Learned traffic-behavior models are commonly trained separately for each intersection, creating model portfolios that cannot share evidence across sites. We present PhaseShift, a topology-aware framework that harmonizes heterogeneous roadside trajectories into a shared actor-centric representation and trains one reusable backbone. Ego-relative coordinates, trajectory-induced movement paths, normalized signal context, and variable-cardinality interaction tokens remove site conventions while preserving behaviorally relevant topology. The backbone supports pooled operation, zero-shot at a held-out intersection, and low-data adaptation. We evaluate five intersections in two Florida regions on balanced field data, 100k training windows and equal-sized test sets per site under a replay-conditioned, best-of-sampled-trajectory protocol. At 10s, one pooled model lowers both minADE and minFDE relative to trained local models at all five sites, with median reductions of 36.8% and 22.0%. Leave-one-intersection-out deployment, including one cross-region fold, beats local training on both 10-s metrics at four of five sites, although short-horizon performance is less uniform. Fine-tuning with 1,000 target update windows improves on zero-shot at three sites and is the strongest regime at one. At site 7, every cross-site mixture sharply lowers long-horizon error under a fixed 100k-window budget; test-likelihood gains argue against a best-of-sample dispersion-only explanation. Local models fall behind calibrated IDM at the two highest-flow sites after long autoregressive rollouts; pretrained-backbone regimes do not. Within this five-site evaluation, PhaseShift demonstrates consolidation across heterogeneous physical control settings while identifying sites that still require adaptation. The protocol measures conditional single-vehicle generation under replayed context, not closed-loop traffic simulation.
Figures & tables
Fig. 1: PhaseShift’s topology-aware multi-intersection pretraining and deployment workflow. A shared spatial–temporal backbone is trained across harmonized domains and reused as one pooled model, deployed zero-shot at a new intersection, or fine-tuned where local data show that specialization is needed. The domain-diagnosis path treats residual zero-shot error as a prioritization signal, not as proof of a particular behavioral cause.
Site
Train (min)
Flow (veh/h)
Movements
Cycle (s)
Cycle CoV
Mean green split (%)
7
48
2672.93
16
150.0
0.1
19.6
8
48
1126.07
11
131.0
0.3
27.8
9
56
985.37
8
120.0
0.3
46.0
10
84
1332.15
14
117.0
0.3
27.6
22
19
5061.54
24
179.0
0.0
24.0
TABLE I: Characteristics of the five intersection domains. “Train” is the duration of recorded traffic from which each site’s 100,000 training samples are drawn; the total collected recordings are longer (0.42–1.75 h per site). Flow is the rate of vehicles assigned to a movement cluster (clustering uses each track’s starting and ending bounding boxes; vehicles missing either endpoint are excluded); Movements is the number of induced movement clusters; CoV is the coefficient of variation of the signal cycle length; green split is the mean green fraction.
Fig. 2: The five intersection domains used by PhaseShift. The panels are cropped around the monitored conflict zones and are not shown at a common scale.
Target
Method
NLL (2 s)
minADE (2 s)
minFDE (2 s)
NLL (5 s)
minADE (5 s)
minFDE (5 s)
NLL (10 s)
minADE (10 s)
minFDE (10 s)
7
Const.-Vel.
–
1.8559
3.6330
–
2.0404
4.2413
–
2.0216
4.3092
7
IDM
–
1.8021
3.4662
–
1.8026
3.8486
–
1.7216
4.0604
7
Local
-4.6159
0.2800
0.5138
-4.2835
0.8262
1.8892
-4.0209
2.1595
5.1121
7
Zero-Shot
-4.5001
0.4039
0.7651
-5.2363
0.7449
1.9011
-5.6398
0.9247
2.8034
7
Fine-Tuned
-5.0538
0.3577
0.6388
-5.6513
0.6523
1.6335
-5.9421
0.9263
2.9048
7
Pooled
-5.1002
0.2402
0.4105
-5.3704
0.4781
1.1053
-5.4502
0.7111
2.0438
TABLE II: Multi-intersection test results. NLL is in nats; minADE/minFDE are in meters over N=6 sampled rollouts. Lower is better. Bold marks the best single-run displacement value per site and the best learned-model NLL. Macro averages include all five sites.
Fig. 3: Domain-level error reduction relative to independently trained Local models. Positive values mean the shared-backbone regime has lower error; negative values indicate a residual specialization gap. “Mean” and “Med.” summarize site-level percentage reductions.
Split 7/8/9/10/22
minADE (2 s)
minFDE (2 s)
minADE (5 s)
minFDE (5 s)
minADE (10 s)
minFDE (10 s)
100/0/0/0/0
0.2800
0.5138
0.8262
1.8892
2.1595
5.1121
80/5/5/5/5
0.2492
0.4268
0.4774
1.2210
0.7309
2.1264
60/10/10/10/10
0.2241
0.3843
0.4912
1.2340
0.8315
2.5654
40/15/15/15/15
0.2599
0.4607
0.5560
1.2933
1.0697
2.9718
20/20/20/20/20
0.2438
0.4015
0.4641
1.0681
0.9986
2.8753
TABLE III: Fixed-100,000-window composition sweep at site 7. Splits give the percentage drawn from sites 7/8/9/10/22; 100/0/0/0/0 is Local . Bold marks the best value per column. One run per composition.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Parameter
Value
Scene context
Time step Δt
0.1s
Observation history Th
20 steps ( 2.0s )
Prediction horizon Tf
20 steps ( 2.0s )
Max. neighbors
10
Neighbor radius
15m
Appendix
TABLE IV: Model and training hyperparameters.
Parameter
Pre-training
Fine-tuning
Learning rate
1×10−3
1×10−5
Batch size
256
64
Warmup steps
4000
20
Training windows M
100,000
1,000
Appendix
TABLE V: Fine-tuning hyperparameters. All values not listed are inherited unchanged from the pre-trained model (Table IV ).
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop. We present Enactor, an actor-centric generative model for closed-loop intersection microsimulation. The model focuses on vehicles; pedestrians are included as context that can influence vehicle decisions but not predicted. Dynamic actors and lane polylines are encoded in polar coordinates referenced to the intersection center. A transformer with separate spatial and temporal attention blocks predicts a distribution over each actor's next-step motion (s, α). Training uses a closed-loop curriculum so the model is exposed to its own predictions. We evaluate Enactor in two regimes. In a 4000-second simulation-in-the-loop test at two intersection geometries, Enactor controls every dynamic vehicle against a continuously refreshing actor set rather than the fixed cohort that learned trajectory predictors are usually evaluated against. It recovers the SUMO data generator's speed and travel-time distributions with KL divergence over an order of magnitude lower than a recent transformer baseline on travel time, and substantially lower on speed (roughly 5× lower at Site 1), and reduces red-light violations relative to the same baseline by more than an order of magnitude. An ablation isolates the leader rear-bumper feature as the change with the largest effect on intersection-aware safety metrics. We also evaluate on real-world field data and apply the same architecture to naturalistic vehicle trajectories from a fish-eye camera at a signalized intersection and evaluate it on multi-horizon predictive tasks. Enactor outperforms a constant-velocity baseline at every horizon evaluated.
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
Accurate prediction of intersection turning movements is essential for adaptive signal control but remains difficult due to the high volatility of directional flows. This study proposes HFD-TM (Hierarchical Flow-Decomposition for Turning Movement Prediction), a hierarchical deep learning framework that predicts turning movements by first forecasting corridor through-movements and then expanding these predictions to individual turning streams. This design is motivated by empirical traffic structure, where corridor flows account for 65.1% of total volume, exhibit lower volatility than turning movements, and explain 35.5% of turning-movement variance. A physics-informed loss function enforces flow conservation to maintain structural consistency. Evaluated on six months of 15-minute interval LiDAR (Light Detection and Ranging) data from a six-intersection corridor in Nashville, Tennessee, HFD-TM achieves a mean absolute error of 2.49 vehicles per interval, reducing MAE by 5.7% compared to a Transformer and by 27.0% compared to a GRU (Gated Recurrent Unit). Ablation results show that hierarchical decomposition provides the largest performance gain, while training time is 12.8 times lower than DCRNN (Diffusion Convolutional Recurrent Neural Network), demonstrating suitability for real-time traffic applications.
Md Atiqur Rahman Mallick, Kamrul Hasan, Pulock Das +2
Department of Electrical and Computer Engineering, Tennessee State University, Nashville, TN, USA
Reinforcement learning (RL) has shown promise in traffic signal control (TSC). However, its reliance on predefined states limits responsiveness to observable open-world events that are absent from training data. IoT-enabled intersections provide heterogeneous observations from roadside sensors and cameras, creating opportunities to improve RL adaptability to such events. To this end, we propose ReasonLight, a multimodal foundation model-enhanced RL framework for zero-shot TSC. ReasonLight integrates three sources of information: structured traffic measurements, multi-view camera observations, and candidate phase decisions from a pre-trained RL controller. Given an RL-proposed phase, ReasonLight extracts visual semantics from multi-view images and aligns them with compact sensor-derived scene descriptions. This alignment enables a semantic-guided refinement module to either preserve or adjust the proposed action according to traffic rules and event semantics. To ensure operational reliability, refined actions are constrained by the set of available phases. Any invalid decision is rejected, and the system falls back to the original RL action. We evaluate ReasonLight on two types of rare events not seen during RL training: emergency vehicle priority and temporary traffic regulation. Experimental results show that ReasonLight achieves zero-shot adaptation without retraining. It reduces emergency vehicle waiting time by up to 88.7% compared with the RL-only backbone while preserving comparable routine traffic performance.
Aoyu Pang, Maonan Wang, Yuejiao Xie +3
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong · Shanghai AI Laboratory, Shanghai, China +1