification network in the first stage and then generates trajectories corresponding to the predicted destination regions through one of the three proposed LSTM-based architectures in the second stage. Our algorithm also outputs probabilities of multiple predicted trajectories that head toward the destination regions. We have evaluated PoPPL against other state-of-the-art methods on two public data sets. The results show that our algorithm outperforms other methods and incorporating potential destination prediction improves the trajectory prediction accuracy.

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. To tackle these challenges, we propose a spatio-temporal interaction aware and trajectory distribution aware graph convolution network. First, we propose a spatio-temporal interaction aware module that integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions among pedestrians. Second, we design a trajectory distribution aware module to learn latent trajectory distribution information from the measured trajectories at observed and future times. This can provide knowledge-rich trajectory distribution information for the multimodality of the predicted trajectories. Finally, to address the problem of the propagation and accumulation of prediction errors, we design a trajectory decoder to generate the multimodal future trajectories. The proposed model is evaluated utilizing videos recorded by a camera sensor in crowded areas and can be applied to predict multiple pedestrians’ future trajectories from in-vehicle cameras. Experimental results demonstrate that the proposed approach can achieve superior results on the average displacement error (ADE) and final displacement error (FDE) metrics to state-of-the-art approaches and can predict socially acceptable future trajectories.

Accurate trajectory prediction plays a crucial role in optimizing the performance of Internet of Vehicles (IoV) systems, reducing data transmission overhead, and enhancing communication network security. However, the expanding sensing range in IoV has led to increasingly complex spatial-temporal interactions, posing significant challenges for future trajectory prediction endeavors. Currently, predominant approaches involve constructing spatial-temporal interactions through various attention mechanisms. Nevertheless, these methods often yield numerous redundant interactions, potentially resulting in unstable predictions and diffuse interactions. Consequently, there is a pressing need to enhance the application of these methods in trajectory prediction within IoV contexts. Motivated by these challenges, our work introduces a sparse spatial-temporal Transformer (SSTT) to predict vehicle trajectories. SSTT consists of two main Transformer modules: the sparse spatial Transformer and the local-global temporal Transformer. We integrate a learnable sparse plugin into the former to minimize extraneous information in spatial interactions. This plugin enables SSTT to focus more effectively on critical interactive neighbor vehicles by optimizing attention weight distribution, thereby enhancing optimization convergence and prediction accuracy. For the latter, local time windows are employed to capture temporal local correlations and extend the attentional receptive field. Experimental results conducted on three real-world datasets demonstrate that SSTT achieves state-of-the-art performance, and even when only 15% of the training data is used, it can still outperform SOAT baselines. This study presents novel ideas and methodologies for advancing trajectory prediction techniques within the IoV paradigm. The code and our model will be available at GitHub.

Model identification of ship turning maneuver and extreme short-term trajectory prediction under the influence of sea currents