An adaptive freeway traffic state estimator

Wang, Yibing; Papageorgiou, Markos; Meßmer, Albert; Coppola, Pierluigi; Tzimitsi, Athina; Nuzzolo, Agostino

doi:10.1016/j.automatica.2008.05.019

Cited by 96 publications

(81 citation statements)

References 23 publications

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“…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: Introductionmentioning

confidence: 99%

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confidence: 99%

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

Sutarto

Boel

Joelianto

2015

IET Control Theory & Applications

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“…One major innovative feature of this recent work is online model parameter estimation, i.e., real-time joint estimation of traffic flow variables and some key parameters of the traffic flow model employed by the designed traffic state estimator [1], [2], [9]. It is demonstrated in [13], [15], and [16] that online model parameter estimation leads to some significant adaptive capabilities of the designed traffic state estimator. This state estimator has been incorporated into REANISSANCE as one of its major functional modules and has been tested for freeway stretches in simulation [9] and using real data [13], [15], [16] as well as for a freeway network in simulation [1].…”

Section: Introductionmentioning

confidence: 99%

“…The study of freeway traffic state estimation dates back to the early 1970s [3], [4] and has attracted considerable attention in the last 25 years [1]- [16]; see [9] for a concise review and [15] for some further remarks. Following the EKF avenue pursued in most previous work, one general approach to the design of traffic state estimators has recently been developed in [9].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Freeway Network Traffic Surveillance: Large-Scale Field-Testing Results in Southern Italy

Wang

Coppola

Tzimitsi

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper reports on some large-scale field-testing results of a real-time freeway network traffic surveillance tool that has recently been developed to enable a number of real-time traffic surveillance tasks. This paper first introduces the related network traffic flow model and the approaches employed to traffic state estimation, traffic state prediction, and incident alarm. The field testing of the tool for these surveillance tasks in the A3 freeway of 100 km between Naples and Salerno in southern Italy is then reported in some detail. The results obtained are quite satisfactory and promising for further future implementations of the tool.

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“…If the sensor density is sufficiently high (e.g., every 500 m), then the collected measurements are usually sufficient for traffic surveillance and control; else, appropriate estimation schemes need to be employed in order to produce traffic state estimates at the required space resolution (typically 500 m); see, for instance, [1], [13], [15], [16], [33], among many other works addressing highway traffic estimation by use of conventional detector data. However, the implementation and maintenance of road-side detectors entail considerable cost; hence various research works attempt to exploit different, less costly data sources, such as mobile phone, or GPS (Global Positioning System), or even vehicle speed data for travel time or highway state estimation; see, e.g., [3], [7], [8], [11], [12], [18], [20], [26], [28], [34], [36]; employing various kinds of traffic or statistic models.…”

Section: Introductionmentioning

confidence: 99%

Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

Bekiaris-Liberis

Roncoli

Papageorgiou

2016

IEEE Trans. Intell. Transport. Syst.

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Abstract-A macroscopic model-based approach for estimation of the traffic state, specifically of the (total) density and flow of vehicles, is developed for the case of "mixed" traffic, i.e., traffic comprising both ordinary and connected vehicles. The development relies on the following realistic assumptions: (i) The density and flow of connected vehicles are known at the (local or central) traffic monitoring and control unit on the basis of their regularly reported positions; and (ii) the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by merely estimating the percentage of connected vehicles with respect to the total number of vehicles. A model is derived, which describes the dynamics of the percentage of connected vehicles, utilizing only well-known conservation law equations that describe the dynamics of the density of connected vehicles and of the total density of all vehicles. Based on this model, which is a linear time-varying system, an estimation algorithm for the percentage of connected vehicles is developed employing a Kalman filter. The estimation methodology is validated through simulations using a secondorder macroscopic traffic flow model as ground truth for the traffic state. The approach calls for a minimum of spot sensorbased total flow measurements according to a variety of possible location configurations.

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An adaptive freeway traffic state estimator

Cited by 96 publications

References 23 publications

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

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

Real-Time Freeway Network Traffic Surveillance: Large-Scale Field-Testing Results in Southern Italy

Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

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