EAR Program

Compendium of Papers From 

Funded Research Projects
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Source: FHWA.
Contents

Behavioral Economics for Managed Lanes 1

Using Behavioral Economics to Better Understand Managed Lane Choice 1

Mobile Ad Hoc Networks 1

Exploring Mobile Ad Hoc Networks (MANETs) To Enable Connected Transportation Services 1

Computer Vision 1

Deep Insight: Deep Extraction of Driver State From Naturalistic Driving Dataset 1

Research Standards and Technical Assessment Support 3

Video Analytics for Automatic Annotation of Driver Behavior 

and Driving Situations in Naturalistic Driving Data 3

Supplementary Materials 4

Physically Informed Data-Driven Methods for Greatly Enhancing the Use 

of Heterogeneous Cementitious Materials in Transportation Infrastructure 4

Nontraditional and Natural Pozzolan-Based SCMs or Inorganic Polymers for Transportation Infrastructure 5

Performance-Based Classification Methods for Reclaimed Fly Ash 5

Development of Aging Resistant Binder Technology 6

Artificial Intelligence 6

Cooperative Perception and Control for Freeway Traffic System Operations 6

Predictive Real-Time Traffic Management in Large-Scale 

Networks Using Model-Based Artificial Intelligence 7

Traffic Incident Detection and Analysis System 7

Realistic Artificial Data (RAD) 8

Mimic: Multidisciplinary Initiative on Methods To Integrate and Create Artificial Realistic Data 8

Autonomous Winter Road Maintenance Decision Making Enabled by Boosting 

Existing Transportation Data Infrastructure with Deep and Reinforcement Learning 8

Development and Application of a Disaggregate Artificial Realistic Data 

Generator for Computationally Testing Safety Analysis Methods 9

Blockchain 9

Decentralized Vehicle Credential Management System Based on Consortium Blockchain 9

Waste Plastics In Asphalt Binder 10

Improving the Compatibility of Waste Plastic and Asphalt Binder Via 

Theoretically Justified Identification of Compatible Blends 10

Interagency Research 11

Augmented Reality for Control of Reservation-Based Intersections 

With Mixed Autonomous Non-Autonomous Flows 11

Identifying, Characterizing, and Shaping Multi-Scale Cyber-Human 

Interactions in Mixed Autonomous/Conventional Vehicle Traffic 11

Making Micromobility Smarter And Safer 12

Hybrid Twins for Urban Transportation: From Intersections to Citywide Management 13

References 14

EXPLORATORY 

1

Compendium of Papers From Funded Research Projects

USING BEHAVIORAL ECONOMICS TO BETTER 

UNDERSTAND MANAGED LANE CHOICE 

TEXAS A&M UNIVERSITY 

Behavioral economics draws on insights from psychology and 

economics to better understand human decisionmaking.(1)

Understanding cognitive biases and heuristics—such as loss 

aversion and present bias—may help policymakers predict 

human behavior and facilitate choices that support public 

benefits. Behavioral economists call these efforts to shape 

decisionmaking nudges—subtle tweaks to an environment 

that may influence human behavior. When applied to 

transportation studies, a behavioral economics framework 

can help develop more accurate models for predicting 

travelers’ choices on the road and inform policy and 

transportation planning decisions. Researchers for the 

project Using Behavioral Economics to Better Understand 

Managed Lane Choice are evaluating how behavioral 

economics could improve travel demand models.

ACCEPTED FOR PUBLICATION

1. Ashraf, S., A. Brown, M. Burris, and V. Vitaku. 2023. 

“Aggregate and Individual Effects of Information in a 

Coordination (Traffic) Game.” Economic Inquiry 61, no. 4: 

818–850. https://doi.org/10.1111/ecin.13143, last 

accessed October 12, 2023.

2. Ashraf, S., M. Burris, A. Brown, and V. Vitaku. 2022. 

“Using Behavioral Economics to Identify Potential 

Managed Lane Users.” Transportation Research Record: 

Journal of the Transportation Research Board, 2676, no. 

© Texas A&M Transportation Institute. Traffic modeling relies 

on data from highways such as the Lyndon B Johnson 

Freeway in Dallas, TX.(1)

Behavioral Economics 

for Managed Lanes

Behavioral Economics for Managed Lanes, Mobile Ad Hoc Networks, and Computer Vision

Mobile Ad Hoc Networks

EXPLORING MOBILE AD HOC NETWORKS 

(MANETs) TO ENABLE CONNECTED 

TRANSPORTATION SERVICES

UNIVERSITY OF VIRGINIA

Researchers investigated the use of MANETs for a 

connected transportation system that meets the needs of all 

travelers. The researchers explored two scenarios where 

MANETs could be useful for enhancing road safety: 

pedestrians and cyclists crossing at the midblock in large 

groups (e.g., people leaving a sporting event or concert) and 

communication improvements in low-volume areas such as 

rural areas and national parks.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Liu, S., H. Shen, B. Smith, and V. Fessmann. 2023. 

“Machine Learning Based Intelligent Routing for 

VDTNs.” Proceedings of the 32nd International 

Conference on Computer Communications and 

Networks (ICCCN). New York, NY: IEEE, 1–10.

Computer Vision

DEEP INSIGHT: DEEP EXTRACTION OF DRIVER 

STATE FROM NATURALISTIC DRIVING DATASET

IOWA STATE UNIVERSITY, SYRACUSE UNIVERSITY, 

UNIVERSITY OF MISSOURI, AND UNIVERSITY OF NEBRASKA 

MEDICAL CENTER

For the project Deep InSight: Deep Extraction of Driver State 

from Naturalistic Driving Dataset, researchers designed a 

driver-state estimation platform to improve the capacity to 

analyze large datasets related to human driving behaviors.(2)

This Deep InSight platform incorporated recurrent neural 

network (RNN) models trained to automatically detect and 

estimate human behaviors and deal with detection 

challenges, such as extreme-angle face detection when a 

driver is looking to the side or down. These RNNs are ideal 

for applications that involve complex interactions and input 

from multiple sensors, crucial for automated evaluation of 

driver state. 
© REACTOR Lab. Deep InSight platform design 

including tools and stages.(2)

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

2

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Aboah, A., Y. Adu-Gyamfi, S. V. Gursoy, J. Merickel, M. 

Rizzo, and A. Sharma. 2023. “Driver Maneuver Detection 

and Analysis Using Time Series Segmentation and 

Classification.” Journal of Transportation Engineering, 

Part A: Systems 149, no. 3. https://doi.org/10.1061/

JTEPBS.TEENG-7312, last accessed October 4, 2023.

2. Chai, W., J. Chen, J. Wang, S. Velipasalar, A. 

Venkatachalapathy, Y. Adu-Gyamfi, J. Merickel, and A. 

Sharma. 2023. “Driver Head Pose Detection from 

Naturalistic Driving Data.” IEEE Transactions on 

Intelligent Transportation Systems 24, no. 9: 9368–9377. 

https://doi.org/10.1109/TITS.2023.3275070, last 

accessed October 4, 2023.

3. Hasan, M. Z., A. Joshi, M. Rahman, A. 

Venkatachalapathy, A. Sharma, C. Hegde, and S. Srakar. 

2022. “DriveCLIP: Zero-Shot Transfer for Distracted 

Driving Activity Understanding Using CLIP.” Presented at 

Machine Learning for Autonomous Driving Workshop at 

the 36th Conference on Neural Information Processing 

Systems (NeurIPS 2022). New Orleans, LA: Neural 

Information Processing Systems. https://dr.lib.iastate.edu/

server/api/core/bitstreams/6caedeff-3a7d-46bc-aa80-

64fbcc5435cc/content, last accessed October 4, 2023.

4. Naphade, M., S. Wang, D. C. Anastasiu, Z. Tang, M-C 

Chang, Y. Yao, L. Zheng, et al. 2023. “The 7th AI City 

Challenge.” Presented at the 2023 IEEE/CVF Computer 

Vision and Pattern Recognition Conference. Vancouver, 

BC: IEEE. https://openaccess.thecvf.com/content/

CVPR2023W/AICity/papers/Naphade_The_7th_AI_City_

Challenge_CVPRW_2023_paper.pdf, last accessed 

October 4, 2023.

5. Naphade, M., S. Wang, D. C. Anastasiu, Z. Tang, M-C 

Chang, Y. Yao, L. Zheng, et al. 2022. “The 6th AI City 

Challenge.” Presented at the 2022 IEEE/CVF Computer 

Vision and Pattern Recognition Conference. New 

Orleans, LA: IEEE. https://openaccess.thecvf.com/

content/CVPR2022W/AICity/papers/Naphade_The_6th_

AI_City_Challenge_CVPRW_2022_paper.pdf, last 

accessed October 4, 2023.

6. Rahman, M. S., A. Venkatachalapathy, A. Sharma, J. 

Wang, S. V. Gursoy, D. Anastasiu, and S. Wang. 2023. 

“Synthetic Distracted Driving (SynDD1) Dataset for 

Analyzing Distracted Behaviors and Various Gaze Zones 

of a Driver.” Data in Brief 46: 108793. https://www.

sciencedirect.com/science/article/pii/

S2352340922009969, last accessed October 4, 2023.

7. Rahman, M. S., A. Venkatachalapathy, and A. Sharma. 

2022. Synthetic Distracted Driving (SynDD1) Dataset. 

Version 2. Mendeley Data. https://data.mendeley.com/

datasets/ptcp7rp3wb/4, last accessed October 4, 2023. 

8. Venkatachalapathy, A., M. S. Rahman, A. Raj, J. Merickel, 

A. Sharma, J. Wang, and S. Velipasalar. 2023. “Deep 

InSight: A Cloud Based Big Data Analytics Platform for 

Naturalistic Driving Studies.” Accepted for publication in 

International Journal of Automotive Engineering.

SUBMITTED FOR PUBLICATION 

1. Chai, W., J. Wang, J. Chen, S. Velipasalar, and A. 

Sharma “Rethinking the Evaluation of Driver Behavior 

Analysis Approaches.” Submitted to IEEE Transactions 

on Intelligent Transportation Systems.

2. Hasan, M. Z., J. Chen, J. Wang, A. Joshi, S. Velipasalar, 

C. Hegde, A. Sharma, and S. Srakar. “Vision-Language 

Models Can Identify Distracted Driver Behavior From 

Naturalistic Videos.” Submitted to IEEE Transactions on 

Intelligent Transportation Systems.

Evaluating driver state requires tracking combinations of 

cues over many frames—from multiple camera views—

and merging those with vehicle sensor data over time. 

The platform also makes it easier for researchers to 

manually check those automated annotations and verify 

the model’s performance.

Computer Vision
RESEARCH STANDARDS AND TECHNICAL 

ASSESSMENT SUPPORT

OAK RIDGE NATIONAL LABORATORY

Researchers at Oak Ridge National Laboratory developed 

calibration and measurement techniques to help the broader 

community of researchers wanting to work with Naturalistic 

Driving Study data. The techniques they employed in the 

Research Standards and Technical Assessment Support 

study enabled benchmarking progress and technical 

assessment of EAR Program-sponsored research teams.(3)

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Karnowski, T. P., D. Aykac, R. Ferrell, L. Thompson, and 

L. Torkelson. 2022. Database to Enable Facial Analysis 

for Driving Studies (DEFADS). Report No. ORNL/

TM-2022/2786. Oak Ridge, TN: Oak Ridge National 

Laboratory. https://www.osti.gov/biblio/1901618/, last 

accessed October 5, 2023. 

2. Rachow, M., T. Karnowski, and A. J. O’Toole. 2023. 

“Identity Masking Effectiveness and Gesture Recognition: 

Effects of Eye Enhancement in Seeing Through the 

Mask.” https://doi.org/10.48550/arXiv.2301.08408, last 

accessed October 5, 2023.

VIDEO ANALYTICS FOR AUTOMATIC ANNOTATION 

OF DRIVER BEHAVIOR AND DRIVING SITUATIONS 

IN NATURALISTIC DRIVING DATA

VIRGINIA TECH TRANSPORTATION INSTITUTE

In this study, researchers at the Virginia Tech Transportation 

Institute worked on developing computer vision methods that 

would facilitate the automatic generation of annotations from 

the Second Strategic Highway Research Program (SHRP2) 

Naturalistic Driving Study (NDS) database using the 

continuous videos.(2,4)

 The study aimed to make it easier for 

researchers to create smaller subsets of data from the more 

than 1 million h of video data that make up the SHRP2 NDS 

database. The research team developed and evaluated a 

series of deep neural network models (including convolutional 

neural network and recurrent neural network) to capture the 

spatial and temporal information embedded in the video. 

Enhanced access to very large datasets with appropriate 

video annotations will facilitate a quantum leap forward in 

transportation safety research. Such work could make it 

possible to explore questions that are currently out of reach of 

investigators, including the interactions between human 

drivers and other road users, road infrastructure elements, 

and roadside objects.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Bhagat, H., S. Jain, L. Abbott, A. Sonth, and A. Sarkar. 2023. 

“Driver Gaze Fixation and Pattern Analysis in Safety Critical 

Events.” In 2023 IEEE Intelligent Vehicles Symposium IV. 

Piscataway, NJ: IEEE. https://ieeexplore.ieee.org/

document/10186718, last accessed October 5, 2023.

2. Bhat, S., L. Abbott, and V. Sundharam. 2023. “Deep 

Intersection and Driving Environment Detection and Its 

Potential in Driver Safety.” Poster presented at the 102nd 

Annual Meeting of the Transportation Research Board. 

Washington, DC: Transportation Research Board.

3. Sonth, A., A. Sarkar, H. Bhagat, and L. Abbott. 2023. 

“Explainable Driver Activity Recognition Using Video 

Transformer in Highly Automated Vehicle.” In 2023 IEEE 

Intelligent Vehicles Symposium IV. Piscataway, NJ: IEEE. 

https://ieeexplore.ieee.org/document/10186584, last 

accessed October 5, 2023.

CONFERENCE PRESENTATIONS

1. Sarkar, A. 2023. “Driver Gaze Fixation and Pattern 

Analysis in Safety Critical Events.” Presented at the 

10th International Workshop on Naturalistic Driving Data 

Analytics (NDDA), IEEE Intelligent Vehicles Symposium 

IV. Anchorage, AK: IEEE.

EXPLORATORY 

3

Compendium of Papers From Funded Research Projects

Computer Vision
PHYSICALLY INFORMED DATA-DRIVEN METHODS 

FOR GREATLY ENHANCING THE USE OF 

HETEROGENEOUS CEMENTITIOUS MATERIALS 

IN TRANSPORTATION INFRASTRUCTURE

UNIVERSITY OF CALIFORNIA, LOS ANGELES

Researchers at the University of California, Los Angeles, 

sought to develop what they called “new data-guided 

pathways” to help determine which grades of fly ashes, 

including reclaimed and off-spec fly ashes, can be used in 

the production of concrete for highway construction 

applications. The project aimed to decipher, through a 

data-guided and machine-learning approach, how the 

physical and chemical features of fly ashes—the “genome” 

of the fly ash—control their performance in concrete. 

ACCEPTED FOR PUBLICATION

1. Collin, M., Y. Song, D. P. Prentice, A. R. A. Arnold, K. 

Ellison, D. A. Simonetti, M. Bauchy, and G. N. Sant. 2023. 

“Fly Ash Degree of Reaction in Hypersaline NaCl and 

CaCl2 Brines: Effects of Calcium-Based Additives.” Waste 

Management 170: 103–111. https://doi.org/10.1016/j.

wasman.2023.08.002, last accessed October 5, 2023.

2. Han, T., S. A. Ponduru, A. Reka, J. Huang, G. Sant, and A. 

Kumar. 2023. “Predicting Dissolution Kinetics of Tricalcium 

Silicate Using Deep Learning and Analytical Models.” 

Algorithms 16, no. 1: 7. https://doi.org/10.3390/

a16010007, last accessed October 5, 2023.

3. Han, T., R. Bhat, S. A. Ponduru, A. Sarkar, J. Huang, G. 

Sant, H. Ma, N. Neithalath, and A. Kumar. 2023. “Deep 

Learning to Predict the Hydration and Performance of Fly 

Ash-Containing Cementitious Binders.” Cement and 

Concrete Research 165: 107093. https://doi.org/10.1016/j.

cemconres.2023.107093, last accessed October 5, 2023.

Fly ash, a supplementary cementitious material (SCM), is an important constituent in the production of concrete. Although demand for SCMs is increasing, the amount of fly ash produced by coal-fired power 

plants is decreasing, so the transportation industry is looking for viable alternatives to traditional fly ash that 

can provide reliable performance.(5) These alternatives, which include nontraditional or off-specification fly 

ash as well as other SCM sources, are promising because of their abundance and potential economic value. 

Three projects supported through the EAR Program seek to study and document how the chemical and 

physical properties of these alternative materials affect the performance of concrete.(6) These projects aim to 

provide State departments of transportation with information that describes how these materials affect the 

durability, sustainability, and strength of concrete to help engineers make informed and timely decisions 

regarding material specifications and concrete mixture performance criteria.

Source: FHWA. A clipboard showing samples of fly ash 

before and after electrostatic separation to reduce the 

amount of carbon in fly ash.(7)

4. Ponduru, S. A., T. Han, J. Huang, and A. Kumar. 2023. 

“Predicting Compressive Strength and Hydration Products 

of Calcium Aluminate Cement Using Data-Driven 

Approach.” Materials 16, no. 2: 654. https://doi.

org/10.3390/ma16020654, last accessed October 5, 2023.

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

4

Supplementary Materials

Supplementary Materials
NONTRADITIONAL AND NATURAL 

POZZOLAN-BASED SCMS OR INORGANIC 

POLYMERS FOR TRANSPORTATION 

INFRASTRUCTURE

PURDUE UNIVERSITY

Researchers at Purdue University, with assistance from 

researchers at Penn State University and Clarkson 

University, are seeking to analyze and conduct further 

studies on how nontraditional and natural pozzolan SCMs 

might perform in concrete pavements or other transportation 

structures.(6)

 These nontraditional sources—calcinated clays, 

natural pozzolans, bottom ashes, and fluidized bed 

combustion ashes—are cost competitive and relatively 

abundant in different U.S. regions. Laboratory tests can 

create an understanding of how these resources could be 

used as viable alternatives to fly ash. 

ACCEPTED FOR PUBLICATION

1. Arachchige, R. M., and S. Peethamparan. 2023. 

“Composition and Chain Length of Alkali-Activated 

Ground Bottom Ash Gels Using NMR.” Presented at the 

16th International Congress on the Chemistry of Cement. 

Bangkok, Thailand: International Congress on the 

Chemistry of Cement. 

2. Mishra, S., and S. Peethamparan. 2023. “Alkali-Silica 

Reaction Resistance of Alkali-Activated Calcined Clays 

Using Accelerated Mortar Bar Test.” Presented at the 

16th International Congress on the Chemistry of Cement. 

Bangkok, Thailand: International Congress on the 

Chemistry of Cement.

3. Rajabipour, F., and M. Sharbaf. 2023 “Predicting the 

Alkali Contribution of SCMs to Concrete Pore Solution.” 

Proceedings of the 1st Interdisciplinary Symposium on 

Smart & Sustainable Infrastructures (ISSSI 2023), 77th 

RILEM Annual Week. Vancouver, Canada: RILEM. 

4. Sharbaf, M., and F. Rajabipour. 2023. “A New Soluble 

Alkali Test to Predict the Alkali Contribution of SCMs to 

Concrete Pore Solution.” Presented at the 16th 

International Congress on the Chemistry of Cement 

(ICCC 2023). Bangkok, Thailand: International Congress 

on the Chemistry of Cement.

5. Tokpatayeva, R., A. Castillo, J. Yoon, G. Kaladharan, 

K. Jafari, R. Arachchige, F. Rajabipour, S. Peethamparan, and 

J. Olek. 2022. “Comparative Study of the Reactivity and 

Performance of Different Nontraditional and Natural Pozzolans 

in Cementitious System.” Advances in Civil Engineering 

Materials 11, no. 2: 670–693. https://doi.org/10.1520/

ACEM20220021, last accessed October 20, 2023.

6. Yoon, J., K. Jafari, R. Tokpatayeva, S. Peethamparan, 

J. Olek, and F. Rajabipour. 2022. “Characterization and 

Quantification of the Pozzolanic Reactivity of Natural 

and Non-Conventional Pozzolans.” Cement and 

Concrete Composites 133: 104708. https://doi.

org/10.1016/j.cemconcomp.2022.104708, last accessed 

October 20, 2023.

SUBMITTED FOR PUBLICATION AND PRESENTATIONS

1. Arachchige, R. M., S. Peethamparan, J. Olek, and 

F. Rajabipour. 2023. “Nontraditional Alumino-Silicate 

Based Alkali-Activated Mortars - Statistical Optimization 

of Solution Parameters and Processing Conditions for 

Optimal Strength, Workability and Setting Time.” 

Submitted and under revision, Construction and 

Building Materials.

2. Arachchige, R. M., S. Peethamparan, J. Olek, and 

F. Rajabipour. 2023. “Phase Identification and 

Micromechanical Properties of Nontraditional and Natural 

Pozzolan based Alkali-Activated Materials.” Submitted 

and under review, Cement and Concrete Composites.

3. Castillo, A., R. Tokpatayeva, J. Olek, F. Rajabipour, and 

S. Peethamparan. 2023. “Influence of Physicochemical 

Properties of Nontraditional and Natural Pozzolans 

(NNPs) on Selected Characteristics of Concrete.” 

Accepted for presentation, Transportation Research 

Board Annual Meeting. 

4. Mishra, S., and S. Peethamparan. 2023. “ASR 

Resistance of Ground Bottom Ash-Based Alkali-activated 

Concrete and the Prospect of Using MCPT for the 

Evaluation.’’ Submitted to International Conference on 

Alkali-Aggregate Reaction in Concrete.

PERFORMANCE-BASED CLASSIFICATION 

METHODS FOR RECLAIMED FLY ASH

OKLAHOMA STATE UNIVERSITY

Researchers are studying older fly ash from landfills and 

surface impoundments, called “reclaimed” fly ash, to 

determine if they provide viable alternatives to the approved 

fly ash sources currently in use. The researchers seek to 

combine advanced material characterization methods, 

performance-based testing, mechanistic modeling, and 

machine learning to create engineering tools to classify 

reclaimed fly ash.(6)

 The project involves tweaking existing 

testing methods to analyze reclaimed fly ash performance 

when used for concrete production. The researchers want to 

capture the differences in chemical composition among the 

various reclaimed fly ashes and how those differences relate 

to their performance in concrete. The team also expects to 

analyze the test data with machine-learning methodology to 

see what patterns emerge. 

EXPLORATORY 

5

Compendium of Papers From Funded Research Projects

Supplementary Materials
ACCEPTED FOR PUBLICATION

1. Kang, S., B. Boyd, and M. T. Ley. 2022. 

“Performance and Prediction of Non-Traditional 

Coal-Ash in Concrete with the Particle Model.” 

Construction and Building Materials 345: 128170. 

https://doi.org/10.1016/j.conbuildmat.2022.128170, 

last accessed October 19, 2023.

2. Kang, S., L. Emerson, J. Lee, and M. T. Ley. 2023. 

“Determining the Air-Entraining Admixture Dosage in 

Concrete with Non-Traditional Coal Ash.” Materials and 

Structures 56, no. 9. https://doi.org/10.1617/s11527-022-

02098-1, last accessed October 19, 2023.

DEVELOPMENT OF AGING RESISTANT 

BINDER TECHNOLOGY

AUBURN UNIVERSITY

Researchers at Auburn University, along with partnering 

groups, examined fundamental aspects of asphalt 

modification to help the highway industry make better 

choices about pavement systems. In a 3-yr study, the 

researchers examined six additives to see how they perform 

as a basis to develop a standard process that can be used 

to evaluate future anti-aging asphalt additives. By testing the 

effectiveness of additive products, the researchers aimed to 

create asphalt mixtures that result in durable and longer 

lasting roads and pavements. The researchers also 

examined whether mitigating oxidation through use of 

certain additives is a viable approach in ensuring the 

durability of the chemical binders used in asphalt production. 

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Garita-Jimenez, J., N. Tran, F. Keuliyan, R. Moraes, C. 

Rodezno, and F. Yin. 2023. “Evaluating Aging Resistant 

Technologies for Enhancing Cracking Resistance of 

Asphalt Mixtures.” Accepted for publication 

Transportation Research Record: Journal of the 

Transportation Research Board.

2. Keuliyan, F., R. Moraes, N. Tran, R. West, F. Yin, C. 

Rodezno, and F. Leiva. 2023. “Evaluation of Innovative 

Aging-Resistant Technologies to Improve the Cracking 

Susceptibility of Asphalt Binders.” Presented at the 

102nd Annual Meeting of the Transportation Research 

Board. Washington, DC: Transportation Research Board. 

3. Keuliyan, F., R. Moraes, and N. Tran. 2022. “Rheological 

and Chemical Evaluation of Aging Resistant Binder 

Technologies.” Presented at the AAPT Annual Meeting. San 

Antonio, TX: Association of Asphalt Paving Technologists.

4. Keuliyan, F., R. Moraes, N. Tran, F. Yin, C. Rodezno, and 

F. Leiva. 2022. “Rheological and Chemical Evaluation of 

Aging Resistant Binder Technologies.” Presented at the 

59th Petersen Asphalt Research Conference. Laramie, 

WY: Western Research Institute.

Artificial Intelligence

COOPERATIVE PERCEPTION AND CONTROL FOR 

FREEWAY TRAFFIC SYSTEM OPERATIONS

UNIVERSITY OF CINCINNATI AND THE UNIVERSITY OF 

CALIFORNIA, LOS ANGELES

The research team aims to develop next-generation Traffic 

System Management and Operations solutions for freeway 

systems based on cooperative driving automation. The 

proposed solution involves cooperative perception (i.e., 

estimation and prediction using various data sources based 

on machine-learning and filtering methods) and cooperative 

control (i.e., advanced artificial intelligence algorithms 

customized for vehicle- and infrastructure-level control, such 

as cooperative merging, platooning, and speed 

harmonization). The work is focusing on computational 

applications that could substantially increase freeway system 

safety and mobility to meet the following objectives: 

• Integrate traditional and nontraditional highway data to 

better explain and predict system performance. 

• Provide decision support to assist experts in highway 

system design, operations, and management.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Meng, Z., X. Xia, R. Xu, W. Liu, and J. Ma. 2023. “HYDRO3D: Hybrid Object Detection and Tracking for Cooperative 

Perception Using 3D LiDAR.” IEEE Transactions on 

Intelligent Vehicles 8, no. 8: 4069–4080. https://ieeexplore.

ieee.org/abstract/document/10148929, last accessed 

October 10, 2023.

2. Xia, X., R. Xu, and J. Ma. 2023. “Secure Cooperative 

Localization for Connected Automated Vehicles Based on 

Consensus.” IEEE Sensors Journal 2023.

3. Xiang, H., R. Xu, X. Xia, Z. Zheng, B. Zhou, and J. Ma. 

2023. “V2XP-ASG: Generating Adversarial Scenes for 

Vehicle-to-Everything Perception.” Presented at the 2023 

IEEE International Conference on Robotics and 

Automation. London, England. 

4. Xu, R., W. Chen, H. Xiang, X. Xia, L. Liu, and J. Ma. 2023. 

“Model-Agnostic Multi-Agent Perception Framework.” 

Presented at the 2023 IEEE International Conference on 

Robotics and Automation. London, England.

5. Xu, R., J. Li, X. Dong, H. Yu, and J. Ma. 2023. “Bridging the 

Domain Gap for Multi-Agent Perception.” Presented at the 

2023 IEEE International Conference on Robotics and 

Automation. London, England.

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

6

Supplementary Materials and Artificial Intelligence
6. Zheng, Z., X. Han, X. Xia, L. Gao, H. Xiang, and J. Ma. 

2023. “OpenCDA-ROS: Enabling Seamless Integration 

of Simulation and Real-World Cooperative Driving 

Automation.” IEEE Transactions on Intelligent Vehicles 8, 

no. 7: 3775–3780. https://ieeexplore.ieee.org/

document/10192346, last accessed October 10, 2023.

PREDICTIVE REAL-TIME TRAFFIC MANAGEMENT 

IN LARGE-SCALE NETWORKS USING 

MODEL-BASED ARTIFICIAL INTELLIGENCE

CARNEGIE MELLON UNIVERSITY

Despite decades of research, mitigating traffic congestion 

due to nonrecurring causes, such as crashes, disabled 

vehicles, and adverse weather events, remains quite difficult 

for highway system operations practitioners.(8)

 This work 

requires an automated process of accurate, real-time 

prediction and proactive operational management that 

currently does not exist. Researchers from Carnegie Mellon 

University and the University of Washington Seattle, in their 

project Predictive Real-Time Traffic Management in 

Large-Scale Networks Using Model-Based Artificial 

Intelligence, aim to address this issue. The project seeks to 

fuse prediction strategies, based on artificial intelligence and 

machine learning guided by transportation network flow 

models, with operational strategies. The researchers want to 

predict nonrecurrent traffic conditions in large-scale networks 

up to 30 min ahead of the earliest time an incident is 

reported and proactively recommend real-time operational 

management strategies. 

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Haocheng, D., H. Wu, P. Huang, and S. Qian. 2023. 

“Two-Stage Multi-Task Learning Model for Proactive 

Non-Recurrent Traffic Prediction.” Accepted for 

presentation at the 2024 Transportation Research Board 

Annual Meeting. Washington, DC: Transportation 

Research Board.

2. Zhang, P., and S. Qian. 2023. “Low-Rank Approximation 

of Path-Based Traffic Network Models.” Accepted for 

presentation at the 2024 Transportation Research Board 

Annual Meeting. Washington, DC: Transportation 

Research Board.

TRAFFIC INCIDENT DETECTION AND 

ANALYSIS SYSTEM

TUFTS UNIVERSITY

Researchers at Tufts University and the City College of New 

York are leveraging artificial intelligence (AI) to improve the 

detection of highway incidents.(9)

 In this study, researchers 

are creating a novel framework using AI and imageprocessing algorithms. The framework aims to exploit the 

potential of currently installed highway camera 

infrastructures for incident detection, including spotting 

wrong-way driving, crashes, hazardous objects in the 

roadway, and bicyclists or pedestrians in tunnels. One 

drawback of existing highway incident detection technologies 

is their scalability. Monitoring and analyzing the 

overwhelming quantity of camera data without assistive 

automated methods is challenging. Utilizing AI, models can 

be trained to enhance images and provide robust detection 

and classification of traffic incidents, resulting in more 

cost-effective deployment of incident-response resources. 

This research project focuses on solving challenges 

including the following: 

• The lack of a robust automatic incident detection 

system capable of emphasizing key events with 

minimal false alarms. 

• The problems inherent in current learning algorithms, 

which significantly degrade in performance under 

adverse weather conditions. 

• The unavailability of a dataset with diverse footage 

of highway incidents to foster the development and 

validation of AI algorithms. 

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Badrieva, S., S. Bernabel, and S. Agaian. 2022. 

“Comprehensive Analysis and Benchmarking of 

De-Raining Techniques Through a Rain Model 

Approach.” Multimodal Image Exploitation and Learning 

2022 12100. Bellingham, WA: SPIE. https://doi.

org/10.1117/12.2618314, last accessed October 18, 2023. 

2. Frants, V., S. Again, and K. Panetta. 2023. “QCNN-H: 

Single-Image Dehazing Using Quaternion Neural 

Networks.” IEEE Transactions on Cybernetics 53, no. 9: 

5448–5458. https://doi.org/10.1109/TCYB.2023.3238640, 

last accessed October 18, 2023. 

3. Frants, V., S. Agaian, and K. Panetta. 2023. “QSAM-Net: 

Rain Streak Removal by Quaternion Neural Network With 

Self-Attention Module.” IEEE Transactions on Multimedia. 

https://doi.org/10.1109/TMM.2023.3271829, last accessed 

October 18, 2023.

EXPLORATORY 

7

Compendium of Papers From Funded Research Projects

Artificial Intelligence 
4. Frants, V., and S. Agaian. 2023. “Weather Removal With 

a Lightweight Quaternion Chebyshev Neural Network.” 

Multimodal Image Exploitation and Learning 2023 12526. 

Bellingham, WA: SPIE. https://doi.org/10.1117/12.2664858, 

last accessed October 18, 2023.

5. Hovhannisyan, S. A., H. A. Gasparyan, S. S. Agaian, 

and A. Ghazaryan. 2022. “AED-Net: A Single Image 

Dehazing.” IEEE Access 10: 12465–12474. https://doi.

org/10.1109/ACCESS.2022.3144402, last accessed 

October 18, 2023.

6. Kezebou, L., V. Oludare, K. Panetta, J. Intriligator, and S. 

Agaian. 2022. “Highway Accident Detection and 

Classification from Live Traffic Surveillance Cameras: A 

Comprehensive Dataset and Video Action Recognition 

Benchmarking.” Multimodal Image Exploitation and 

Learning 2022 12100. Bellingham, WA: SPIE. https://doi.

org/10.1117/12.2618943, last accessed October 18, 2023.

7. Liu, S., and S. S. Agaian. 2023. “ALSA: Adaptive 

Low-light Correction and Self-Attention Module for Vehicle 

Re-Identification.” Artificial Intelligence Evolution 4, no. 2: 

99–119. https://doi.org/10.37256/aie.4220232901, last 

accessed October 18, 2023.

SUBMITTED FOR PUBLICATION OR IN PREPARATION

1. Bernabel, S. A., and S. S. Agaian. “NDELS: Nighttime 

Dehazing Low-Light Enhancement and Light 

Suppression.”

2. Frants V., S. Agaian, K. Panetta, and P. Huang. 

“CMAWRNet: Multiple Adverse Weather Removal via a 

Unified Quaternion Neural Architecture.” Submitted to 

IEEE Intelligent Transportation Systems Transactions.

3. Frants, V., S. Agaian, K. Panetta, and J. Intriligator. 

“DCQ-Net: A Deep Quaternion Neural Network for 

Accident Detection in Real-Life Scenarios.”

4. Kaplan, L. E., V. Frants, and S. Agaian. “CUNY Video 

Database for Collision and Anomaly Detection in 

Real-Life Traffic Scenarios.”

5. Liu, S., and S. S. Agaian. “VERI-D: A New Dataset and 

Method for Multi-Camera Vehicle Re-Identification of 

Damaged Cars.” Submitting to APL Machine Learning.

AUTONOMOUS WINTER ROAD MAINTENANCE 

DECISION MAKING ENABLED BY BOOSTING 

EXISTING TRANSPORTATION DATA 

INFRASTRUCTURE WITH DEEP AND 

REINFORCEMENT LEARNING

MICHIGAN TECHNOLOGICAL UNIVERSITY

Researchers at the Michigan Technological University are 

investigating the potential to shift winter maintenance 

decisionmaking from a model-driven to an artificial intelligence 

(AI)-enhanced framework.(10)

 The researchers are developing 

a data-driven maintenance decision support system to help 

State department of transportation (DOT) highway 

maintenance professionals plan for weather events through 

improved data processing, predictive road condition methods, 

and computer-supported decisionmaking. The team is 

investigating opportunities from AI to analyze real-world 

situations in realtime. Recurrent neural networks (RNNs) and 

road condition predictions provide a data-driven environment. 

Deep reinforced learning takes the RNN predictions and puts 

them into action using machine learning to make autonomous 

decisions. Convolutional neural networks provide real-time 

road condition sensing. To test these machine-learning 

models in the field, researchers are working with the Michigan 

DOT and Michigan county road agencies to conduct field 

tests. The researchers are developing a closed-loop approach 

consisting of data gathering, condition predictions, 

decisionmaking, validation, and human intervention. This 

approach maximizes AI’s capability to significantly improve 

winter maintenance operations, safety, and mobility; reduce 

labor hours and costs; and indirectly enhance pavement 

design and management.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Tavakoli Dastjerdi, M. H., Z. Liu, B. Azmoon, and X. Yuan. 

2023. “Enhancing Winter Road Maintenance via Cloud 

Computing.” IEEE Internet Computing 27, no. 4: 6–14. 

https://ieeexplore.ieee.org/abstract/document/10098262, 

last accessed November 17, 2023.

Realistic Artificial Data 

(RAD)

MIMIC: MULTIDISCIPLINARY INITIATIVE ON 

METHODS TO INTEGRATE AND CREATE 

ARTIFICIAL REALISTIC DATA

UNIVERSITY OF MISSOURI

To expand traditional safety modeling practices and results, 

this project developed a framework that would generate 

RADs that mimic the known causal relationships between 

contributing factors and crashes. The researchers applied 

the framework to generate RADs for ramp terminals and 

speed change lane facilities at diamond interchanges.(11)

The researchers also developed Web-based software to 

provide easy access to the RADs by other researchers who 

wanted to test their models. 

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

8

Artificial Intelligence and Realistic Artificial Data (RAD) 
The researchers also used RADs to evaluate new behavioral 

and roadway countermeasures by generating virtual reality 

simulation testbeds for crashes and near-crashes occurring 

at interchanges. A graphical user interface developed by the 

researchers facilitated the testbeds for left-turn and speed 

change lane crashes. Virtual reality provides an engaging 

platform for evaluating countermeasures and educating the 

public about interchange crashes, which can help achieve 

the U.S. Department of Transportation’s goal of zero 

roadway fatalities.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

Li, C., P. Edara, and Y. Shang. 2023. “Crash Frequency 

Modeling Using Realistic Artificial Data.” 2023 IEEE 

Conference on Artificial Intelligence (CAI). Piscataway, 

NJ: IEEE, 28–29. https://ieeexplore.ieee.org/abstract/

document/10195079, last accessed October 10, 2023.

DEVELOPMENT AND APPLICATION OF A 

DISAGGREGATE ARTIFICIAL REALISTIC DATA 

GENERATOR FOR COMPUTATIONALLY TESTING 

SAFETY ANALYSIS METHODS

UNIVERSITY OF CONNECTICUT

In a collaboration between the University of Connecticut and 

the University of Central Florida, researchers are building a 

RAD tool for generating datasets for all facility types using 

different input combinations.(12)

 The tool will be customizable 

and capable of generating datasets at both the macroscopic 

and microscopic levels. The macroscopic level is suited for 

single-step data models, such as those currently used in 

transportation safety analysis. The microscopic level can 

compare advanced research models and consider complex 

factors like human behavior as well as accommodate major 

changes in transportation, such as the inclusion of 

autonomous vehicles on roadways. 

To show proof of concept, the research team will generate 

two case studies: one focused on vehicle crashes on 

segments and one focused on vehicle and pedestrian 

crashes at intersections. Developing RAD tools is key to the 

Federal Highway Administration (FHWA) goal of expanding 

the adoption of data-driven safety analysis, as it will enable 

users to objectively select the best methods for their data. 

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Hoover, L, M. I. Jahan, T. Bhowmik, S. D. Tirtha, K. C. 

Konduri, J. Ivan, K. Wang, S. Zhao, J. Auld, and N. Eluru. 

2023. “Implementation of a Realistic Artificial Data 

Generator for Crash Data Generation.” Submitted and in

review with Accident Analysis and Prevention.

2. Olufowobi, O., J. Ivan, S. Zhao, and K. Wang. 2023. 

“Application of Realistic Artificial Data for Testing Various 

Crash Safety Analysis: A Case Study for Rural Two-Lane 

Undivided Highways.” Presented at the 102nd 

Transportation Research Board Annual Meeting. 

Washington, DC: Transportation Research Board. 

3. Olufowobi, O., J. Ivan, S. Zhao, and K. Wang. 2023. 

“Application of Realistic Artificial Data for Testing Artificial 

Data for Testing Various Crash Safety Analysis: A Case 

Study for Rural Two-Lane Undivided Highways.” 

Transportation Research Record. https://doi.

org/10.1177/03611981231175901, last accessed 

October 13, 2023.

Blockchain

DECENTRALIZED VEHICLE CREDENTIAL 

MANAGEMENT SYSTEM BASED ON 

CONSORTIUM BLOCKCHAIN

NEW JERSEY INSTITUTE OF TECHNOLOGY

Vehicle-to-vehicle (V2V) communication uses wireless 

technology to send and receive messages with surrounding 

vehicles.(13)

 V2V communication increases highway safety 

through precollision warnings and provides convenience, 

such as information on traffic congestion. It is becoming 

standard in the automotive trade to install equipment in 

new vehicles that allow V2V communication. One concern 

in wireless communication is the increasing potential for 

unauthorized intrusions or cyberattacks and the breech of 

personal privacy. This project is researching and 

developing software that increases V2V security by 

improving upon the current “handshake” protocols for 

authenticating vehicle identification and maintaining 

security during message transportation. 

© 2016 Missouri DOT. Modifications by FHWA to show the 

gore point, speed change, diverging, and merging lanes.

Components of speed change lanes at an intersection.(11)

EXPLORATORY 

9

Compendium of Papers From Funded Research Projects

Realistic Artificial Data RAD ( ) and Blockchain
ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Du, W., A. Dash, J. Li, H. Wei, and G. Wang. 2023. “Safety 

in Traffic Management Systems: A Comprehensive 

Survey.” Designs 7 no. 4: 100. https://doi.org/10.3390/

designs7040100, last accessed October 29, 2023.

2. Du, W., J. Ye, J. Gu, J. Li, H. Wei, and G. Wang. 2023. 

“SafeLight: A Reinforcement Learning Method toward 

Collision-free Traffic Signal Control.” AAAI 23: 

Proceedings of the Third-Seventh AAAI Conference on 

Innovative Applications of Artificial Intelligence and 

Thirteenth Symposium on Educational Advances in 

Artificial Intelligence. Washington, DC: Association for the 

Advancement of Artificial Intelligence. 14801–14810. 

https://doi.org/10.1609/aaai.v37i12.26729, last accessed 

October 29, 2023.

3. He, S., Y. Lu, Q. Tang, G. Wang, and C. Wu. 2022. 

“Blockchain-Based P2P Content Delivery With Monetary 

Incentivization and Fairness Guarantee.” IEEE Transactions 

on Parallel and Distributed Systems 34, no. 2: 746–765. 

https://ieeexplore.ieee.org/document/9929262, last 

accessed October 29, 2023. 

4. Yao, W., F. P. Deek, R. Murimi, and G. Wang. 2023. 

“SoK: A Taxonomy for Critical Analysis of Consensus 

Mechanisms in Consortium Blockchain.” IEEE Access 11: 

79572–79587. https://ieeexplore.ieee.org/

document/10192902, last accessed October 29, 2023.

5. Yao, W., J. Gu, W. Du, F. P. Deek, and G. Wang. 2022. 

“A Novel Anomaly Detection and Privacy-Preserving 

Framework Using Blockchain and Neural Networks in 

Tokenomics.” International Journal of Artificial Intelligence 

& Applications 13, no. 6. https://aircconline.com/ijaia/

V13N6/13622ijaia02.pdf, last accessed October 29, 2023.

6. Yao, W., Y. Liu, F. P. Deek, and G. Wang. 2023. “iBCTrans: 

A Practical Blockchain-Based Framework for Cellular 

Vehicular-To-Everything Networks.” Accepted for 

publication in IEEE Blockchain. 

7. Yao, W., Y. Liu, F. P. Deek, and G. Wang. 2023. “VDKMS: 

Vehicular Decentralized Key Management System for 

Cellular Vehicular-to-Everything Networks, A BlockchainBased Approach.” Accepted for publication in IEEE Global 

Communications Conference.

Waste Plastics In 

Asphalt Binder

IMPROVING THE COMPATIBILITY OF WASTE 

PLASTIC AND ASPHALT BINDER VIA 

THEORETICALLY JUSTIFIED IDENTIFICATION 

OF COMPATIBLE BLENDS

LOUISIANA TECH UNIVERSITY

Waste plastic, though largely considered an environmental 

concern, has the potential to be a valuable resource.(14)

Use of waste plastics to supplement traditional asphalt 

binders can reduce resource consumption and become an 

alternative to the disposal of waste plastic in landfills. 

However, challenges exist with using waste plastics in 

asphalt. Knowledge of waste plastic’s compatibility with 

asphalt binders is limited, as are the different types of 

polymers that make up waste plastic. The researchers for 

this 3-yr, first-of-its-kind project are investigating and 

developing a computational model that can understand on a 

molecular and atomic level which waste polymers are 

compatible with which given asphalt binders to optimize the 

blend’s performance. Through this computational model, the 

researchers aim to provide a foundation for using waste 

plastic in asphalt pavements on an industrial scale.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Chowdhury, A., P. Nourian, N. M. Wasiuddin, and A. 

Peters. 2023. “Investigation of Phase Separation Behavior 

of Polymer-Asphalt Mix Using Molecular Dynamics 

Simulation.” Under review in Fuel.

© 2021 Louisiana Tech University. This figure shows the 

model used to calculate the compatibility of different 

polymers and asphalt (the “solvent” in the figure).(14)

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

10

Blockchain and Waste Plastics in Asphalt Binder
2. Chowdhury, A., P. Nourian, N. M. Wasiuddin, and A. 

Peters. 2023. “MD Simulation of Waste Plastic-Asphalt 

Compatibility.” Presented at the 2023 AIChE Annual 

Meeting. Orlando, FL: AIChE.

3. Islam, M. R., and N. M. Wasiuddin. 2022. “Identification of 

Low Density Polyethylene, High Density Polyethylene, and 

Polypropylene in Asphalt Binder with a Hand-Held FTIR 

Spectrometer.” Proceedings of the 2022 Tran-SET 

Conference. Austin, TX: Transportation Consortium of 

South-Central States. https://ascelibrary.org/

doi/10.1061/9780784484609.002, last accessed 

November 2, 2023. 

4. Peters, A., and N. M. Wasiuddin. 2023. “A Coarse-Grained 

Model for Asphalt Binder and Polymer Mixtures Based on 

the MARTINI Forcefield.” To be presented at the 103rd 

Annual Meeting of the Transportation Research Board. 

Washington, DC: Transportation Research Board.

5. Rahman, S. M. R., R. Hossain, N. M. Wasiuddin, and 

A. Peters. 2022. “Understanding Phase Separation, Aging 

Characteristics, and SARA Fractions of HDPE, LDPE, and 

PP Modified Asphalt Binder.” Presented at 101st Annual 

Meeting of the Transportation Research Board. 

Washington, DC: Transportation Research Board.

6. Selim, S., M. R. Islam, N. M. Wasiuddin, A. Peters. 2023. 

“A Thermodynamic Approach to Investigate Compatibility 

of HDPE, LDPE, and PP Modified Asphalt Binders Using a 

Novel Differential Scanning Calorimeter (DSC) Method.” 

To be presented at the 103rd Annual Meeting of the 

Transportation Research Board. Washington, DC: 

Transportation Research Board.

Interagency Research

The following projects were jointly funded by the FHWA EAR Program and the National Science 

 Foundation (NSF).

AUGMENTED REALITY FOR CONTROL OF 

RESERVATION-BASED INTERSECTIONS WITH 

MIXED AUTONOMOUS NON-AUTONOMOUS FLOWS

UNIVERSITY OF TEXAS AUSTIN AND UNIVERSITY 

OF WASHINGTON 

If there were only fully automated vehicles, an intersection 

could, in theory, greatly reduce wait times and traffic 

congestion.(15)

 Yet even one manually driven vehicle can 

significantly reduce these benefits. Legacy vehicles will be 

on the road for years to come, so researchers at the 

University of Texas at Austin are examining how to 

accommodate both fully automated and manually driven 

vehicles on the road. The researchers will use an intersection 

control system to transmit a time reservation to fully 

automated vehicles and use augmented reality and a 

heads-up display, such as a projection on the windshield, to 

notify the human drivers to either speed up or slow down to 

drive through the intersection at their appointed time. This 

smart intersection concept would also incorporate 

nonmotorized travelers, who would be connected through 

augmented-reality goggles or their smartphones. These 

devices would then communicate to the pedestrian or the 

bicyclist when to cross. The researchers developed 

algorithms based on human behavior that help vehicles and 

humans safely and efficiently pass through an intersection. 

Then, the researchers experimented with these algorithms 

using human participants outfitted with augmented-reality 

devices in vehicles and on their person. (NSF award 17-39964) 

ACCEPTED FOR PUBLICATION 

1. Du, B., K. Qian, C. Claudel, and D. Sun. 2022. “JacobiStyle Iteration for Distributed Submodular Maximization.” 

IEEE Transactions on Automatic Control 67, no. 9. https://

ieeexplore.ieee.org/document/9789722, last accessed 

October 16, 2023.

2. Mohamed, A., and F. Lejarza. 2022. “HAR-GCNN: Deep 

Graph CNNs for Human Activity Recognition From Highly 

Unlabeled Mobile Sensor Data.” 2022 IEEE International 

Conference on Pervasive Computing and Communications

Workshops and other Affiliated Events (PerCom Workshops). 

Alexandria, VA: National Science Foundation. https://par.nsf.

gov/biblio/10386595, last accessed October 16, 2023. 

3. Mohamed, A., and D. Zhu. 2022. “Social-Implicit: 

Rethinking Trajectory Prediction Evaluation and The 

Effectiveness of Implicit Maximum Likelihood Estimation.” 

Presented at the European Conference on Computer 

Vision. Tel Aviv, Israel: European Computer Vision 

Association. 

IDENTIFYING, CHARACTERIZING, AND SHAPING 

MULTI-SCALE CYBER-HUMAN INTERACTIONS 

IN MIXED AUTONOMOUS/CONVENTIONAL 

VEHICLE TRAFFIC

UNIVERSITY OF WISCONSIN-MADISON 

Researchers at the University of Wisconsin-Madison are 

exploring what makes human drivers lose trust in vehicle 

automation, leading the driver to intervene unnecessarily.(15)

The researchers are exploring scenarios that include human 

drivers in both fully automated and manually controlled 

vehicles and how erroneous human interventions could 

negatively impact traffic flow. With more automated vehicles 

on the roadways in the upcoming decades, researchers are 

interested in increasing trust in automated vehicles, 

which would then help improve traffic flow. 

EXPLORATORY 

11

Compendium of Papers From Funded Research Projects

Waste Plastics in Asphalt Binder and Interagency Research
In the first phase of the project, the researchers are using a 

driving simulator and software to simulate traffic, providing 

the capability to test automated vehicle algorithms and 

human-driven vehicle simulations safely and efficiently. For 

the next phase of this project, the researchers are designing 

experiments to include human participants in controlled field 

tests. (NSF award 17-39869) 

ACCEPTED FOR PUBLICATION 

1. Elmquist, A., R. Serban, and D. Negrut. 2023. “Synthetic 

Image Generation for Robot Simulation: Quantifying the 

Impact of Model Modifications on Perception.” IEEE 

Sensors Journal 23, no. 16: 18304–18315. https://

ieeexplore.ieee.org/document/10163921, last accessed 

October 16, 2023.

2. Elmquist, A., R. Serban, and D. Negrut. 2022. 

“A Performance Contextualization Approach to Validating 

Camera Models for Robot Simulation.” https://arxiv.org/

abs/2208.01022, last accessed October 16, 2023. 

3. Kamaraj, A. V., J. Lee, J. E. Domeyer, S. Y. Liu, and J. D. 

Lee. 2023. “Comparing Subjective Similarity of Automated 

Driving Styles to Objective Distance-Based Similarity.” 

Human Factors. https://journals.sagepub.com/

doi/10.1177/00187208221142126, last accessed 

October 16, 2023.

4. Kamaraj, A. V., J. Lee, J. Parker, J. E. Domeyer, S. Y. Liu, 

and J. D. Lee. 2023. “Bimodal Trust: High and Low Trust in 

Vehicle Automation Influence Response to Automation 

Errors.” Proceedings of the Human Factors and 

Ergonomics Society Annual Meeting 67.

5. Unjhawala, H. M., R. Zhang, W. Hu, J. Wu, R. Serban, 

and D. Negrut. 2023. “Using a Bayesian-Inference 

Approach to Calibrating Models for Simulation in 

Robotics.” Journal of Computational and Nonlinear 

Dynamics 18, no. 6: 061004. https://asmedigitalcollection.

asme.org/computationalnonlinear/articleabstract/18/6/061004/1160342/Using-a-BayesianInference-Approach-to-Calibrating?redirectedFrom=fullte

xt, last accessed October 16, 2023.

6. Zhou, Y., X. Zhong, Q. Chen, S. Ahn, J. Jiang, and 

G. Jafarsalehi. 2023. “Data Driven Analysis for 

Disturbance Amplification in Car-Following Behavior of 

Automated Vehicles.” Transportation Research Part B

174: 102768. https://doi.org/10.1016/j.trb.2023.05.005, 

last accessed October 16, 2023. 

7. Zhou, Z. 2023. “An Open-Source Chrono-Based

Framework for Large-Scale Traffic Simulation With 

Human-in-the-Loop.” Master thesis. University of 

Wisconsin-Madison. http://dx.doi.org/10.13140/

RG.2.2.36555.36643, last accessed October 16, 2023. 

8. Zhou, Z., H. Unjhawala, A. V. Kamaraj, A. Kissel, J. D Lee, 

R. Serban, and D. Negrut, 2023. “A Chrono-Based 

Framework for Large-Scale Traffic Simulation with 

Human-In-The-Loop.” Proceedings of the Multibody 2023 

11th ECCOMAS Thematic Conference on Multibody 

Dynamics. Lisbon, Portugal: IDMEC. https://doi.

org/10.13140/RG.2.2.23133.59361, last accessed 

October 16, 2023. 

MAKING MICROMOBILITY SMARTER 

AND SAFER 

RUTGERS UNIVERSITY

The growth of micromobility vehicles (transportation 

devices such as pedal-driven and electric-assist bicycles as 

well as electric-assist scooters) in the United States over 

the past decade has been staggering.(16)

 From 2010 to 

2019, shared micromobility vehicle ridership ballooned from 

321,000 trips annually in 2010 to 136 million annually in 

2019.(17)

 In 2020, travelers in the United States took an 

estimated 67.9 million trips on shared micromobility vehicles.(18)

Driven by the rise of shared rentals deployed by municipalities 

and private companies, micromobility vehicles have become a 

popular transportation alternative for individuals in cities and, 

increasingly, in smaller towns and suburbs throughout the 

Nation. The types of micromobility vehicles available for use 

have also increased. As a result, policymakers and researchers 

have grappled with the implications of this growing mode of 

transit. In particular, public safety for micromobility vehicle 

users, as well as the pedestrians and drivers they encounter, 

has become an increasing concern. The Rutgers University 

research team seeks to gather better data and create 

technological tools that help improve safety for pedestrians 

and micromobility vehicle users. (NSF award 19-51890)

ACCEPTED FOR PUBLICATION

1. Fang, Z., G. Wang, Y. Yang, F. Zhang, Y. Wang, and D. 

Zhang. 2022. “A Long-Term Travel Delay Measurement 

Study Based on Multi-Modal Human Mobility Data.” 

Scientific Reports 12. https://doi.org/10.1038/s41598-022-

19394-z, last accessed October 16, 2023. 

© 2021 Rutgers University. E-scooters being used alongside 

car traffic.(16)

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

12

Interagency Research
2. Tan, H., Y. Yuan, S. Zhong, and Y. Yang. 2023. “Joint 

Rebalancing and Charging for Shared Electric 

Micromobility Vehicles with Human-System Interaction.” 

Proceedings of the ACM/IEEE 14th International 

Conference on Cyber-Physical Systems. New York, NY: 

Association for Computing Machinery. 235–236. https://

doi.org/10.1145/3576841.3589613, last accessed 

October 16, 2023. 

3. Wang, G., F. Zhang, and D. Zhang. 2023. “When Mobility 

on Demand Meets Vehicle Electrification: A Longitudinal 

Study on Evolution of City-Scale Ridesharing.” CCF 

Transactions on Pervasive Computing and Interaction 5: 

226–240. https://doi.org/10.1007/s42486-023-00125-w, 

last accessed October 16, 2023.

4. Wen, S., H. Wang, and D. Metaxas. 2022. “Social ODE: 

Multi-Agent Trajectory Forecasting with Neural Ordinary 

Differential Equations.” Computer Vision—ECCV 2022. 

ECCV 2022. Lecture Notes in Computer Science 13682. 

https://par.nsf.gov/biblio/10431064, last accessed 

October 16, 2023. 

5. Xie, X., Z. Hong, Z. Qin, Z. Fang, Y. Tian, and D. Zhang. 

2022. “TransRisk: Mobility Privacy Risk Prediction Based 

on Transferred Knowledge.” Proceedings of the ACM on 

Interactive, Mobile, Wearable and Ubiquitous Technologies 

6, no. 2: 1–19. https://doi.org/10.1145/3534581, last 

accessed October 16, 2023.

6. Zhong, S., W. Lyu, D. Zhang, and Y. Yang. 2022. 

“BikeCAP: Deep Spatial-Temporal Capsule Network for 

Multi-Step Bike Demand Prediction.” 2022 IEEE 42nd 

International Conference on Distributed Computing 

Systems (ICDCS). New York, NY: IEEE. 831–841. https://

doi.org/10.1109/ICDCS54860.2022.00085, last accessed 

October 16, 2023. 

HYBRID TWINS FOR URBAN TRANSPORTATION: 

FROM INTERSECTIONS TO CITYWIDE 

MANAGEMENT

COLUMBIA UNIVERSITY

Traffic management in metropolitan areas poses distinct 

challenges.(19)

 Faced with congested city streets shared by 

pedestrians, bicyclists, electric-scooter riders, and drivers, 

traffic managers must find new ways to maintain smooth 

and safe traffic flow. Traffic managers can harness the data 

collected by electronic devices that connect to the Internet, 

infrastructural sensors, other devices or systems, and 

communications networks in the Internet of Things (IoT) to 

transform urban streets. This 3-yr research project 

leverages the IoT to develop an urban traffic management 

system that will help improve traffic safety, mobility, and 

reliability. To explore the possibilities of this advanced traffic 

management system, the research team at Columbia 

University is creating a hybrid twin of an area of New York 

City, NY. (NSF award 20-38984)

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Angus, A., Z. Duan, G. Zussman, and Z. Kostic. 2022. 

“Real-Time Video Anonymization in Smart City 

Intersections.” IEEE 19th International Conference on 

Mobile Ad Hoc and Smart Systems (MASS). New York, NY: 

IEEE. 514–522. https://doi.org/10.1109/

MASS56207.2022.00078, last accessed October 16, 2023. 

2. Bautista-Montesano, R., R. Galluzzi, Z. Mo, Y. Fu, R. 

Bustamante-Bello, and X. Di. 2023. “Longitudinal Control 

Strategy for Connected Electric Vehicle with Regenerative 

Braking in Eco-Approach and Departure.” Applied 

Sciences 13, no. 8: 5089. https://doi.org/10.3390/

app13085089, last accessed October 16, 2023. 

3. Bautista-Montesano, R., R. Galluzzi, K. Ruan, Y Fu, and 

X. Di. 2022. “Autonomous Navigation at Unsignalized 

Intersections: A Coupled Reinforcement Learning and 

Model Predictive Control Approach.” Transportation 

Research Part C: Emerging Technologies 139: 103662. 

https://doi.org/10.1016/j.trc.2022.103662, last accessed 

October 16, 2023. 

4. Di, X., R. Shi, Z. Mo, and Y. Fu. 2023. “Physics-Informed 

Deep Learning for Traffic State Estimation: A Survey and 

the Outlook.” Algorithms 16, no. 6: 305. https://doi.

org/10.3390/a16060305, last accessed October 16, 2023. 

5. Du, Q., K. Huang, J. Scott, and W. Shen. 2023. “A 

Space-Time Nonlocal Traffic Flow Model: Relaxation 

Representation and Local Limit.” Discrete and 

Continuous Dynamical Systems 43, no. 9: 3456–3484. 

https://doi.org/10.3934/dcds.2023054, last accessed 

October 16, 2023.

6. Ghasemi, M., and S. Kleisarchaki. 2023. “Real-Time 

Multi-Camera Analytics for Traffic Information Extraction 

and Visualization.” Presented at IEEE PerCom ’23. 

Atlanta, GA: IEEE. https://par.nsf.gov/biblio/10451156, 

last accessed October 16, 2023. 

7. Ghasemi, M., S. Kleisarchaki, T. Calmant, J. Lu, S. Ojha, 

Z. Kostic, L. Gürgen, G. Zussman, and J. Ghaderi. 2023. 

“Real-Time Multi-Camera Analytics for Traffic Information 

Extraction and Visualization.” Presented at IEEE PerCom 

’23. Atlanta, GA: IEEE. https://par.nsf.gov/biblio/10406095, 

last accessed October 16, 2023. © 2022 Columbia University. The researchers are using 

cameras to capture traffic data.(19)

EXPLORATORY 

13

Compendium of Papers From Funded Research Projects

Interagency Research
8. Kostic, Z., A. Angus, Z. Yang, Z. Duan, I. Seskar, G. 

Zussman, and D. Raychaudhuri. 2022. “Smart City 

Intersections: Intelligence Nodes for Future Metropolises.” 

Computer 55, no. 12: 74–85. https://doi.org/10.1109/

MC.2022.3206273, last accessed October 16, 2023. 

9. Liu, S., Y. Wang, X. Chen, Y. Fu, and X. Di. 2022. 

“SMART-eFlo: An Integrated SUMO-Gym Framework for 

Multi-Agent Reinforcement Learning in Electric Fleet 

Management Problem.” 2022 IEEE 25th International 

Conference on Intelligent Transportation Systems (ITSC). 

New York, NY: IEEE. https://par.nsf.gov/biblio/10447569, 

last accessed October 16, 2023. 

10. Mo, Z., and X. Di. 2022. “Uncertainty Quantification of

Car-following Behaviors: Physics-Informed Generative 

Adversarial Networks.” The 28th ACM SIGKDD in 

conjunction with the 11th International Workshop on 

Urban Computing (UrbComp2022). https://par.nsf.gov/

biblio/10359038, last accessed October 16, 2023. 

11.Mo, Z., and Y. Fu. 2022. “TrafficFlowGAN: Physics-Informed 

Flow Based Generative Adversarial Network for 

Uncertainty Quantification.” Presented at the European 

Conference on Machine Learning and Data Mining 

(ECML PKDD). Torino, Italy. https://par.nsf.gov/

biblio/10359039, last accessed October 16, 2023. 

12.Mo, Z., W. Li, Y. Fu, K. Ruan, and X. Di. 2022. “CVLight: 

Decentralized Learning for Adaptive Traffic Signal 

Control with Connected Vehicles.” Transportation 

Research Part C: Emerging Technologies 141: 103728. 

https://doi.org/10.1016/j.trc.2022.103728, last accessed 

October 16, 2023.

References

1. FHWA. 2020. Safer, More Reliable Transportation with 

Behavioral Economics: Cellphone Use and Managed 

Lane Choice. Publication No. FHWA-HRT-20-013. 

Washington, DC: Federal Highway Administration. https://

www.fhwa.dot.gov/publications/research/ear/20013/20013.

pdf, last accessed September 28, 2023.

2. FHWA. 2022. The Role of Artificial Intelligence and 

Machine Learning in Federally Supported Surface 

Transportation: 2022 Updates. Report No. FHWAHRT-22-026. Washington, DC: Federal Highway 

Administration. https://www.fhwa.dot.gov/publications/

research/ear/22026/22026.pdf, last accessed 

September 28, 2023.

3. FHWA. 2015. Video Analytics Research Projects. Report 

No. FHWA-HRT-15-025. Washington, DC: Federal 

Highway Administration. https://www.fhwa.dot.gov/

publications/research/ear/15025/index.cfm, last accessed 

November 21, 2022.

4. Virginia Tech Transportation Institute. 2020. “InSight Data 

Access Website: SHRP2 Naturalistic Driving Study” 

(website). https://insight.shrp2nds.us/, last accessed 

September 28, 2023.

5. Al-Shmaisani, S., and M. Juenger. 2020. “What Does the 

Changing Face of Electricity Production Mean for 

Concrete?” Public Roads 83, no. 4. https://highways.dot.

gov/public-roads/winter-2020/what-does-changing-faceelectricity-production#:~:text=Table%20of%20Contents-

,What%20Does%20the%20Changing%20Face%20of%20

Electricity%20Production%20Mean%20for,industry%20

is%20looking%20for%20alternatives, last accessed 

September 28, 2023.

6. FHWA. 2020. Supplementary Cementitious Material 

Advancements: Helping to Make Longer Lasting Highway 

Concrete Highways and Transportation Structures. 

Publication No. FHWA-HRT-20-048. Washington, DC: 

Federal Highway Administration. https://www.fhwa.dot.gov/

publications/research/ear/20048/20048.pdf, last accessed 

September 6, 2023.

7. FHWA. 2014. EAR Program Research Results: Updated 

through 2014.” Publication No. FHWA-HRT-15-024. 

Washington, DC: Federal Highway Administration. https://

www.fhwa.dot.gov/publications/research/ear/15024/15024.

pdf, last accessed September 6, 2023.

8. FHWA. Forthcoming. Predictive Real-Time Management 

in Large-Scale Networks Using Model-Based Artificial 

Intelligence. Publication No. FHWA-HRT-23-107. 

Washington, DC: Federal Highway Administration.

9. FHWA. n.d. “Traffic Incident Detection and Analysis 

System (TIDAS)” (web page). https://highways.dot.gov/

research/projects/traffic-incident-detection-and-analysissystem-tidas, last accessed September 29, 2023.

10. FHWA. 2021. Implementation of AI Framework to Improve 

Winter Maintenance. Publication No. FHWA-HRT-21-090. 

Washington, DC: Federal Highway Administration. https://

www.fhwa.dot.gov/publications/research/ear/21090/21090.

pdf, last accessed September 29, 2023.

11. Edara, P., C. Sun, H. Brown, P. Savolainen, V. Shankar, 

B. Balakrishnan, Y. Shang, et al. 2023. Multidisciplinary 

Initiative to Create and Integrate Realistic Artificial 

Datasets. Report No. FHWA-HRT-23-058. Washington, 

DC: Federal Highway Administration. https://highways.dot.

gov/sites/fhwa.dot.gov/files/FHWA-HRT-23-058.pdf, last 

accessed October 2, 2023.

12. FHWA. 2020. Realistic Artificial Datasets: Objective 

Evaluation of Data-Driven Safety Analysis Models. 

Publication No. FHWA-HRT-20-047. Washington, DC: 

Federal Highway Administration. https://www.fhwa.dot.gov/

publications/research/ear/20047/20047.pdf, last accessed 

October 2, 2023.

13. FHWA. 2021. Securing Vehicle-to-Vehicle 

Communication Through Consortium Blockchain. 

Publication No. FHWA-HRT-21-048. Washington, DC: 

Federal Highway Administration. https://www.fhwa.dot.gov/

publications/research/ear/21048/21048.pdf, last accessed 

October 2, 2023.

14. FHWA. 2021. Improving the Compatibility of Waste. 

Publication No. FHWA-HRT-21-084. Washington, DC: 

Federal Highway Administration. https://www.fhwa.dot.gov/

publications/research/ear/21084/21084.pdf, last accessed 

October 2, 2023.

EXPLORATORY ADVANCED RESEARCH

Compendium of Papers From Funded Research Projects

14

Interagency Research and References
15.FHWA. 2020. Preparing Our Nation’s Roadways 

for Advanced Vehicle Technologies. Publication No. 

FHWA-HRT-20-049. Washington, DC: Federal Highway 

Administration. https://www.fhwa.dot.gov/publications/

research/ear/20049/20049.pdf, last accessed 

October 2, 2023.

16. FHWA. 2022. Making Micromobility Smarter and Safer. 

Publication No. FHWA-HRT-22-083. Washington, DC: 

Federal Highway Administration. https://highways.dot.gov/

sites/fhwa.dot.gov/files/FHWA-HRT-22-083.pdf, last 

accessed October 2, 2023.

17. National Association of City Transportation Officials. 2020. 

Shared Micromobility in the U.S.: 2019. New York, NY: 

National Association of City Transportation Officials. 

https://nacto.org/shared-micromobility-2019/, last 

accessed April 18, 2022.

18. North American Bikeshare and Scootershare Association. 

2021. 2nd Annual Shared Micromobility State of the 

Industry Report. Portland, ME: North American Bikeshare 

and Scootershare Association. https://share.hsforms.

com/19DY9nNTJQemTS57h3SD6CA55271, last 

accessed May 18, 2022.

19.FHWA. 2022. Developing Future Traffic Management 

Systems Using Hybrid Twins. Publication No. FHWAHRT-22-089. Washington, DC: Federal Highway 

Administration. https://highways.dot.gov/sites/fhwa.dot.

gov/files/FHWA-HRT-22-089.pdf, last accessed 

October 2, 2023.

EXPLORATORY 

15

Compendium of Papers From Funded Research Projects

References
FHWA-HRT-24-020

HRTM-30/12-23(50)E

Recommended Citation: Federal Highway Administration,

Exploratory Advanced Research (EAR) Program Compendium of 

Papers from Funded Research Projects (Washington, DC: 2023) 

https://doi.org/10.21949/1521903

EXPLORATORY ADVANCED RESEARCH

EAR Program Results

The EAR Program strives to develop partnerships with the public and private sectors 

because the very nature of the EAR Program is to apply ideas across traditional 

fields of research and stimulate new approaches to problemsolving. The program 

bridges basic research (e.g., academic work funded by National Science Foundation 

grants) and applied research (e.g., studies funded by State departments of 

transportation). In addition to sponsoring EAR Program projects that advance the 

development of highway infrastructure and operations, the EAR Program is 

committed to promoting cross-fertilization with other technical fields, furthering 

promising lines of research, and deepening vital research capacity.

Getting Involved with the EAR Program

To take advantage of a broad variety of scientific and engineering discoveries, the EAR 

Program involves both traditional stakeholders (State department of transportation 

researchers, University Transportation Center researchers, and Transportation 

Research Board committee and panel members) and nontraditional stakeholders 

(investigators from private industry, related disciplines in academia, and research 

programs in other countries) throughout the research process.

The program has awarded 101 research projects on 57 different topics between 2007 

and 2021. The research awards include work by multidisciplinary teams at 80 academic 

institutions, 57 private companies, 13 State and local agencies, 10 Federal laboratories, 

and 10 foreign institutions.

Learn More

For more information, see the EAR Program website at https://highways.dot.gov/

research/exploratory-advanced-research. The site features information on research 

solicitations, updates on ongoing research, links to published materials, summaries 

of past EAR Program events, and details on upcoming events.