Freeway traffic estimation within particle filtering framework

Mihaylova, Lyudmila; Boel, René; Hegyi, Andreas

doi:10.1016/j.automatica.2006.08.023

Cited by 186 publications

(135 citation statements)

References 14 publications

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“…We can describe the evolution of queue-length x j (t) for example by a Stochastic Hybrid Automaton as shown in Figure 3, equivalent to equation (2). The event set that affects the evolution of queue x j (t) is } , , , , { also a mode dependent parameter to be identified).…”

Section: Problem Formulationmentioning

confidence: 99%

“…A great deal of recent work has considered traffic flow state estimation for freeway traffic and for arterial roads [1,2,3,4]. Our focus on urban networks requires different models and different estimation procedures, due to the stop and go phenomenon resulting from the green/red switching.…”

Section: Introductionmentioning

confidence: 99%

“…This stochastic hybrid models represents uncertainties in the evolution of the traffic flow in probabilistic form, for time scales of the order several red/green cycles of the traffic light. This corresponds to the prediction horizon that should be taken into account for a real-time model-prediction based feedback controller of the traffic lights in an urban network.A great deal of recent work has considered traffic flow state estimation for freeway traffic and for arterial roads [1,2,3,4]. Our focus on urban networks requires different models and different estimation procedures, due to the stop and go phenomenon resulting from the green/red switching.…”

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confidence: 99%

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Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

Sutarto

Boel

Joelianto

2015

IET Control Theory & Applications

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This paper proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described in this paper using a mode-dependent first order autoregressive (AR) stochastic process. The parameters of the AR-process take different values depending on the mode of traffic operation -free flowing, congested, or faulty -making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain, and hence we call this hybrid process a jump Markov process. This paper proposes an expectationmaximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied in this paper to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inferences algorithms and an online particle filter (PF). We also develop an EM parameter estimation that, in combination with a time window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator. The proposed parameter estimation technique can thus be used as part of an adaptive model-based filter for feedback control of traffic lights.Keywords: Hybrid system, parameter estimation, particle filter, stochastic system, fault detection, urban traffic networks. 2 IntroductionUrban traffic congestion is a problem that significantly affects many aspects of the quality of life. A more efficient use of the existing road infrastructure, using advanced traffic control strategies, can lead to reduced congestion, reduced emissions, reduced fuel consumption, and improved safety. The model based control strategies that are needed in order to achieve such an improvement depend strongly on the quality and the accuracy of the dynamic model of the system, on the availability of reliable online data and also on the ease of implementation of the control strategy. The model must describe the variability over time of the traffic flow, allowing model based estimation of the current state, fusing noisy traffic data from various traffic sensors. This estimation together with the model in turn allows probabilistic prediction of future traffic flows, so that control actions -selecting switching times of traffic lights in the application we have in mind -can properly anticipate future traffic flow. In this paper we use a stochastic hybrid model to effectively describe the evolution over time of the arrival and flow rates of vehicles in an urban traffic network as stochastic processes. We define the flow rate as the number of vehicles that pass a location per red/green cycle, divided by the length of this cycle (veh./sec). The random value of the continuous v...

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

Sutarto

Boel

Joelianto

2015

IET Control Theory & Applications

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“…Recently, in [10], the adaptive Kalman filtering approach proposed in [9] has been tailored to fitt h eC T M (inheriting the same pros and cons), and in [11] it has been compared with the unscented Kalman filter for joint and dual estimation. A particle filter is designed in [12] to estimate both speed and traffic density. The particle filter performs well with a small number of particles in light traffic conditions, but obtaining accurate estimates in the presence of severe congestion turns out to be computationally demanding.…”

Section: Introduction a Motivation And Related Workmentioning

confidence: 99%

A new robust approach for highway traffic density estimation

Morbidi

Ojeda

Canudas‐de‐Wit

et al. 2014

2014 European Control Conference (ECC)

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Abstract-In this paper we present a robust mode selector for the uncertain graph-constrained Switching Mode Model (SMM), which we use to describe the highway trafficd e n s i t y evolution. Assuming an uncertain speed of the congestion wave, the proposed selector relies on a transition digraph suitably incorporating the present and historical statistical traffici nformation, to determine the most probable current mode of the SMM. Its effectiveness is demonstrated on the problem of traffic density reconstruction via a switching observer, in an instrumented 2.2 km highway section of Grenoble south ring in France.

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“…Based on a second-order traffic flow model, linearization around the current state is required to determine the transferring flows between sections [1], [6]. A variant of the cell transmission model based on a second-order flow model and adopted an alternative particlefiltering framework to avoid linearization operations is developed in [10].…”

Section: Introductionmentioning

confidence: 99%