Artificial Intelligence Deployment Center of Excellence (AI COE)
The AI COE Collection is a central resource for transportation stakeholders that may aid in deployment of safe and responsible AI in surface transportation. The collection will feature current and emerging resources, innovations, and technologies. For more information on the AI COE, go to https://highways.dot.gov/ai-coe.
In the past few years, computer vision has made huge jumps due to deep learning which leverages increased computational power and access to data. The computer vision community has also embraced transparency to accelerate research progress by sharing open datasets and open source code. Access to large scale datasets and bench- mark challenges propel
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Stanik, P. I., & Morris, B. (2022). Real-Time Semantic Segmentation for Railway Anomalies Analysis (Report No. UNLV-8). University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability. https://rosap.ntl.bts.gov/view/dot/78334
Stanik, Paul III and Brendan Morris. Real-Time Semantic Segmentation for Railway Anomalies Analysis. Report no. UNLV-8. University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability, 2022. https://rosap.ntl.bts.gov/view/dot/78334.
Stanik, Paul III, and Brendan Morris Real-Time Semantic Segmentation for Railway Anomalies Analysis. University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability, 2022, Report no. UNLV-8, ROSA P. https://rosap.ntl.bts.gov/view/dot/78334.
This report presents research results and experiments that were designed to compare four traffic systems: Radar, AVC, HERE, and INRIX in terms of speed, volume, and travel time. The first three chapters introduce the systems, related studies, data collection procedures, and preprocessing techniques. The second three chapters detail the comparison r
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Refai, H. H., Habib, M. A., Li, J. Q., & Liu, W. (2022). ODOT Radar System for Real-Time Traffic Flow Monitoring (Report No. FHWA-OK-22-05). Oklahoma. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/68293
Refai, Hazem H., Mohamed Ali Habib, Joshua Qiang Li, and Wenyao Liu. ODOT Radar System for Real-Time Traffic Flow Monitoring. Report no. FHWA-OK-22-05. Oklahoma. Department of Transportation, 2022. https://rosap.ntl.bts.gov/view/dot/68293.
Refai, Hazem H., et al. ODOT Radar System for Real-Time Traffic Flow Monitoring. Oklahoma. Department of Transportation, 2022, Report no. FHWA-OK-22-05, ROSA P. https://rosap.ntl.bts.gov/view/dot/68293.
Technology that connects human experience with artificial intelligence is increasingly reaching every sector of society - including Florida's transportation network. To stay ahead of this curve, the Florida Department of Transportation (FDOT) currently has 33 deployed projects that explore this interconnectivity through Connected Vehicle (CV) techn
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Ponnaluri, R., & Alluri, P. (2022). Performance Evaluation of Connected Vehicle (CV) and Transportation Systems Management and Operations (TSM&O) Projects in Florida [Summary]. Florida Department of Transportation. https://rosap.ntl.bts.gov/view/dot/70440
Ponnaluri, Raj and Priyanka Alluri. Performance Evaluation of Connected Vehicle (CV) and Transportation Systems Management and Operations (TSM&O) Projects in Florida [Summary]. Florida Department of Transportation, 2022. https://rosap.ntl.bts.gov/view/dot/70440.
Ponnaluri, Raj, and Priyanka Alluri Performance Evaluation of Connected Vehicle (CV) and Transportation Systems Management and Operations (TSM&O) Projects in Florida [Summary]. Florida Department of Transportation, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/70440.
Connected vehicle (CV) technologies are entering the realm of deployment. They have the potential to help drivers make safe, reliable, and informed decisions, and thereby to enhance network capacity and reduce congestion. During the transition to CV technologies, there will be mixed-flow traffic streams of CVs and non-CVs, i.e., human-driven vehicl
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Liu, Y., Zhou, A., Wang, Y., & Peeta, S. (2022). Non-Connected Vehicle Detection Using Connected Vehicles (Report No. CCAT Report #24). University of Michigan. Center for Connected and Automated Transportation. http://dx.doi.org/10.5703/1288284317646
Liu, Yongyang, Anye Zhou, Yu Wang, and Srinivas Peeta. Non-Connected Vehicle Detection Using Connected Vehicles. Report no. CCAT Report #24. University of Michigan. Center for Connected and Automated Transportation, 2022. http://dx.doi.org/10.5703/1288284317646.
Liu, Yongyang, et al. Non-Connected Vehicle Detection Using Connected Vehicles. University of Michigan. Center for Connected and Automated Transportation, 2022, Report no. CCAT Report #24, ROSA P. http://dx.doi.org/10.5703/1288284317646.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
2022-11-10
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Access to ITS data is critical for public agencies, private companies, and academia to conduct research and create new ITS innovations and services. Consistent and timely access to data increases the return on federal investment in ITS research and demonstration projects while helping to inform future investment decisions, which speeds deployment o
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United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office (2022). ITS Data Access and Exchanges. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/65753
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. ITS Data Access and Exchanges. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022. https://rosap.ntl.bts.gov/view/dot/65753.
United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office ITS Data Access and Exchanges. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/65753.
Coastal bridges are critical to emergency response after extreme events and are vulnerable to cascading seismic-tsunami events. After the 2011 earthquake and subsequent tsunami in Japan, instances of damage and collapse were observed in Japanese bridges that survived the earthquake but failed under the hydrodynamic loads induced by the tsunami. The
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Dataset
Simpson, B. G., & Scott, M. H. (2022). Pilot Study: Machine Learning and Deep Learning Study for Fluid Structure Interaction Problems [Supporting Dataset] (Report No. 2019-S-OSU-2). Pacific Northwest Transportation Consortium (PacTrans) (UTC). https://doi.org/10.7910/DVN/OGK2SQ
Simpson, Barbara G and Michael H. Scott. Pilot Study: Machine Learning and Deep Learning Study for Fluid Structure Interaction Problems [Supporting Dataset]. Report no. 2019-S-OSU-2. Pacific Northwest Transportation Consortium (PacTrans) (UTC), 2022. https://doi.org/10.7910/DVN/OGK2SQ.
Simpson, Barbara G, and Michael H. Scott Pilot Study: Machine Learning and Deep Learning Study for Fluid Structure Interaction Problems [Supporting Dataset]. Pacific Northwest Transportation Consortium (PacTrans) (UTC), 2022, Report no. 2019-S-OSU-2, ROSA P. https://doi.org/10.7910/DVN/OGK2SQ.
In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a vi
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Kulhandjian, H. (2022). AI-Based Pedestrian Detection and Avoidance at Night Using an IR Camera, Radar, and a Video Camera (Report No. 22-53). San Jose State University. College of Business. Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/65596
Kulhandjian, Hovannes. AI-Based Pedestrian Detection and Avoidance at Night Using an IR Camera, Radar, and a Video Camera. Report no. 22-53. San Jose State University. College of Business. Mineta Transportation Institute, 2022. https://rosap.ntl.bts.gov/view/dot/65596.
Kulhandjian, Hovannes AI-Based Pedestrian Detection and Avoidance at Night Using an IR Camera, Radar, and a Video Camera. San Jose State University. College of Business. Mineta Transportation Institute, 2022, Report no. 22-53, ROSA P. https://rosap.ntl.bts.gov/view/dot/65596.
The Federal Aviation Administration (FAA) rigid pavement design process is based on bottom-up cracking failure resulting from tensile stress at the bottom of a flat slab under aircraft loads. The FAA has a long-term goal to add top-down cracking failure mode to the FAA Rigid and Flexible Iterative Elastic Layered Design (FAARFIELD) program. The exi
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Ashtiani, A. Z., Paniagua, T., Parsons, T. (., & Foderaro, G. (2022). Machine Learning Solutions for Top-Down Cracking Design of Airport Rigid Pavement (Report No. DOT/FAA/TC-22/44). United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center. https://doi.org/10.21949/1524514
Ashtiani, Ali Z, Thomas Paniagua, Timothy (Tim) Parsons, and Greg Foderaro. Machine Learning Solutions for Top-Down Cracking Design of Airport Rigid Pavement. Report no. DOT/FAA/TC-22/44. United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center, 2022. https://doi.org/10.21949/1524514.
Ashtiani, Ali Z, et al. Machine Learning Solutions for Top-Down Cracking Design of Airport Rigid Pavement. United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center, 2022, Report no. DOT/FAA/TC-22/44, ROSA P. https://doi.org/10.21949/1524514.
Weigh-in-Motion (WIM) is a promising solution to regulate weight-related violations but has high installation and recurring maintenance costs. Also, the requirement of installing induction loops for vehicle presence detection may cause damage to pavement structure and lead to early deterioration of the pavement surface. Recently, virtual weigh-in-m
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Dai, F., Sujon, M., & Wang, L. (2022). Development of a Virtual Weigh-In-Motion System for Enhanced Pavement System Management (Report No. CIAM-UTC-REG32). Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC). https://rosap.ntl.bts.gov/view/dot/78195
Dai, Fei, Mohhammad Sujon, and Linbing Wang. Development of a Virtual Weigh-In-Motion System for Enhanced Pavement System Management. Report no. CIAM-UTC-REG32. Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC), 2022. https://rosap.ntl.bts.gov/view/dot/78195.
Dai, Fei, et al. Development of a Virtual Weigh-In-Motion System for Enhanced Pavement System Management. Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC), 2022, Report no. CIAM-UTC-REG32, ROSA P. https://rosap.ntl.bts.gov/view/dot/78195.
Despite its challenges, independent travel for blind and visually impaired (BVI) individuals is an essential component of quality of life, enabling travel to work and recreational activities. Autonomous vehicle technologies have the potential of meeting these challenges. However, efficiently and safely guiding BVI travelers between indoor environme
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Li, B., Comert, G., Brooks, J., & Arditi, A. (2022). Safe and Efficient E-wayfinding (SeeWay) Assistive Navigation for the Visually Impaired. Center for Connected Multimodal Mobility, Clemson University. https://rosap.ntl.bts.gov/view/dot/67816
Li, Bing, Gurcan Comert, Johnell Brooks, and Aries Arditi. Safe and Efficient E-wayfinding (SeeWay) Assistive Navigation for the Visually Impaired. Center for Connected Multimodal Mobility, Clemson University, 2022. https://rosap.ntl.bts.gov/view/dot/67816.
Li, Bing, et al. Safe and Efficient E-wayfinding (SeeWay) Assistive Navigation for the Visually Impaired. Center for Connected Multimodal Mobility, Clemson University, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/67816.
Artificial Intelligence (AI), including Machine Learning (ML), offers the opportunity to make transportation systems safer, and more equitable, reliable, accessible, secure, efficient, and resilient. However, several challenges exist that could impede the successful adoption of AI for Intelligent Transportation Systems (ITS) and the potential reali
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Vasudevan, M., Townsend, H., Samach, M., Walsh, P. T., Wang, P., Seshadri, A., & McManus, I. (2022). Artificial Intelligence (AI) for Intelligence Transportation Systems (ITS) Challenges and Potential Solutions, Insights, and Lessons Learned (Report No. FHWA-JPO-22-968). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office. https://rosap.ntl.bts.gov/view/dot/66971
Vasudevan, Meenakshy, Haley Townsend, Matt Samach, Patrick T. Walsh, Peiwei Wang, Anand Seshadri, and Ian McManus. Artificial Intelligence (AI) for Intelligence Transportation Systems (ITS) Challenges and Potential Solutions, Insights, and Lessons Learned. Report no. FHWA-JPO-22-968. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022. https://rosap.ntl.bts.gov/view/dot/66971.
Vasudevan, Meenakshy, et al. Artificial Intelligence (AI) for Intelligence Transportation Systems (ITS) Challenges and Potential Solutions, Insights, and Lessons Learned. United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office, 2022, Report no. FHWA-JPO-22-968, ROSA P. https://rosap.ntl.bts.gov/view/dot/66971.
Mobile sensing is an alternative paradigm that offers numerous advantages compared to the conventional stationary sensor networks. Mobile sensors have low setup costs, collect spatial information efficiently, and require no dedicated sensors to any particular structure. Most importantly, they can capture comprehensive spatial information using few
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Pakzad, S. N., & Takac, M. (2022). Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges (Report No. CIAM-UTC-REG20). Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC). https://rosap.ntl.bts.gov/view/dot/64792
Pakzad, Shamim N and Martin Takac. Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges. Report no. CIAM-UTC-REG20. Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC), 2022. https://rosap.ntl.bts.gov/view/dot/64792.
Pakzad, Shamim N, and Martin Takac Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges. Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC), 2022, Report no. CIAM-UTC-REG20, ROSA P. https://rosap.ntl.bts.gov/view/dot/64792.
Transportation asset management needs timely information collection to inform relevant maintenance practices (e.g., resource planning). Traditional data collection methods in transportation asset management require either manual operation or support of unique equipment (e.g., Light Detection and Ranging (LiDAR)), which could be labor-intensive or c
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Kuang, B., Chen, J., & Yang, X. (. (2022). Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management (Report No. UT-22.24). Utah. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/66158
Kuang, Biao, Jianli Chen, and Xianfeng (Terry) Yang. Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management. Report no. UT-22.24. Utah. Department of Transportation, 2022. https://rosap.ntl.bts.gov/view/dot/66158.
Kuang, Biao, et al. Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management. Utah. Department of Transportation, 2022, Report no. UT-22.24, ROSA P. https://rosap.ntl.bts.gov/view/dot/66158.
In this study, the authors develop a comprehensive framework to model the impact of cyberattacks on safety, security, and head-to-tail stability of connected and automated vehicular platoons. First, the authors propose a general platoon dynamics model with heterogeneous time delays that may originate from the communication channel and/or vehicle on
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Wang, Y., Zhang, R., Masoud, N., & Liu, H. X. (2022). Anomaly Detection and String Stability Analysis in Connected Automated Vehicular Platoons. University of Michigan. https://rosap.ntl.bts.gov/view/dot/68522
Wang, Yiyang, Ruixuan Zhang, Neda Masoud, and Henry X. Liu. Anomaly Detection and String Stability Analysis in Connected Automated Vehicular Platoons. University of Michigan, 2022. https://rosap.ntl.bts.gov/view/dot/68522.
Wang, Yiyang, et al. Anomaly Detection and String Stability Analysis in Connected Automated Vehicular Platoons. University of Michigan, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/68522.
The updated information about the location and type of landing sites is essential for the Federal Aviation Administration (FAA) and the Department of Transportation (DOT). However, the acquisition, verification, and regular updating of information about landing sites are not straightforward. The lack of current and correct information on helicopter
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Dataset
Rasool, G., Bouaynaya, N., Nasir, A., & Koutsoubis, N. (2022). Rotorcraft Landing Sites Identification - Scaling and Generalization of the AI Model [Supporting Dataset] (Report No. CAIT-UTC-REG54). Rutgers University. Center for Advanced Infrastructure and Transportation. https://doi.org/10.7910/DVN/ZCQNYS
Rasool, Ghulam, Nidhal Bouaynaya, Abdullah Nasir, and Nikolas Koutsoubis. Rotorcraft Landing Sites Identification - Scaling and Generalization of the AI Model [Supporting Dataset]. Report no. CAIT-UTC-REG54. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022. https://doi.org/10.7910/DVN/ZCQNYS.
Rasool, Ghulam, et al. Rotorcraft Landing Sites Identification - Scaling and Generalization of the AI Model [Supporting Dataset]. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022, Report no. CAIT-UTC-REG54, ROSA P. https://doi.org/10.7910/DVN/ZCQNYS.
Express lanes mitigate traffic congestion by providing a time-reliable alternative and exploiting travelers' willingness to pay to generate revenue for infrastructure projects. Over the last decade, equity and fairness issues for express lanes have been considered; however, there is a lack of guidance on the design of equitable discounts. In this a
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Pandey, V., Alamri, B. G., Khoury, H., & Bakre, M. (2022). Equitable Dynamic Pricing of Express Lanes (Report No. CATM-2022-R9-NCAT). North Carolina A&T Sate University. Transportation Institute. Center For Advanced Transportation Mobility. https://rosap.ntl.bts.gov/view/dot/64671
Pandey, Venktesh, Bader G Alamri, Hadi Khoury, and Mary Bakre. Equitable Dynamic Pricing of Express Lanes. Report no. CATM-2022-R9-NCAT. North Carolina A&T Sate University. Transportation Institute. Center For Advanced Transportation Mobility, 2022. https://rosap.ntl.bts.gov/view/dot/64671.
Pandey, Venktesh, et al. Equitable Dynamic Pricing of Express Lanes. North Carolina A&T Sate University. Transportation Institute. Center For Advanced Transportation Mobility, 2022, Report no. CATM-2022-R9-NCAT, ROSA P. https://rosap.ntl.bts.gov/view/dot/64671.
With the accelerated deployment of connected and automated vehicle (CAV) technologies, public agencies have urgent needs on how to utilize these rich data sources of CAVs to improve traffic mobility, safety, and environmental and energy impact. This research will tackle one of the big data challenges, which is mining driving behavior patterns using
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Dataset
Di, X., Jin, P., Huang, Y., & Mo, Z. (2022). Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub [Supporting Dataset] (Report No. CAIT-UTC-REG46). Rutgers University. Center for Advanced Infrastructure and Transportation. https://doi.org/10.7910/DVN/XIHFDG
Di, Xuan, Peter Jin, Yufei Huang, and Zhaobin Mo. Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub [Supporting Dataset]. Report no. CAIT-UTC-REG46. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022. https://doi.org/10.7910/DVN/XIHFDG.
Di, Xuan, et al. Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub [Supporting Dataset]. Rutgers University. Center for Advanced Infrastructure and Transportation, 2022, Report no. CAIT-UTC-REG46, ROSA P. https://doi.org/10.7910/DVN/XIHFDG.
Considering traffic safety concerns and challenging driving conditions on Interstate 80 in Wyoming, the United States Department of Transportation (USDOT) and Federal Highway Administration (FHWA) selected an I-80 400- mile freeway corridor as one of the three sites in the United States to develop, test, and deploy a suite of connected vehicle (CV)
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Ahmed, M. M., Gaweesh, S., & Bakhshi, A. K. (2022). Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives (Report No. MPC 22-489). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/86957
Ahmed, Mohamed M., Sherif Gaweesh, and Arash Khoda Bakhshi. Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives. Report no. MPC 22-489. Mountain-Plains Consortium, 2022. https://rosap.ntl.bts.gov/view/dot/86957.
Ahmed, Mohamed M., et al. Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives. Mountain-Plains Consortium, 2022, Report no. MPC 22-489, ROSA P. https://rosap.ntl.bts.gov/view/dot/86957.
This study provides a new traffic safety perspective for the safety performance evaluation of the WYDOT CV Pilot through advanced statistical modeling, machine learning, data mining applications, safety data visualizations, high-fidelity driving simulator experiments, and traffic microsimulation modeling. Analytical inference for developing a basel
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Ahmed, M. M., Gaweesh, S., & Bakhshi, A. K. (2022). Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives [Research Brief] (Report No. MPC-22-489). Mountain-Plains Consortium. https://rosap.ntl.bts.gov/view/dot/87081
Ahmed, Mohamed M., Sherif Gaweesh, and Arash Khoda Bakhshi. Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives [Research Brief]. Report no. MPC-22-489. Mountain-Plains Consortium, 2022. https://rosap.ntl.bts.gov/view/dot/87081.
Ahmed, Mohamed M., et al. Assessing the Effectiveness of the Wyoming Connected Vehicle Pilot Program: New Traffic Safety Research Perspectives [Research Brief]. Mountain-Plains Consortium, 2022, Report no. MPC-22-489, ROSA P. https://rosap.ntl.bts.gov/view/dot/87081.
Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering
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Huang, H., Tutumluer, E., Luo, J., Ding, K., Qamhia, I., & Hart, J. M. (2022). 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates-Phase 2 (Report No. FHWA-ICT-22-013). Illinois. Department of Transportation. https://doi.org/10.36501/0197-9191/22-017
Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John M. Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates-Phase 2. Report no. FHWA-ICT-22-013. Illinois. Department of Transportation, 2022. https://doi.org/10.36501/0197-9191/22-017.
Huang, Haohang, et al. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates-Phase 2. Illinois. Department of Transportation, 2022, Report no. FHWA-ICT-22-013, ROSA P. https://doi.org/10.36501/0197-9191/22-017.
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