Vehicle Trajectory Reconstruction Using Conditional Random Fields

Dilip, Deepthi Mary; Jabari, Saif Eddin; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)

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Vehicle Trajectory Reconstruction Using Conditional Random Fields

2018-01-07
By Dilip, Deepthi Mary ; Jabari, Saif Eddin

[PDF-2.05 MB]

English

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Creators:

Dilip, Deepthi Mary ; Jabari, Saif Eddin

Dilip, Deepthi Mary ; Jabari, Saif Eddin 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:

[+]

Algorithms Car Following Cellular Automata Conditional Random Fields Crash Reconstruction Global Positioning System Probe Vehicles Single Lane Traffic Traffic Data Traffic Estimation Trajectory Reconstruction Vehicle Trajectories
Publication/ Report Number:

18-03053
Resource Type:

Manuscript
Geographical Coverage:

United States
TRIS Online Accession Number:

01663617
Corporate Publisher:

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

This paper presents a probabilistic approach to reconstruct vehicle trajectories from GPS probe data on arterials. By combining car-following concepts with machine learning algorithms, we overcome the drawbacks of pure statistical modeling to investigate the question of adequate probe penetration levels on single-lane roads. Although the parameters of the traffic state estimation model are learned from historical data, the proposed algorithm is found to be robust to unpredictable conditions. The estimation algorithm is tested using a vehicle trajectory dataset generated using microsimulation software. The results highlight the need to take into account the randomness of the spatio-temporal coverage associated with probe data for reliable state estimation algorithms.
Content Notes:

Publication date derived from date of 97th TRB annual meeting. This is the author’s version of a work that was presented at the 97th Annual Meeting of the Transportation Research Board, Washington D.C., 2018. No. 18-03053. https://trid.trb.org/view/1495579
Format:

PDF
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

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urn:sha256:3c8f10114d2691abd55c4d241884e0fcc92c90927e77c3a33fd14362e41f8523
Download URL:

https://rosap.ntl.bts.gov/view/dot/40272/dot_40272_DS1.pdf
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Vehicle Trajectory Reconstruction Using Conditional Random Fields

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