Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.

The occurrence of fatal traffic accidents often causes serious casualties and property losses, endangering travel safety. This work uses the statistical data of fatal road traffic accidents in Shenzhen from 2018 to 2022 as the basis to determine the characteristic patterns and the main influencing factors of the occurrence of fatal road traffic accidents. The accident description data are also analyzed using the analysis method based on Term Frequency-Inverse Document Frequency (TF-IDF) data mining to obtain the characteristics of accident fields, objects, and types. Furthermore, this work conducts a kernel density analysis combined with spatial autocorrelation to determine the hotspot areas of accident occurrence and analyze their spatial aggregation effects. A principal component analysis is performed to calculate the factors related to the accident subjects. Results showed that weak safety awareness of motorists and irregular driving operations are the main factors for the occurrence of accidents. Finally, targeted safety management strategies are proposed based on the analysis results. In the current data era, the research results of this paper can be used for the prevention and emergency of accidents to formulate corresponding measures, and provide a theoretical basis for decision making.

Due to their high kinematic characteristics, six-bar tensegrities have great potential application value in the field of robotics, especially in the field of deep space exploration robots. In this paper, an ultralight six-bar tensegrity robot is designed, and a gait planning method for continuous motion is proposed. First, the equilibrium matrix of the tensegrity structure is constructed, and singular value decomposition (SVD) is performed to find the node coordinates and internal forces of the tensegrity structure. Two representative examples regarding tensegrity structures are presented to demonstrate the capability of the proposed method in the initial selfstress design that satisfies the stability of tensegrities. Furthermore, both the principal rolling analysis and gait planning are also addressed based on the offset of the center of gravity. A six-bar tensegrity robot prototype is developed, and the obstacle avoidance experiment is completed. Finally, the results show that the six-bar tensegrity robot has good kinematic performance. Moreover, this robot is expected to play a key role in future planetary exploration.

Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.

Aviation resources in the post-pandemic era are still in short supply. The increasing air traffic flow aggravates flight delays and makes it difficult to ensure aviation safety. Instead of focusing on the economic benefits, this paper proposes a quantitative method for evaluating the safety of flight plans. A flight plan safety index system is constructed through airspace environment modelling and a conflict probability calculation. The proposed method provides a quantitative basis for the management and adjustment of flight plans at the strategic level. Improving the flight plan management strategy from the perspective of conflict avoidance is expected to fundamentally reduce the potential conflict and workload of controllers and pilots during flight and improve the safety level of the whole air transport system. Finally, the performance of the proposed flight plan safety evaluation method is demonstrated through an illustrative air traffic scenario.

This article presents a novel risk assessment method for aircraft flight plans in a four-dimensional trajectory-based operation background. With the increasing air transport volume and airspace demand and during the allocation and modification of flight plans, safety should also be considered in addition to economic benefits and competitiveness. By employing the time geography concept, this article first quantifies the space-time accessibility and estimates the visiting probability of aircraft in the four-dimensional trajectory-based operation context. On this basis, this article studies the method and process to evaluate the conflict risk between one flight plan and a special airspace, the weather-influenced airspace, and another flight plan. This article also proposes the conflict risk alerting strategy. The achievements of the study are expected to provide assistance for air traffic controllers in strategic conflict avoidance and are of great significance in assuring air transport safety.