| 1. |
- Allström, Andreas, 1978-, et al.
(författare)
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A hybrid approach for short-term traffic state and travel time prediction on highways
- 2016
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Ingår i: TRB 95th annual meeting compendium of papers.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Allström, Andreas, 1978-, et al.
(författare)
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Evaluation of travel time estimation based on LWR-v and CTM-v : A case study in Stockholm
- 2012
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Ingår i: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012. - Piscataway, N.J, USA : IEEE. - 9781467330640 - 9781467330626 ; , s. 1644-1649
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Allström, Andreas, 1978-, et al.
(författare)
-
Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
- 2016
-
Ingår i: Transportation Research Record. - Washington, DC, USA : The National Academies of Sciences, Engineering, and Medicine. - 0361-1981 .- 2169-4052. ; 2554, s. 60-68
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 4. |
- Allström, Andreas, 1978-, et al.
(författare)
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Mobile Millennium Stockholm
- 2011
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Ingår i: 2nd International Conference on Models and Technologies for Intelligent Transportation Systems.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Gundlegård, David, 1978-, et al.
(författare)
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Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering
- 2015
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Ingår i: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. - : IEEE conference proceedings. - 9781467365956 ; , s. 2121-2128
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Konferensbidrag (refereegranskat)abstract
- 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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