Vehicle Path Reconstruction Using Automatic Vehicle Identification Data: A Bi-Directional Long Short-Term Memory-Based Approach-Reference-Cited by-同舟云学术

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Vehicle Path Reconstruction Using Automatic Vehicle Identification Data: A Bi-Directional Long Short-Term Memory-Based Approach

Published:2023-09-24 Issue: Volume: Page:5995-6000
ISSN:
Container-title:2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
language:
Short-container-title:

Author:

Bian Jing¹,Chen Peng²

Affiliation:

1. School of Transportation Science and Engineering, Beihang University,Beijing,100191

2. School of Transportation Science and Engineering, Ministry of Industry and Information Technology, Beihang University,Key Laboratory of Autonomous Transportation Technology for Special Vehicles,Beijing,100191

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420842/10420843/10422280.pdf?arnumber=10422280

Reference19 articles.

1. Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

2. Evaluation of Freeway Traffic Management and Control Measures Based on SUMO

3. Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

4. Traffic congestion analysis at the turn level using Taxis' GPS trajectory data

5. Estimation and prediction of the OD matrix in uncongested urban road network based on traffic flows using deep learning

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