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  • 1.
    Allström, Andreas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Highway Traffic State Estimation and Short-term Prediction2016Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.

    The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.

    In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

  • 2.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Archer, Jeffery
    Sweco Infrastructure, Sweden.
    Bayen, Alexandre
    University of California, Berkeley, USA.
    Blandin, Sebastian
    University of California, Berkeley, USA.
    Butler, Joe
    California Center for Innovative Transportation. USA.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Koutsopoulose, Haris N
    Royal Institute of Technology (KTH), Sweden.
    Lundgren, Jan
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rahmani, Mahmood
    Royal Institute of Technology (KTH), Sweden.
    Tossavainen, Olli-Pekka
    NAVTEQ LLC, USA.
    Mobile Millennium Stockholm2011In: 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, 2011Conference paper (Other academic)
  • 3.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology. Sweco TransportSystem, Stockholm, Sweden.
    Bayen, Alexandre M.
    University of California Berkeley, USA.
    Fransson, Magnus
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology. Sweco TransportSystem, Stockholm, Sweden.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Patire, Anthony D.
    University of California Berkeley, USA.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Sandin, Mats
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Calibration Framework based on Bluetooth Sensors for Traffic State Estimation Using a Velocity based Cell Transmission Model2014In: Transportation Research Procedia, ISSN 2352-1465, Vol. 3, p. 972-981Article in journal (Refereed)
    Abstract [en]

    The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.

  • 4.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    Department of Civil and Environmental Engineering, University of California.
    Patire, Anthony D.
    Department of Civil and Environmental Engineering, University of California.
    A hybrid approach for short-term traffic state and travel time prediction on highways2016In: TRB 95th annual meeting compendium of papers, 2016Conference paper (Refereed)
    Abstract [en]

    Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes ahead in time to allow the journey to be completed.

  • 5.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
    Patire, Anthony D.
    Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
    Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways2016In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, Vol. 2554, p. 60-68Article in journal (Refereed)
    Abstract [en]

    Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed

  • 6.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Evaluation of travel time estimation based on LWR-v and CTM-v: A case study in Stockholm2012In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012, Piscataway, N.J, USA: IEEE , 2012, p. 1644-1649Conference paper (Refereed)
    Abstract [en]

    Real-time estimations of current and future traffic states are an essential part of traffic management and traffic information systems. Within the Mobile Millennium project considerable effort has been invested in the research and development of a real-time estimation system that can fuse several sources of data collected in California. During the past year this system has been adapted to also handle traffic data collected in Stockholm. This paper provides an overview of the model used for highways and presents results from an initial evaluation of the system. As part of the evaluation process, GPS data collected in an earlier field-test and estimations generated by the existing system used by the TMC in Stockholm, are compared with the estimations generated by the Mobile Millennium system. Given that the Mobile Millennium Stockholm system has not undergone any calibration, the results from the evaluation are considered promising. The estimated travel times correspond well to those measured in the field test. Furthermore, the estimations generated by the Mobile Millennium system can be regarded as superior to those of existing traffic management system in Stockholm. The highway model was found to perform well even with a reduction in the number of sensors providing data. The findings of this study indicate the robustness of the Mobile Millennium system and demonstrate how the system can be migrated to other geographical areas with similar sources of available data.

  • 7.
    Bergman, Astrid
    et al.
    Trivector Traffic, Lund, Sweden.
    Olstam, Johan
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Allström, Andreas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Analytical traffic models for roundabouts with pedestrian crossings2011Conference paper (Refereed)
    Abstract [en]

    Roundabouts have become a more common type of intersection in Sweden over the last 30 years. In order to evaluate the roundabout level-of-service both analytical models and simulation models are being used. Analytical traffic models for intersections, such as the Swedish capacity model Capcal, has difficulties estimating the level-of-service of a roundabout if there are pedestrians and cyclists at crossings located close to the roundabout. It is well known that a crossing located after a roundabout exit can cause an up-stream blocking effect that affects the performance of the roundabout. But how the upstream blocking effect depends on the different flows of vehicles and pedestrians is not known. In this paper an existing analytical model by Rodegerdts and Blackwelder has been investigated and compared to simulations in VISSIM and measurements from Swedish roundabouts. The purpose of this investigation is to examine if the model by Rodegerdts and Blackwelder is suitable for implementing into existing analytical models such as Capcal. The results show that the model by Rodegerdts and Blackwelder can estimate if a capacity loss will occur, but the magnitude of this loss is more difficult to evaluate. The conclusion and recommendation is that the model by Rodegerdts and Blackwelder should be implemented into the Swedish capacity model Capcal. The model by Rodegerdts and Blackwelder is to be used as a warning system if the results in Capcal are too uncertain to use for analysis of the roundabout performance.

  • 8.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Allström, Andreas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bergfeldt, Erik
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    University of California, Berkeley, USA.
    Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering2015In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE conference proceedings, 2015, p. 2121-2128Conference paper (Refereed)
    Abstract [en]

    The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

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