Clas Rydergren
Linköping University
51 Papers
167 Citations
Clas Rydergren is an academic researcher from Linköping University. The author has contributed to research in topics: Computer science & Congestion pricing. The author has an hindex of 11, co-authored 41 publications.
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Papers
Travel demand estimation and network assignment based on cellular network data
TL;DR: A tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones is proposed.
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A column generation procedure for the side constrained traffic equilibrium problem
TL;DR: A column generation procedure for the side constrained traffic equilibrium problem is presented and a dual stabilization scheme is introduced to improve the computational performance.
Heuristic algorithms for a second-best congestion pricing problem
TL;DR: In this article, the authors developed and evaluated methods for finding the most efficient design for a congestion pricing scheme in a road network model with elastic demand, where the design efficiency is measured by the net social surplus, which is computed as the difference between the social surplus and the collection costs of the congestion pricing system.
34
A hybrid approach for short-term traffic state and travel time prediction on highways
Andreas Allström,Joakim Ekström,David Gundlegård,Rasmus Ringdahl,Clas Rydergren,Alexandre M. Bayen,Anthony D. Patire +6 more
- 01 Jan 2016
TL;DR: In this article, a hybrid approach combining parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter is proposed, which can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future.
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Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
Andreas Allström,Joakim Ekström,David Gundlegård,Rasmus Ringdahl,Clas Rydergren,Alexandre M. Bayen,Anthony D. Patire +6 more
TL;DR: 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.