Bayesian Traffic State Estimation Using Extended Floating Car Data

Englezou, Yiolanda; Victor, Kyriacou; Christos, Panayiotou G.; Timotheou, Stelios

doi:10.1109/tits.2022.3225057

Cited by 6 publications

(4 citation statements)

References 66 publications

(88 reference statements)

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“…Recently, more and more researchers investigated the trajectory-based TSE methods from the data. For example, utilized the trajectory data of probe vehicles or connected-automated vehicles (CAVs) to estimate the traffic state of freeways Wang et al [6], Seo and Kusakabe [33], Seo et al [35], Kyriacou et al [36]. Moreover, there is a trend that combines model-based and data-driven models to develop "physics-informed" machine learning models for TSE [27,28,37].…”

Section: Related Workmentioning

confidence: 99%

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Traffic State Estimation with Anisotropic Gaussian Processes from Vehicle Trajectories

Wang¹,

Cheng²,

Chen³

et al. 2023

Preprint

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Accurately monitoring road traffic state and speed is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the propagation of traffic waves in traffic flow data. This method can be applied to impute traffic state data from fixed sensors or probe vehicles. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. Results show that our method outperforms state-ofthe-art methods in terms of estimation accuracy, efficiency, and robustness.

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Section: Related Workmentioning

confidence: 99%

“…At the same time, Bekiaris-Liberis et al [40] developed a model-based TSE approach for per-lane density estimation and an on-ramp and off-ramp flow estimation in the presence of connected vehicles. There have also been works discussed online and offline TSE with CV data in a Bayesian model [36].…”

Section: Related Workmentioning

confidence: 99%

Traffic State Estimation with Anisotropic Gaussian Processes from Vehicle Trajectories

Wang¹,

Cheng²,

Chen³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…MCMC methods construct a Markov chain with steady state distribution equal to the posterior density of interest. A widely used MCMC algorithm that is relatively simple, is the Metropolis-Hastings algorithm [19] and it will be used for the purposes of this work (see [20] for more information).…”

Section: A Statistical Model For Calibrationmentioning

confidence: 99%

A Probabilistic Approach for the Calibration of Incomplete Microscopic Traffic Models

Englezou,

Timotheou,

Panayiotou

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Digital Twins (DTs) are steadily gaining popularity for the study of large systems in real time. In order to build an efficient and reliable DT, it is crucial to perform calibration prior to its use, that is, to use real data to estimate unknown parameters in the DT, that are of great importance for the actual physical process. This work studies the calibration of the Intelligent Driver Model (IDM) to infer driver behaviour, a crucial task when building a DT of the traffic network. We introduce a statistical model to calibration which takes into account the model uncertainty, while also taking into account possible correlations between individual vehicles that have similar characteristics. In contrast with other works in the literature, we assume that vehicles belong to a prespecified group and develop a Bayesian approach to derive the posterior distributions of the parameters that characterise each group's behaviour. We apply the proposed approach to the IDM and derive probability density functions of the unknown model parameters and the model uncertainty. The proposed probabilistic approach is validated using realistic SUMO micro-simulations of a highway stretch. We present estimation results that show that the proposed approach can accurately derive the posterior distributions of the calibration parameters. In addition, we compare the proposed approach with a literature methodology and show that our approach higher quality posterior distributions of the parameters of interest than the literature approach.

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“…Traffic monitoring has been one of the major tools used for transportation planning, decision-making and the implementation of various control strategies [1]. Traffic monitoring tasks include traffic state estimation (TSE) [2], origindestination (OD) matrix estimation [3], travel-time [4], and queue estimation [5] and traffic state and demand prediction [6]. Traffic counts, i.e.…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

Sofokleous,

Englezou,

Savva

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under reallife conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.

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Bayesian Traffic State Estimation Using Extended Floating Car Data

Cited by 6 publications

References 66 publications

Traffic State Estimation with Anisotropic Gaussian Processes from Vehicle Trajectories

Traffic State Estimation with Anisotropic Gaussian Processes from Vehicle Trajectories

A Probabilistic Approach for the Calibration of Incomplete Microscopic Traffic Models

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

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