Learning-based Traffic State Reconstruction using Probe Vehicles

Liu, John; Barreau, Matthieu; Čičić, Mladen; Johansson, Karl Henrik

doi:10.1016/j.ifacol.2021.06.013

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…The Greenshields model is a limit case of the first order follow-the-leader dynamics [20] and the integral of the normalized density is proportional to the number of vehicles. Consequently, ρ is directly related to the intravehicular space and one can use the velocity function v of the macroscopic scheme as the vehicle velocity V [13]. That leads to the following cascaded system:…”

Section: Relation Between the Modelsmentioning

confidence: 99%

“…Remark 4: Compared to the authors' previous work [13], we need to measure the instantaneous speed since we do not make any assumptions on the velocity-density relation.…”

Section: Measurementsmentioning

confidence: 99%

“…The use of probe vehicles in particular is studied in [12]. The application to traffic flow reconstruction with data coming from external simulators is considered in [13].…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution is the development of a machine learning framework for joint identification and state estimation of traffic flow from sparse measurements taken by probe vehicles. In comparison with our previous papers [12], [13], which focused on state estimation, here we consider in addition the problem of identifying the velocity function. Such a difference implies an explosion of the number of variables and physical costs.…”

Section: Introductionmentioning

confidence: 99%

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Physics-informed Learning for Identification and State Reconstruction of Traffic Density

Barreau¹,

Aguiar²,

Liu³

et al. 2021

Preprint

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This paper deals with traffic density reconstruction using measurements from Probe Vehicles (PVs). The main difficulty arises when considering a low penetration rate, meaning that the number of PVs is small compared to the total number of vehicles on the road. Moreover, the formulation assumes noisy measurements and a partially unknown firstorder model. All these considerations make the use of machine learning to reconstruct the state the only applicable solution. We first investigate how the identification and reconstruction processes can be merged and how a sparse dataset can still enable a good identification. Secondly, we propose a pre-training procedure that helps the hyperparameter tuning, preventing the gradient descent algorithm from getting stuck at saddle points. Examples using numerical simulations and the SUMO traffic simulator show that the reconstructions are close to the real density in all cases.

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Section: Relation Between the Modelsmentioning

confidence: 99%

“…Remark 4: Compared to the authors' previous work [13], we need to measure the instantaneous speed since we do not make any assumptions on the velocity-density relation.…”

Section: Measurementsmentioning

confidence: 99%

“…The use of probe vehicles in particular is studied in [12]. The application to traffic flow reconstruction with data coming from external simulators is considered in [13].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physics-informed Learning for Identification and State Reconstruction of Traffic Density

Barreau¹,

Aguiar²,

Liu³

et al. 2021

Preprint

Self Cite

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“…The idea is to assess the computed traffic state of a neural network not only based on measurement data, but also on its compliance with the given physical model. Recent approaches include Huang and Agarwal (2020), who publish an approach giving promising insights into the ability to reconstruct traffic densities based on sparse measurements; Liu et al (2020), who use simulated trajectory data providing local densities, study an approach to reconstruct the traffic density in space and time and Shi et al (2021), who design a PINN with additional error term based on the LWR and a Greenshields FD in order to estimate traffic density on a road. These approaches show promising results, though, published network architectures are not applicable to the problem of traffic speed estimation with probe data: Some approach require that probe vehicles provide density information Liu et al (2020), which is rarely the case.…”

Section: Related Workmentioning

confidence: 99%

Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach

Rempe,

Franeck,

Bogenberger

2021

Preprint

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This paper presents a dedicated Deep Neural Network (DNN) architecture that reconstructs space-time traffic speeds on freeways given sparse data. The DNN is constructed in such a way, that it learns heterogeneous congestion patterns using a large dataset of sparse speed data, in particular from probe vehicles. Input to the DNN are two equally sized input matrices: one containing raw measurement data, and the other indicates the cells occupied with data. The DNN, comprising multiple stacked convolutional layers with an encoding-decoding structure and feed-forward paths, transforms the input into a full matrix of traffic speeds.The proposed DNN architecture is evaluated with respect to its ability to accurately reconstruct heterogeneous congestion patterns under varying input data sparsity. Therefore, a large set of empirical Floating-Car Data (FCD) collected on German freeway A9 during two months is utilized. In total, 43 congestion distinct scenarios are observed which comprise moving and stationary congestion patterns. A data augmentation technique is applied to generate input-output samples of the data, which makes the DNN shift-invariant as well as capable of managing varying data sparsities. The DNN is trained and subsequently applied to sparse data of an unseen congestion scenario. The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy; even given highly sparse input data. Reconstructed speeds are compared qualitatively and quantitatively with the results of several state-of-the-art methods such as the Adaptive Smoothing Method (ASM), the Phase-Based Smoothing Method (PSM) and a standard Convolutional Neural Network (CNN) architecture. As a result, the DNN outperforms the other methods significantly.

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