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<p>Accurate network macroscopic fundamental diagram (NMFD) estimation requires accurate spatial mean speed estimates that are hard to get from loop detector devices (LDD) as they capture local speeds only. This study introduces a correction method able to reconstruct the mean speed from loop data leveraging floating car devices (FCD) during the training phase. This significantly improves LDD-based NMFD estimation. Unlike previous studies focusing on local speed corrections, our approach integrates LDD and FCD using machine learning techniques to enhance link-average speed estimation. We compare the performance of Ordinary Least-Squares Regression (OLSR), Random Forest (RF), Multilayer perceptron (MLP), and XGBoost models, using a comprehensive dataset to evaluate their effectiveness in aligning LDD speeds with FCD as ground truth average speed. The RF model showed the highest accuracy, with a 37.44% improvement over original LDD speeds and a Root-Mean-Squared Error (RMSE) of 9.54 km/h. Additionally, we investigated the impact of LDD positions and demand by separating loading and unloading periods. The RF model’s adjustments were especially effective under these conditions, resulting in an improved accuracy of 44.9%. Our analysis of NMFDs further validated the RF model’s robustness and accurate estimation of NMFDs using corrected LDD. We validate our methodology across two urban networks, Athens and Lyon, demonstrating its spatial and temporal transferability. Results show that bias correction significantly improves LDD speed accuracy, leading to more reliable NMFD estimation. This work advances data fusion methods for urban traffic monitoring and provides a robust framework for addressing LDD positional bias in large-scale traffic studies.</p>

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Estimating Spatial Mean Speeds from Local Sensors: A Machine-Learning Approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • Nandan Maiti,
  • Ludovic Leclercq

摘要

Accurate network macroscopic fundamental diagram (NMFD) estimation requires accurate spatial mean speed estimates that are hard to get from loop detector devices (LDD) as they capture local speeds only. This study introduces a correction method able to reconstruct the mean speed from loop data leveraging floating car devices (FCD) during the training phase. This significantly improves LDD-based NMFD estimation. Unlike previous studies focusing on local speed corrections, our approach integrates LDD and FCD using machine learning techniques to enhance link-average speed estimation. We compare the performance of Ordinary Least-Squares Regression (OLSR), Random Forest (RF), Multilayer perceptron (MLP), and XGBoost models, using a comprehensive dataset to evaluate their effectiveness in aligning LDD speeds with FCD as ground truth average speed. The RF model showed the highest accuracy, with a 37.44% improvement over original LDD speeds and a Root-Mean-Squared Error (RMSE) of 9.54 km/h. Additionally, we investigated the impact of LDD positions and demand by separating loading and unloading periods. The RF model’s adjustments were especially effective under these conditions, resulting in an improved accuracy of 44.9%. Our analysis of NMFDs further validated the RF model’s robustness and accurate estimation of NMFDs using corrected LDD. We validate our methodology across two urban networks, Athens and Lyon, demonstrating its spatial and temporal transferability. Results show that bias correction significantly improves LDD speed accuracy, leading to more reliable NMFD estimation. This work advances data fusion methods for urban traffic monitoring and provides a robust framework for addressing LDD positional bias in large-scale traffic studies.