IDEAS home Printed from https://ideas.repec.org/r/eee/transb/v39y2005i2p141-167.html
   My bibliography  Save this item

Real-time freeway traffic state estimation based on extended Kalman filter: a general approach

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Florin, Ryan & Olariu, Stephan, 2020. "Towards real-time density estimation using vehicle-to-vehicle communications," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 435-456.
  2. Sun, Zhe & Jin, Wen-Long & Ritchie, Stephen G., 2017. "Simultaneous estimation of states and parameters in Newell’s simplified kinematic wave model with Eulerian and Lagrangian traffic data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 106-122.
  3. Siqueira, Adriano F. & Peixoto, Carlos J.T. & Wu, Chen & Qian, Wei-Liang, 2016. "Effect of stochastic transition in the fundamental diagram of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 1-13.
  4. Duret, Aurélien & Yuan, Yufei, 2017. "Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 51-71.
  5. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
  6. Ernesto Cipriani & Lorenzo Giannantoni & Livia Mannini, 2023. "Integrated Variable Speed Limits and User Information Strategy," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
  7. Rodriguez-Vega, Martin & Canudas-de-Wit, Carlos & Fourati, Hassen, 2021. "Average density estimation for urban traffic networks: Application to the Grenoble network," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 21-43.
  8. Ngoduy, D., 2008. "Applicable filtering framework for online multiclass freeway network estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 599-616.
  9. Nantes, Alfredo & Ngoduy, Dong & Miska, Marc & Chung, Edward, 2015. "Probabilistic travel time progression and its application to automatic vehicle identification data," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 131-145.
  10. Huang, Hai-Jun & Xia, Tian & Tian, Qiong & Liu, Tian-Liang & Wang, Chenlan & Li, Daqing, 2020. "Transportation issues in developing China's urban agglomerations," Transport Policy, Elsevier, vol. 85(C), pages 1-22.
  11. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2016. "Time Series Based Hourly Traffic Flow Prediction on the GTA Freeways Using TSTVEC Model," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319287, Transportation Research Forum.
  12. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
  13. Wang, Chun & Zhang, Weihua & Wu, Cong & Hu, Heng & Ding, Heng & Zhu, Wenjia, 2022. "A traffic state recognition model based on feature map and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
  14. Jingqiu Guo & Xinyao Chen & Yuqi Pang & Yibing Wang & Pengjun Zheng, 2019. "Bottlenecks, Shockwave, and Off-Ramp Blockage on Freeways," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
  15. Nicholas Molyneaux & Riccardo Scarinci & Michel Bierlaire, 2021. "Design and analysis of control strategies for pedestrian flows," Transportation, Springer, vol. 48(4), pages 1767-1807, August.
  16. Panda, Manoj & Ngoduy, Dong & Vu, Hai L., 2019. "Multiple model stochastic filtering for traffic density estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 280-306.
  17. Nicholas Molyneaux & Riccardo Scarinci & Michel Bierlaire, 0. "Design and analysis of control strategies for pedestrian flows," Transportation, Springer, vol. 0, pages 1-41.
  18. Čičić, Mladen & Johansson, Karl Henrik, 2022. "Front-tracking transition system model for traffic state reconstruction, model learning, and control with application to stop-and-go wave dissipation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 212-236.
  19. Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
  20. Blandin, Sébastien & Argote, Juan & Bayen, Alexandre M. & Work, Daniel B., 2013. "Phase transition model of non-stationary traffic flow: Definition, properties and solution method," Transportation Research Part B: Methodological, Elsevier, vol. 52(C), pages 31-55.
  21. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
  22. Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
  23. Zheng, Zuduo & Su, Dongcai, 2016. "Traffic state estimation through compressed sensing and Markov random field," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 525-554.
  24. Hilmi Berk Celikoglu & Mehmet Ali Silgu, 2016. "Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering," Transportation Science, INFORMS, vol. 50(3), pages 966-981, August.
  25. Pereira, Mike & Boyraz Baykas, Pinar & Kulcsár, Balázs & Lang, Annika, 2022. "Parameter and density estimation from real-world traffic data: A kinetic compartmental approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 210-239.
  26. Cheng, Zeyang & Wang, Wei & Lu, Jian & Xing, Xue, 2020. "Classifying the traffic state of urban expressways: A machine-learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 411-428.
  27. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
  28. Zhao, Mingming & Yu, Hongxin & Wang, Yibing & Song, Bin & Xu, Liang & Zhu, Dianchen, 2024. "Real-time freeway traffic state estimation for inhomogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
  29. van Erp, Paul B.C. & Knoop, Victor L. & Hoogendoorn, Serge P., 2018. "Macroscopic traffic state estimation using relative flows from stationary and moving observers," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 281-299.
  30. Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
  31. Yuan, Yun & Zhang, Zhao & Yang, Xianfeng Terry & Zhe, Shandian, 2021. "Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 88-110.
  32. Jabari, Saif Eddin & Liu, Henry X., 2013. "A stochastic model of traffic flow: Gaussian approximation and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 15-41.
  33. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
  34. Yibing Wang & Markos Papageorgiou & Albert Messmer, 2007. "Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study," Transportation Science, INFORMS, vol. 41(2), pages 167-181, May.
  35. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
  36. Bakibillah, A.S.M. & Tan, Yong Hwa & Loo, Junn Yong & Tan, Chee Pin & Kamal, M.A.S. & Pu, Ziyuan, 2022. "Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter," Applied Mathematics and Computation, Elsevier, vol. 421(C).
  37. Qian, Wei-Liang & F. Siqueira, Adriano & F. Machado, Romuel & Lin, Kai & Grant, Ted W., 2017. "Dynamical capacity drop in a nonlinear stochastic traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 328-339.
  38. Zhang, Jie & Song, Chunyue & Cao, Shan & Zhang, Chun, 2023. "FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
  39. Bouadi, Marouane & Jia, Bin & Jiang, Rui & Li, Xingang & Gao, Zi-You, 2022. "Stability analysis of stochastic second-order macroscopic continuum models and numerical simulations," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 193-209.
  40. Pan, Yingjiu & Chen, Shuyan & Niu, Shifeng & Ma, Yongfeng & Tang, Kun, 2020. "Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity," Journal of Transport Geography, Elsevier, vol. 83(C).
  41. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.