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• A hybrid graphical network approach considering causality named AC-GGN is proposed. • It can accurately make spatio-temporal prediction of deep excavation-induced ground settlement. • It is verified in a practical excavation case to reach high accuracy for each monitoring point. • Data augmentation techniques help to strengthen the long-term prediction ability of AC-GGN. • Global sensitivity analysis is conducted to quantitatively explain prediction behavior of AC-GGN. With the increasing demand for deep and large-scale excavation pits, the deformation response during excavation has become exceedingly complex, especially located in building-intensive areas. This paper proposes a hybrid deep learning model named attention-causality-based graphical gated network (AC-GGN) to accurately make the spatio-temporal prediction about the excavation-induced ground settlement at different monitoring points during the foundation pit construction. The novelty of the AC-GGN model lies in its flexible integration of four key components, including the Granger causality (GC) test, graph convolutional network (GCN), the gated recurrent unit (GRU), and attention mechanisms, which work together to effectively capture casual relationships along with spatial and temporal dependence embedded in the observed time-series from each monitoring points and then boost the prediction performance. To validate its applicability and superiority, a case study about a metro station excavation project in the Shanghai Metro Line 14 is conducted. Results indicate that the AC-GGN model outperforms state-of-the-art algorithms, which can make precise predictions for each monitoring point. The proper data augmentation technique facilitates long-term prediction with high accuracy, thereby expanding the scope of AC-GGN application beyond short-term prediction. Moreover, the global sensitivity analysis can be used to reveal which monitoring points have the most significant impact on ground settlement prediction. It can aid in identifying key risk areas for monitoring and control. In summary, the novel architecture of AC-GGN is beneficial to dynamically capture and predict the trend of ground settlement across different areas of the construction site. Practically, the accurate prediction results generated by AC-GGN offer rich evidence to not only perceive the excavation-induced risk development but also formulate corresponding measures in advance for risk mitigation. [ABSTRACT FROM AUTHOR]

• Public ecological aesthetic perception basically follows a three-step process (seeing ecological elements—concept perception processing—aesthetic experience). • Ecological and aesthetic perceptions vary in terms of conceptual relevance and processing. • Natural landscape elements are more likely to contribute to positive ecosystem aesthetic perception. • Evaluation models developed using Bayesian networks combined with deep learning and eye-tracking have practical applications. Public perceptions of ecological aesthetics are a crucial component in evaluations of the quality of urban green spaces (UGSs) and in guiding urban planning, design and renewal practices. In this paper, we propose a novel methodological framework that combines deep learning and eye-tracking to investigate the relationships between landscape elements, features, visual focus and public ecosystem aesthetic perceptions of UGSs. Our Public Ecosystem Aesthetic Perceptions (PEAP) model emphasises interactions between ecosystem quality and landscape aesthetic quality. To validate the model, we conducted a study in Shanghai, China, where we selected 54 representative UGSs for photography, perception surveys, and image processing. The results demonstrated that visual focus acts as a mediator between landscape stimuli (landscape elements and features) and perceptions of ecosystem aesthetics. Notably, we found that natural elements elicited significantly stronger visual focus than artificial elements. Thus, our PEAP approach can enhance understanding of aesthetic qualities of landscapes and ecological qualities of urban ecosystems, enabling creation of more aesthetically pleasing and ecologically sustainable UGSs. [ABSTRACT FROM AUTHOR]

It is critical for the management and control of urban rail transit (URT) to be able to predict passenger flow accurately and in real time. Considering that the high-resolution data aggregated by the automatic fare collection (AFC) system is wasted, this paper analyzes the problem of applying a multi-time granularity passenger flow data fusion forecasting process. First, we examine the challenge of constructing a dataset of passenger flow data with different time granularities. Thus, an algorithm is proposed for selecting passenger flow datasets with multi-time granularity. Furthermore, a multi-time granularity dense residual network (Mul-DesLSTM) with a dense residual structure and LSTM (long short-term memory) as the predictor is constructed, inspired by a residual network. Using Mul-DesLSTM, finer-grained passenger flow features can be fused layer by layer while maintaining the accuracy of traditional single-granularity passenger flow predictions. Lastly, Mul-DesLSTM is applied to the URT system of Shanghai, China, and compared with baselines. As a result, the proposed Mul-DesLSTM outperforms the baselines with LSTM as a predictor and state-of-the-art model. When the predicted time granularity is 30 min, compared to the single-time granularity LSTM network, the mean absolute error, root mean square error, and symmetric mean absolute percentage error can be reduced by 51%, 63%, and 15%, respectively. The results can serve as a reference and basis for the operation and management of URT systems. [ABSTRACT FROM AUTHOR]

Interacting with complex environments is a major challenge for autonomous vehicles (AVs), especially interacting with bicycles, because bicycle trajectories are difficult to predict. Bicycles typically interact with multiple traffic participants simultaneously, resulting in diverse and random trajectories, even sudden-change behavior. Sudden-change behavior of bicycles, which is hazardous for AVs but with sparse data, cannot be well learned by current deep-learning models due to the characteristics of "learning average behavior". Furthermore, the current domain knowledge of explicit trajectory laws integrated into the deep learning modes cannot be used to describe the implicit decision-making of cyclists which is a major factor affecting cyclists' sudden change behavior. Based on these observations, this paper develops a two-layer integrated framework for bicycle trajectory prediction. The trajectory generation layer is used to provide a kinematically feasible set of candidate trajectories, and then the trajectory selection layer is performed to select a reasonable trajectory by analyzing the cyclists' decision-making. In the trajectory generation layer, the kinematic laws are first used to determine a feasible set of endpoints, and then deep learning is utilized to learn and generate each complete trajectory under the known start and endpoints. In the trajectory selection layer, a utility-based model is proposed to analyze the decision-making for cyclists, which is defined as the cyclist's tradeoff between path efficiency and risk in the multiple interactions of the environment. Finally, the candidate with the maximum utility serves as the predicted trajectory. The reasonableness of the proposed model has been verified using the bicycle trajectory data from an intersection in Shanghai, China. Compared with the state-of-the-art models, the integrated model not only exhibits significantly lower errors but also fewer long-tail samples for the prediction error. In addition, it demonstrates a higher validity of predicting cycling decisions during sudden changes in behaviors. [ABSTRACT FROM AUTHOR]