Traffic State Estimation Using Stochastic Lagrangian Dynamics

Zheng, Fangfang; Jabari, Saif Eddin; Liu, Henry X.; Lin, DianChao; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)

doi:10.1016/j.trb.2018.07.004

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Traffic State Estimation Using Stochastic Lagrangian Dynamics

2018-09-01
By Zheng, Fangfang ; Jabari, Saif Eddin ; Liu, Henry X. ; ...

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Creators:

Zheng, Fangfang ; Jabari, Saif Eddin ; Liu, Henry X. ; Lin, DianChao

Zheng, Fangfang ; Jabari, Saif Eddin ; Liu, Henry X. ; Lin, DianChao Less -
Corporate Creators:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Corporate Contributors:

University Transportation Centers Program (U.S.)
Subject/TRT Terms:

[+]

Car Following Data Assimilation Heterogeneous Drivers Hydrodynamic Limits Kalman Filtering Lagrangian Coordinates Lagrangian Functions Mean (Statistics) Mean Dynamics Traffic Estimation Traffic State Estimation Uncertainty Quantification Variability
DOI:

https://doi.org/10.1016/j.trb.2018.07.004
Resource Type:

Manuscript
Geographical Coverage:

United States
Edition:

Final manuscript prior to publication Transportation Research Part B: Methodological, Volume 115, September 2018
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Abstract:

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.
Content Notes:

NOTICE: this is the authors version of a work that was accepted for publication in Transportation Research Part B. A definitive version was subsequently published in Transportation Research Part B, vol.115, pp 143– 165 (2018). https://doi.org/10.1016/j.trb.2018.07.004
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PDF
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University Transportation Centers US Transportation Collection
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urn:sha256:4acfbf2b5a307567777fae3f50c696b303121bc976f7cf11e6cc33b35af6e229
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https://rosap.ntl.bts.gov/view/dot/40271/dot_40271_DS1.pdf
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Traffic State Estimation Using Stochastic Lagrangian Dynamics

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