Subject
Creator
- Konstantina Gkritza, 0000-0002-6598-4771 5
- Lisa Lorena Losada-Rojas, 0000-0002-7221-4900 4
- Aravind Ramakrishnan 3
- Christos Gkartzonikas, 0000-0001-6579-9106 3
- Srinivas Peeta 3
- Yanfeng Ouyang 3
- Apidej Sakulneya 2
- Ashraf Alrajhi 2
- David W. Eby 2
- Hadi Meidani 2
Type
- Dataset 22
Date
Total is 22 Results
Enhancing Construction Work-Zone Safety by Passive Pavement-to-Vehicle Communication
10.4231/F281-ZZ69
Apidej Sakulneya , Jeffery Roesler
09/01/2023
A laboratory-based experiment was developed to validate the theoretical configurations of smart work zones (SWZ) using passive pavement sensing.
Pavement Distresses Due to Truck Platoons: A Holistic Analysis
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.
Cooperative Control Mechanism for Platoon Formation of Connected and Autonomous Vehicles
10.4231/S6JE-ME83
Anye Zhou
,
Chaojie Wang
,
Jian Wang
,
Samuel Labi
,
Srinivas Peeta
,
Yu Wang
07/08/2022
The dataset of CCAT project "Cooperative Control Mechanism for Platoon Formation of Connected and Autonomous Vehicles"
Acceleration autonomous and connected vehicles ccat congestion
Development of In-vehicle Information Dissemination Mechanisms to Reduce Cognitive Burden in the Information-rich Driving Environment
10.4231/QVDH-1124
Irina Benedyk
,
Shubham Agrawal
,
Srinivas Peeta
07/18/2022
Processed driving simulator data collected at NEXTRANS Center, Purdue University. Data features synchronized EEG band powers around an instance of auditory travel information and contains demographic, information scenario, and trip details.
ccat Civil Engineering driver cognition driving simulator EEG travel information
In-vehicle Alerts for Conditionally Automated Vehicles
10.4231/JGHH-F716
Dustin J Souders
,
Irina Benedyk
,
Shubham Agrawal
,
Srinivas Peeta
,
Yuntao Guo
08/10/2022
Driving simulator study data collected at Purdue University from 2018-2019. Participants came in to the lab and completed the 2.5 hour long study that investigated the effects of different introductory information on take-over performance.
ccat Civil Engineering Conditionally automated driving graded warnings introductory materials SAE L3 take-over vehicle automation
Origin-Destination (O-D) Matrix from Indianapolis
10.4231/543K-FR90
Christos Gkartzonikas
,
Konstantina Gkritza
,
Lisa Lorena Losada-Rojas
,
Zimo Zhang
08/16/2022
The Origin-Destination matrix for Indy contains the flows of the 42 Transportation Analysis Zones surrounding the Indianapolis area. It also presents the total amount of travel within each region.
ccat Civil Engineering environmental impacts Shared autonomous vehicles
Highly Transportation Disadvantaged Area Chicago
10.4231/HYGW-T670
Christos Gkartzonikas
,
Konstantina Gkritza
,
Lisa Lorena Losada-Rojas
,
Raul F. Elizondo Candanedo
08/10/2022
This file contains areas designated as highly transportation disadvantaged in Chicago. This areas were outlined using three different measures and publicly available data. The methodology is replicable and can be used in different areas.
Autonomous Vehicles ccat Civil Engineering disadvantaged population GIS
Trajectory Dataset for translation of driver-pedestrian behavioral models at semi- controlled crosswalks into a quantitative framework.
10.4231/KMQX-R785
Jon D. Fricker
,
Yunchang Zhang
10/03/2022
On-site cameras provided a bird-eye view of an unsignalized crosswalk. Computer Vision algorithms generate a large-scale spatial-temporal trajectory dataset from more than three hours of videos.
ccat Civil Engineering Pedestrian Behavior Pedestrian-Vehicle Interactions
Multifront Approach for Improving Navigation of Autonomous and Connected Trucks
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
Highly Transportation Disadvantaged Areas Indianapolis
10.4231/12HS-8G97
Christos Gkartzonikas
,
Konstantina Gkritza
,
Lisa Lorena Losada-Rojas
,
Raul F. Elizondo Candanedo
08/10/2022
This file contains areas designated as highly transportation disadvantaged in Indianapolis. These two areas were outlined using three different measures and publicly available data. The methodology is replicable and can be used in other cities.
Autonomous Vehicles ccat Civil Engineering disadvantaged population GIS
Display #
Results 1 - 10 of 22