Understanding how many aircraft, and which traffic configurations, can be safely and efficiently managed by air traffic controllers within a given airspace is fundamental to air traffic management. Air traffic controllers continuously modify aircraft trajectories through interventions such as speed advisories and radar vectoring, and the patterns of these interventions are difficult to capture effectively using existing air traffic simulators. To address this limitation, we propose Diffusion Transformers for Traffic Scene Generation (DiTSG), which learns to generate air traffic scenes from recorded multi-aircraft trajectories. DiTSG employs stacked multi-agent inverted Transformer layers as the denoising backbone of a diffusion transformer, jointly generates the trajectories of multiple aircraft within a traffic scene, and conditions the generation on agent-specific required times of arrival. The resulting trajectories generated by DiTSG preserve realistic inter-aircraft interactions observed in actual air traffic operations. Experiments show that the DiTSG generates diverse traffic scenes that reproduce the statistical properties of recorded traffic while preserving realistic inter-aircraft interactions.
Figures & tables
Fig. 1: Overview of DiTSG . Left: a DiTSG block, which applies masked multivariate attention, agent attention, and a feed-forward layer, each modulated by the conditioning vector Y through adaLN. Right: the sampling loop, which tokenizes the noisy scene Xs , passes it through L blocks and a linear layer to obtain the velocity Vθ , and integrates the ODE for K Euler steps from noise to a traffic scene.
Method
Docc
Rnov
Diversity
SVR (%)
Real
–
1.000
–
0.000
MA-VAE
0.033
0.947
16.690
0.250
DiTSG
0.015
0.953
14.938
0.101
TABLE I: Evaluation of generated traffic scenes.
Fig. 2: Separation patterns in real and generated traffic. Distribution of the minimum horizontal (left) and vertical (right) separation between aircraft pairs. Dashed lines indicate the separation minima.
Fig. 3: Traffic fidelity across arrival stages and diffusion timesteps. JSD between real and generated traffic within each RTA ( τ ) range (left). Horizontal and vertical mean absolute error between DiTSG’s predicted and true positions as a function of the diffusion timestep s (right).
Fig. 4: Generated traffic scenes with varying numbers of aircraft. Examples of scenes generated by DiTSG for N=6 to N=11 . Each panel shows one scene over the ICN terminal area, with STARs and runways shown in gray for reference. Aircraft are labeled AC1 to AC N in landing order, and each label reports the RTA τ , current altitude, and ground speed.
Fig. 5: Generated alternatives under the same RTA condition. Comparison between a recorded traffic scene and the closest scenes generated by DiTSG under the same τ .
Fig. 6: Agent attention throughout the denoising process. The query aircraft is shown in orange, while the other aircraft are colored according to the attention they receive from the query. Intermediate states are shown as points rather than trajectories because they remain noisy during early denoising steps.