An Extended Kalman Filter Application for Traffic State Estimation Using CTM with Implicit Mode Switching and Dynamic Parameters

Tampère, Chris; Immers, L H

doi:10.1109/itsc.2007.4357755

Cited by 76 publications

(48 citation statements)

References 4 publications

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“…In comparison to other higher order traffic flow models, CTM has lesser number of output variables and input parameters which qualifies CTM as a suitable model for real-time applications. CTM has been used for traffic state estimation (Munoz et al 2003;Munoz et al 2006;Gang, Jiang, and Cai 2007;Tampere andImmers 2007, Long et al 2008;Long et al 2011) as well as for DTA applications of traffic network optimization (Lo 1999, Ziliaskopoulos 2000, Lo 2001, Gomes and Horowitz 2006, Liu, Lai and Gang 2006, Chiu et al 2007). …”

Section: Methodsmentioning

confidence: 99%

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Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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Prediction of traveller information and route choice based on real-time estimated traffic stateAccurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. In this paper we propose a framework which utilizes real-time traffic state estimate to optimize network performance during an incident through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended KalmanFilter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.

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Section: Methodsmentioning

confidence: 99%

“…Recently, many research studies have focused on traffic state estimation problem (Wang et al 2011;Munoz et al 2003 ;Munoz et al 2006;Tampere and Immers 2007;Sun, Munoz and Horowitz 2004, Ngoduy 2008, Ngoduy 2011a. Of particular relevance to the present paper is the work of Wang and Papageorgiou (2005).…”

Section: Introductionmentioning

confidence: 94%

Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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“…Notice that the traffic density dynamics in (18) are piecewise linear due to the presence of the min operators in parameters q out t and q in t . To deal with this issue we adopt the implicit mode switching approach [4], [5] which considers the estimationρ t , as an indication of the active segments for q In the presence of faulty sensors a more appropriate measurement model is…”

Section: Fault-tolerant Traffic State Estimationmentioning

confidence: 99%

“…In the context of the Asymmetric Cell Transmission Model (ACTM), TSE has also been examined from a Kalman filtering perspective in [4], [5]. These works develop implicit mode switching methods to alternate between cell congestion modes and apply Kalman filtering in the resulting linear timevariant system.…”

Section: Introductionmentioning

confidence: 99%

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Timotheou¹,

Panayiotou²,

Polycarpou³

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Traffic state estimation is an important problem with significant applications in advanced traveler information systems, transportation management and traffic control. Nonetheless, the often faulty nature of measurement sensors, especially inductive loop detectors, hinders reliable state estimation. This work proposes a systematic, model-based, networkwide, moving-horizon approach for fault-tolerant traffic state estimation. By exploiting information redundancy and fault sparsity, it achieves reliable estimation and simultaneously detects, isolates and corrects measurements from periods of faulty sensor behavior. The approach is examined in relation to the Asymmetric Cell Transmission Model, a popular and powerful macroscopic first-order traffic flow model. In the absence of any faults, the proposed approach achieves similar results with other state-of-the-art estimation approaches while it can achieve better estimation performance when some sensors are faulty. It is further demonstrated that the developed approach can successfully handle multiple faults of different types.

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“…Given a set of measurements, also measures like link and route travel times or flows can be estimated with any traffic flow estimation model. Many traffic flow models are already able to estimate the uncertainty of these estimates based on the available measurements (via, e.g., state-space models with Kalman Filtering ( [5], [6] and [14]), and therefore define a solution space whereupon the real traffic states lie. For sake of simplicity we formulate the new MPRE by using travel times as target values.…”

Section: Link Travel Time-based Maximum Possible Relative Errormentioning

confidence: 99%

Sensor Locations for Reliable Travel Time Prediction and Dynamic Management of Traffic Networks

Viti

Verbeke

Tampère

2008

Transportation Research Record: Journal of the Transportation R

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Accurate state estimation and prediction models strongly rely on the quality and the dissemination of the available traffic data. In complex networks, such as large urban areas, the position of traffic counts (i.e. loop detectors) is of critical importance for such models. Nevertheless, sensor locations are typically thought for different objectives, e.g., for monitoring bottleneck sections, or to obtain optimal information on the trip-generation matrix. As result, link state estimations and predictions are often inaccurate, and sometimes large blank areas in monitoring traffic are observed, which may reflect into bad prediction, sometimes even on the monitored links.This paper proposes a new approach to the sensor location problem, which has the objective of optimizing the position of traffic counts for reliable travel time estimation and prediction at complex road networks. To do so, we reformulate the Maximum Possible Relative Error, used as objective function in classical approaches, to minimize the error in link traffic states. By formulating the problem at the link level we can obtain more reliable predictions of partial as well as whole network travel times.The solution of the new MPRE would require two extra inputs with respect to the classical approach: 1) the specification of a network travel time or a state estimation model and 2) the specification of the network traffic variability, in order to set the boundaries of the solution space. For large complex networks the latter information might be very difficult to be obtained.As alternative, we propose a simple solution algorithm to the problem that uses the link flow and travel time correlations between links to select, in sequence, the most representative links in the network. We tested the algorithm on a toy network showing that it succeeds in catching a substantially larger percentage of link flows than a classical approach. 3 SENSOR LOCATIONS FOR RELIABLE TRAVEL TIME PREDICTION AND DYNAMIC MANAGEMENT OF TRAFFIC NETWORKS FOREWORDReliable estimation and prediction of travel times at road networks strongly depends on the quality of information available on the current, as much as on the expectations of future, traffic conditions. A very popular way to obtain this type of information is through traffic counts collected from automatic detection points (e.g., loop detectors). These sensors provide, with some degree of precision, average traffic intensities and speeds within fixed time periods and at specific locations in the network. This information is therefore typically fragmented in time and space. First, not all parts of the network are monitored and often blank areas in the data are completed with interpolated data. Second, traffic counts are sent to the data users (e.g., road authorities, information service providers) after they are aggregated into fixed time intervals, and therefore they may lose information due to averaging (e.g., speeds are usually collected as mean values). Considering that traffic counts contain always a degree of er...

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An Extended Kalman Filter Application for Traffic State Estimation Using CTM with Implicit Mode Switching and Dynamic Parameters

Cited by 76 publications

References 4 publications

Prediction of traveller information and route choice based on real-time estimated traffic state

Prediction of traveller information and route choice based on real-time estimated traffic state

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Sensor Locations for Reliable Travel Time Prediction and Dynamic Management of Traffic Networks

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