Q-Net: Transferable Queue Length Estimation via Kalman-based Neural Networks

• URL: http://arxiv.org/abs/2509.24725v1
• Date: Mon, 29 Sep 2025 12:51:08 GMT
• Title: Q-Net: Transferable Queue Length Estimation via Kalman-based Neural Networks
• Abstract summary: Estimating queue lengths at signalized intersections remains a challenge in traffic management.<n>This paper introduces Q-Net, a data-efficient and interpretable framework for queue length estimation.<n>Q-Net integrates two widely available and privacy-friendly data sources.
• Score: 16.815532601851217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating queue lengths at signalized intersections remains a challenge in traffic management, especially under partially observed conditions where vehicle flows are not fully captured. This paper introduces Q-Net, a data-efficient and interpretable framework for queue length estimation that performs robustly even when traffic conservation assumptions are violated. Q-Net integrates two widely available and privacy-friendly data sources: (i) vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD), which divides each road section into segments and provides segment-wise average speed measurements. These data sources often differ in spatial and temporal resolution, creating fusion challenges. Q-Net addresses this by employing a tailored state-space model and an AI-augmented Kalman filter, KalmanNet, which learns the Kalman gain from data without requiring prior knowledge of noise covariances or full system dynamics. We build on the vanilla KalmanNet pipeline to decouple measurement dimensionality from section length, enabling spatial transferability across road segments. Unlike black-box models, Q-Net maintains physical interpretability, with internal variables linked to real-world traffic dynamics. Evaluations on main roads in Rotterdam, the Netherlands, demonstrate that Q-Net outperforms baseline methods by over 60\% in Root Mean Square Error (RMSE), accurately tracking queue formation and dissipation while correcting aFCD-induced delays. Q-Net also demonstrates strong spatial and temporal transferability, enabling deployment without costly sensing infrastructure like cameras or radar. Additionally, we propose a real-time variant of Q-Net, highlighting its potential for integration into dynamic, queue-based traffic control systems.

Related papers

• Fusing Cellular Network Data and Tollbooth Counts for Urban Traffic Flow Estimation [0.0]
  Traffic simulations, essential for planning urban transit infrastructure interventions, require origin-destination (OD) data.<n>Existing data sources are imperfect: sparse tollbooth sensors provide accurate vehicle counts by category, while extensive mobility data from cellular network activity captures aggregated crowd movement.<n>This study develops a machine learning framework to correct and disaggregate cellular network data using sparse tollbooth counts as ground truth.
  arXiv  Detail & Related papers  (2026-04-17T07:40:10Z)
• Adaptive Learned State Estimation based on KalmanNet [0.38290014221246177]
  Adaptive Multi-modal KalmanNet (AM-KNet) is an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting.<n>AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently.<n>Results demonstrate improved estimation accuracy and tracking stability compared to the base KalmanNet.
  arXiv  Detail & Related papers  (2026-04-02T18:12:40Z)
• Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention [6.298495506269846]
  We present the Hybrid Directed-Attention Spatio-Temporal Graph Neural Network (HDA-STGNN), an inductive deep learning framework designed to tackle the network-wide volume estimation problem.<n>Our approach leverages speed profiles, static road attributes, and network topology to predict daily traffic volume across all road segments in the network.
  arXiv  Detail & Related papers  (2025-12-15T11:30:44Z)
• 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)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Transport-Hub-Aware Spatial-Temporal Adaptive Graph Transformer for Traffic Flow Prediction [9.99440375621286]
  We propose a Transport-Hub-aware spatial-temporal adaptive graph transFormer for traffic flow prediction.<n>Specifically, we first design a novel spatial self-attention module to capture the dynamic spatial dependencies.<n>We also employ a temporal self-attention module to detect dynamic temporal patterns in the traffic flow data.
  arXiv  Detail & Related papers  (2023-10-12T13:44:35Z)
• Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network [46.409567044630116]
  We propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. We first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow.
  arXiv  Detail & Related papers  (2023-08-31T15:49:21Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• 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)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction [16.010576606023417]
  We propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective.
  arXiv  Detail & Related papers  (2020-03-16T05:36:58Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.