Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing

• URL: http://arxiv.org/abs/2608.13993v1
• Date: Fri, 14 Aug 2026 06:19:47 GMT
• Title: Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing
• Abstract summary: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.<n>Machine learning models trained on such sparse data cannot generalize unmonitored locations.<n>We propose a simulation-based methodology that addresses this problem by generating augmented traffic count datasets.
• Score: 1.237454174824584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning models trained on such sparse data cannot generalize to unmonitored locations and must be retrained whenever the sensor infrastructure changes. We propose a simulation-based methodology that addresses this problem by generating augmented traffic count datasets in which each physical sensor is replaced by a virtual sensor placed at a surrogate location in the road network. Virtual sensors are selected by a graph-search heuristic that jointly maximises vehicle-flow continuity and traffic-metric similarity between the original and surrogate locations, while enforcing a minimum spatial displacement to ensure diversity of observed traffic conditions. We validate the method on two Belgian cities: Brussels, using a calibrated model, and Namur, using synthetic models. The augmented datasets preserve the bimodal daily demand profile and the dynamics of traffic at the observed locations.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data [1.735650011442274]
  This study presents a novel framework for dynamic origin-destination demand estimation (DODE) in multi-class mesoscopic network models.<n>We leverage high-resolution satellite imagery together with conventional traffic data from local sensors.<n>Results show that supplementing traditional data with satellite-derived density significantly improves estimation performance.
  arXiv  Detail & Related papers  (2025-06-25T06:47:06Z)
• 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)
• Calibration of Vehicular Traffic Simulation Models by Local Optimization [43.942239814508106]
  Calibrating simulation models using traffic count data is challenging because of complexity of environment, lack of data, and uncertainties in traffic dynamics.<n>This paper introduces a novel simulation-based traffic calibration technique.<n>We assess the proposed technique on a model of Brussels, Belgium, using data from real traffic monitoring devices.
  arXiv  Detail & Related papers  (2025-02-17T09:17:01Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting [32.354251863295424]
  Traffic forecasting is a critical task to extract values from cyber-physical infrastructures. In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Next, we propose a novel module called Spatial Graph Transformers (SGT) to leverage the self-attention mechanism. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-lineartemporal traffic dynamics.
  arXiv  Detail & Related papers  (2023-02-20T12:57:31Z)
• 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)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)

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.