Dynamic representation of the fundamental diagram via Bayesian networks for estimating traffic flows from probe vehicle data

Neumann, Thorsten; Böhnke, Philipp; Tcheumadjeu, Louis Calvin Touko

doi:10.1109/itsc.2013.6728501

Cited by 20 publications

(18 citation statements)

References 7 publications

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“…Some data-driven methods have used aggregated GPS probe vehicle data. Essentially, they first extracted an FD or similar relations from historical stationary data, and then estimated traffic states from mobile data and the FD-like relation (e.g., Blandin et al, 2012b;Neumann et al, 2013). Wilby et al (2014) proposed a TSE method using xFCD with a similar approach.…”

Section: Mobile Datamentioning

confidence: 99%

“…Essentially, the parameters of TSE models (i.e., concepts similar to an FD) are estimated from historical stationary data, and then streaming mobile data are used for TSE. For example, speed (Anuar et al, 2015;Neumann et al, 2013), variance of speed (Blandin et al, 2012b;Bulteau et al, 2013), and other variables in xFCD (Wilby et al, 2014) are used for such estimation.…”

Section: Data-driven Approachesmentioning

confidence: 99%

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Traffic state estimation on highway: A comprehensive survey

Seo

Bayen

Kusakabe

et al. 2017

Annual Reviews in Control

326

174

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Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a topic which has gained great attention in the recent decades. We characterize existing TSE methods based on three fundamental elements: estimation approach, traffic flow model, and input data. Estimation approach encompasses methods that estimate the traffic state, based on partial observation and a priori knowledge (assumptions) on traffic dynamics. Estimation approaches can be roughly classified into three according to their dependency on a priori knowledge and empirical data: model-driven, data-driven, and streaming-data-driven. A traffic flow model usually means a physics-based mathematical model representing traffic dynamics, with various solution methods. Input data can be characterized by using three different properties: collection method (stationary or mobile), data representation (disaggregated or aggregated), and temporal condition (real-time or historical). Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.

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Section: Mobile Datamentioning

confidence: 99%

Section: Data-driven Approachesmentioning

confidence: 99%

Traffic state estimation on highway: A comprehensive survey

Seo

Bayen

Kusakabe

et al. 2017

Annual Reviews in Control

326

174

View full text Add to dashboard Cite

show abstract

“…When using the fundamental diagram to predict how traffic states propagate (both in time and in space), the estimation should be based on spatial measurements (in accordance to Edie's definition of equilibrium). As detectors only collect data at a single cross-section, probe vehicles can be used (Neumann, Bohnke, and Touko Tcheumadjeu 2013). Chiabaut, Buisson, and Leclercq (2009) use the passing rate to estimate a fundamental diagram, as this is independent of the traffic flow state.…”

Section: Fitting a Fundamental Diagram To Datamentioning

confidence: 99%

Automatic fitting procedure for the fundamental diagram

Knoop

Daamen

2016

Transportmetrica B: Transport Dynamics

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The fundamental diagram of a road, including free-flow capacity and queue discharge rate, is very important for traffic engineering purposes. In the real word, most traffic measurements come from stationary loop detectors. This paper proposes a method to fit Wu's fundamental diagram to loop detector data. Wu's fundamental diagram is characterised by five parameters, being free-flow speed, wave speed, free-flow capacity, queue discharge rate and jam density. The proposed method entails fixing the wave speed and the free-flow speed. The method consists of two steps. We first use a triangular fundamental diagram to separate the congested branch from the free-flow branch. Then, the remaining three parameters of Wu's fundamental diagram are fitted on each branch using a least-square fit. This method is shown to be robust for cases tested in real life, and hence very noisy, data. ARTICLE HISTORY

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“…This method estimates a conditional distribution on the observed data by integrating prior knowledge. In traffic engineering, Bayesian methods are widely used to estimate capacity (Ozguven and Ozbay, 2008), travel time (Jintanakul et al, 2009;Fei et al, 2011;Hofleitner et al, 2012), or traffic state (Neumann et al, 2013;Kim and Wang, 2016). Since traffic exhibits recurrent daily patterns, past traffic information can complement limited real-time data from CAVs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Traffic Flow Rate With Data From Connected-Automated Vehicles Using Bayesian Inference and Deep Learning

Han

Ahn

2021

Front. Future Transp.

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Connected automated vehicles (CAVs) hold promise to replace current traffic detection systems in the near future. However, traffic state estimation, particularly flow rate, poses a major challenge at low CAV penetration rates without other supporting infrastructure of sensors. This paper proposes flow rate estimation methods using headway data from CAVs. Specifically, Bayesian inference and deep learning based methods are developed and compared with a naïve method based on a simple arithmetic mean of observed headways. The proposed methods are investigated via numerical experiments to evaluate their performance with respect to the CAV penetration rate, traffic demand, and availability of historical data. The methods are further validated with real data. The results show that the Bayesian inference based method, which estimates the flow rate distribution by integrating current (real-time) data and previous knowledge, can perform well even at low penetration rates with good prior information. However, in high CAV penetration, its relative advantage to the other methods diminishes because the prior information always influences the flow rate estimation. The deep learning based method can be effective with a large amount of data to train the model; however, in low CAV penetration, it tends to converge to the mean of target output values regardless of the observed data. At last, in relatively high CAV penetration, the relative advantage of the advanced methods is negligible and in fact, the naïve method is preferred in terms of accuracy as well as efficiency.

show abstract

Dynamic representation of the fundamental diagram via Bayesian networks for estimating traffic flows from probe vehicle data

Cited by 20 publications

References 7 publications

Traffic state estimation on highway: A comprehensive survey

Traffic state estimation on highway: A comprehensive survey

Automatic fitting procedure for the fundamental diagram

Estimation of Traffic Flow Rate With Data From Connected-Automated Vehicles Using Bayesian Inference and Deep Learning

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