RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

• URL: http://arxiv.org/abs/2608.20656v1
• Date: Fri, 21 Aug 2026 01:26:15 GMT
• Title: RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction
• Abstract summary: We develop a public benchmark suite that preserves raw flow, speed, and occupancy measurements from PeMS detectors for flow prediction.<n>We also propose textbfRiskTraf, a model-agnostic risk-extrapolated residual plug-in.<n>RiskTraf consistently improves diverse forecasting backbones and outperforms debiasing and distribution-shift adaptation methods.
• Score: 8.558982321151184
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw measurements reliably. Although speed and occupancy provide sensor-native traffic-state information beyond flow alone, existing releases often omit these variables, replace them with proxies, or contain logically inconsistent records. Moreover, direct empirical risk minimization over three-variable inputs may exploit regime-dependent shortcuts, as the relationships among flow, speed, and occupancy vary substantially between free-flow and congested states. We introduce \textbf{PEMSB-3V}, a public benchmark suite that preserves raw flow, speed, and occupancy measurements from PeMS detectors for flow prediction. We also propose \textbf{RiskTraf}, a model-agnostic risk-extrapolated residual plug-in. For each trained spatio-temporal backbone, RiskTraf freezes the selected checkpoint and learns a lightweight zero-start residual head from historical speed and occupancy. The residual head constructs ordered traffic-risk environments and optimizes horizon-wise flow corrections with a risk extrapolation objective, thereby mitigating regime-specific shortcut correlations without modifying the backbone. Extensive experiments demonstrate that RiskTraf consistently improves diverse forecasting backbones and outperforms debiasing and distribution-shift adaptation methods. Our code and benchmark are available at https://github.com/Guangyu4/RiskTraf.

Related papers

• RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation [17.00557757061364]
  RiskFlow is a closed-loop safety-critical multi-agent traffic generation framework.<n>It formulates future trajectory generation as transport in the action space.<n>It consistently improves realism while maintaining competitive safety-critical generation capability.
  arXiv  Detail & Related papers  (2026-06-04T17:28:42Z)
• Riemannian MeanFlow for One-Step Generation on Manifolds [54.09734511705173]
  Flow Matching enables simulation-free training of generative models.<n>MeanFlow can be extended to manifold-valued generation where velocities lie in location-dependent tangent spaces.<n>Experiments on spheres, tori, and SO(3) demonstrate competitive one-step sampling with improved quality-efficiency trade-offs and substantially reduced sampling cost.
  arXiv  Detail & Related papers  (2026-03-11T12:41:46Z)
• Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling [54.94692733670454]
  Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles.<n>We propose a lightweight yet highly accurate streaming-based trajectory forecasting approach.<n>Our approach significantly reduces inference latency, making it well-suited for real-world deployment.
  arXiv  Detail & Related papers  (2026-03-02T13:44:23Z)
• Learning Minimally-Congested Drive Times from Sparse Open Networks: A Lightweight RF-Based Estimator for Urban Roadway Operations [0.0]
  This paper develops a lightweight estimator for minimally-congested car travel times.<n>It integrates open road-network data, speed constraints, and sparse control/turn features within a random forest framework.<n>It preserves point-to-point fidelity at metropolitan scale, reduces resource requirements, and supplies defensible performance estimates.
  arXiv  Detail & Related papers  (2026-01-04T09:54:44Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving [0.6299766708197883]
  We propose a unified, context-aware framework that adapts labels and models over time and across drivers.<n>The framework is tested using two safety indicators, speed-weighted headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN)<n>Overall, the framework shows promise for adaptive, context-aware risk detection that can enhance real-time safety feedback and support driver-focused interventions in intelligent transportation systems.
  arXiv  Detail & Related papers  (2025-07-26T16:24:25Z)
• ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors [0.0]
  This work introduces ON-Traffic, a novel deep operator and receding horizon learning-based framework tailored for online estimation of traffic state.<n>Our framework is evaluated in both numerical and simulation datasets, showcasing its ability to handle irregular, sparse input data, adapt to time-shifted, and provide well-calibrated uncertainty estimates.<n>The results demonstrate that the model captures complex traffic scenarios, including shockwaves and congestion propagation, while maintaining robustness to noise and sensor dropout.
  arXiv  Detail & Related papers  (2025-03-18T09:13:24Z)
• Geometry-Aware Instrumental Variable Regression [56.16884466478886]
  We propose a transport-based IV estimator that takes into account the geometry of the data manifold through data-derivative information. We provide a simple plug-and-play implementation of our method that performs on par with related estimators in standard settings.
  arXiv  Detail & Related papers  (2024-05-19T17:49:33Z)
• Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs [47.18837782862979]
  This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. We demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues.
  arXiv  Detail & Related papers  (2023-03-10T02:22:33Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Accurate non-stationary short-term traffic flow prediction method [0.0]
  This paper proposes a Long Short-Term Memory (LSTM) based method that can forecast short-term traffic flow precisely. The proposed method performs favorably against other state-of-the-art methods with better performance on extreme outliers, delay effects, and trend-changing responses.
  arXiv  Detail & Related papers  (2022-05-01T17:11:34Z)
• GMFlow: Learning Optical Flow via Global Matching [124.57850500778277]
  We propose a GMFlow framework for learning optical flow estimation. It consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark.
  arXiv  Detail & Related papers  (2021-11-26T18:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.