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Trajectory prediction serves as a prerequisite for future trajectory-based operation, significantly reducing the uncertainty of aircraft movement information within airspace by scientifically forecasting the three-dimensional positions of aircraft over a certain period. As convergence points in the aviation network, airport terminal airspace exhibits the most complex traffic conditions in the entire air route network. It has stronger mutual influences and interactions among aircraft compared to the en-route phase. Current research typically uses the trajectory time series information of a single aircraft as input for subsequent predictions. However, it often lacks consideration of the close-range spatial interactions between multiple aircraft in the terminal airspace. This results in a gap in the study of aircraft trajectory prediction that couples spatiotemporal features. This paper aims to predict the four-dimensional trajectories of aircraft in terminal airspace, constructing a Spatio-Temporal Transformer (ST-Transformer) prediction model based on temporal and spatial attention mechanisms. Using radar aircraft trajectory data from the Guangzhou Baiyun Airport terminal airspace, the results indicate that the proposed ST-Transformer model has a smaller prediction error compared to mainstream deep learning prediction models. This demonstrates that the model can better integrate the temporal sequence correlation of trajectory features and the potential spatial interaction information among trajectories for accurate prediction. [ABSTRACT FROM AUTHOR]

Microscopic modeling of mixed traffic (i.e., automaton-driven vehicles and human-driven vehicles) dynamics, particularly car-following, lane-changing, and gap-acceptance, provides the opportunity to gain a more accurate estimation of flow-density relationships for both traditional traffic with human-driven vehicles and different mixed traffic scenarios. Our paper proposes a microscopic framework to model multilane traffic for both vehicle types on shared roadways which sets the stage to explore the capability of macroscopic car-following models in general to explain the fundamental flow-density relationship. Since prior models inadequately represent the fundamental diagram realistically, we propose a rectified macroscopic flow model that can account for the impact of both lane-changing and gap-acceptance. Differentiability, boundary conditions, and flexibility of the proposed model are tested to validate its applicability. Finally, the capability to interpret the flow-density relationship by the proposed model is verified for different mixed traffic scenarios. Although few model parameter values were obtained directly from the simulation input, the rest of the parameters have been calibrated by flow and density outputs from the simulations. The analysis results show a distinct correlation between the proposed model parameters with automation-driven vehicle shares and lane-changing rates of traffic. The findings from this study emphasize the importance of taking complete motion dynamics into account, rather than partial motion dynamics (i.e., car-following) as has been the case in the previous studies, to explain macroscopic traffic flow characteristics, irrespective of the vehicle type., Author(s): Mudasser Seraj (corresponding author) [1]; Jiangchen Li [1]; Tony Z. Qiu [1] 1. Introduction The rapid development of Connected and Automated Vehicle (CAV) technology has motivated researchers and practitioners [...]

Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naive microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations., 1. Introduction Vehicles with diverse levels of integrated connectivity and automated control systems are considered to be pushing a technological leap towards diminished trip delay, fuel efficiency, reduced emission, and [...]