A revised video vision transformer for traffic estimation with fleet trajectories

Li, D. ORCID: 0000-0003-0142-9290 and Lasenby, J., 2022. A revised video vision transformer for traffic estimation with fleet trajectories. IEEE Sensors Journal, 22 (17), pp. 17103-17112. ISSN 1530-437X

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Real-time traffic monitoring represents a key component for transportation management. The increasing penetration rate of connected vehicles with positioning devices encourages the utilization of trajectory data for real-time traffic monitoring. The use of commercial fleet trajectory data could be seen as the first step towards mobile sensing networks. The main objective of this research is to estimate space occupancy of a single road segment with partially observed trajectories (commercial fleet trajectories in our case). We first formulate the trajectory-based traffic estimation as a video computing problem. Then, we reconstruct trajectory series into video-like data by performing spatial discretization. Following this, video input is embedded using a tubelet embedding strategy. Finally, a Revised Video Vision Transformer (RViViT) is proposed to estimate traffic state from video embeddings. The proposed RViViT is tested on a public dataset of naturalistic vehicle trajectories collected from German highways around Cologne during 2017 and 2018. The results witness the effectiveness of the proposed method in traffic estimation with partially observed trajectories.

Item Type: Journal article
Publication Title: IEEE Sensors Journal
Creators: Li, D. and Lasenby, J.
Publisher: Institute of Electrical and Electronics Engineers
Date: 1 September 2022
Volume: 22
Number: 17
ISSN: 1530-437X
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 09 Nov 2022 15:02
Last Modified: 09 Nov 2022 15:02
URI: https://irep.ntu.ac.uk/id/eprint/47358

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