Vehicle Trajectory Reconstruction Incorporating Probe and Fixed Sensor Data

Author:

Deng Yue1ORCID,Cao Qi2,Ren Gang3,Ma Jingfeng4ORCID,Zhu Sai2

Affiliation:

1. Ph.D. Candidate, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast Univ., No. 2 Southeast University Rd., Nanjing 211189, People’s Republic of China. ORCID: .

2. School of Transportation, Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 211189, People’s Republic of China.

3. Professor, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 211189, People’s Republic of China (corresponding author).

4. Ph.D. Candidate, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 211189, People’s Republic of China. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Reference32 articles.

1. Anderson, C. 2006. The long tail: Why the future of business is selling less of more. Hachette, UK: Canadian Journal of Communication.

2. Estimating freeway traffic volume using shockwaves and probe vehicle trajectory data;Anuar K.;Transp. Res. Procedia,2017

3. Lane-level trajectory reconstruction based on data-fusion;Arman M. A.;Transp. Res. Part C: Emerging Technol.,2022

4. Vehicle trajectory reconstruction for signalized intersections: A hybrid approach integrating Kalman Filtering and variational theory;Chen P.;Transportmetrica B: Transport Dyn.,2020

5. Integrated macro-micro modelling for individual vehicle trajectory reconstruction using fixed and mobile sensor data;Chen X.;Transp. Res. Part C: Emerging Technol.,2022

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