subject: ccat subject: Truck platooning type: dataset
10.4231/2EEQ-3R20
Amir Kazemi
,
Aravind Ramakrishnan
,
Ashraf Alrajhi
,
Egemen Okte
,
Hadi Meidani
,
Hasan Ozer
,
Imad L. Al-Qadi
,
Jeffery R. Roesler
,
Rui Feng She
,
Sachindra Dahal
,
Yanfeng Ouyang
01/04/2022
This project have four different blocks. Block I: Real Time Optimization, Block II: Road Network Optimization of ACT Platoons, Block III: Resting Period and Pavement Damage, Block IV: Passive Sensing Paving Material
asphalt pavement autonomous and connected vehicles ccat Network Optimization Passive Sensing Platooning Truck platooning
10.4231/JVXT-YQ42
09/05/2023
A data-driven surrogate model was proposed to predict the drag force and fuel-consumption rate of truck platoons.
autonomous and connected vehicles ccat Platooning Truck platooning
10.4231/WEYJ-EG42
Aravind Ramakrishnan , Ashraf Alrajhi , Hasan Ozer , Imad Al-Qadi
09/05/2023
The main goal of the study was to understand the effect of truck platoons qualitatively and quantitatively on pavement distresses.
10.4231/Z5RS-KZ82
Angeli Jayme , Aravind Ramakrishnan , Fangyu Liu , Imad Al-Qadi
12/02/2024
This study utilized a conventional Burger’s model, incorporating a nonlinear power-law dashpot. A new load-pass approach enabled a reduction in computational domain and cost. A graph neural network was established to extend the framework.
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