Long-Term Infrastructure Performance (LTIP) Student Data Analysis Contest
Encouraging Undergraduate and Graduate Students To Explore a Career in Pavement Engineering.
The Federal Highway Administration (FHWA) is pleased to announce the LTIP Student Data Analysis Contest, designed to encourage university students to use pavement performance data to study the various factors affecting pavement lifecycles and to develop a technical paper to document their research.
Students must use LTPP InfoPave™ to investigate a topic that advances pavement performance. Some suggested topics include “Concerns Faced by a Region or State,” “Multidisciplinary Approach To Address Transportation Infrastructure Resilience and Sustainability,” “Condition Forecasting Models,” and “Role of Preservation in Extending Service Life of Highway Infrastructure Assets.” Papers should use the following Transportation Research Board (TRB) formatting requirements:
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Format papers as Adobe® Acrobat™ PDFs.
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Submit all papers in English.
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Include a title page with title, authors, affiliations, and word count.
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Include all coauthor names, affiliations, and email addresses.
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Embed tables and figures in the text, near the text that discusses the item.
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Use an 8.5-inch-by-11-inch page with normal margins.
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Capitalize the first letter of each word in the title, except for conjunctions, prepositions, and articles.
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Use Times New Roman font, 10-point size or larger.
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Single space all papers.
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Number lines (restart numbering on each page).
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Number pages.
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Do not exceed 7,500 words, including the abstract, text, acknowledgments, references, and tables. Each table counts as 250 words. Papers not meeting this requirement may be withdrawn from the peer review process at any time. Abstracts should not exceed 250 words.
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Number all references and call them out in numerical order in the text.
Please note that students should not have their names in the header or footer, and they should not include acknowledgments in the body of the paper. However, students may recognize other individuals or organizations in the cover letter.
View the sample paper taken from TRB. Please note that while the sample paper refers to TRB in the title, this is not a TRB paper. FHWA is sponsoring this contest. Submit papers by email to LTIPStudentContest@dot.gov by Friday, August 1, 2025, 11:59 p.m. EST.
We look forward to your participation in this year’s contest. For more information, contact Deborah Walker at 202–493–3068 or deborah.walker@dot.gov.
Winners of the 2024 Student Data Analysis Contest
Four papers were selected for awards in the 2024 LTIP Student Data Analysis Contest. The student winners are:
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Shadi Azad and Dr. Shafei Behrouz from Iowa State University.
First Place (Bridge): Data-Enabled Joint Condition Assessment of Bridges with Integral Abutments and Tied Approach Slabs.
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Ruohan Li, Dr. Jorge A. Prozzi, and Dr. Feng Hong from University of Texas at Austin.
First Place (Pavement): Quantification of Post-Rainfall Moisture Content in Unbound Layers Using LTPP Data.
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Faizan Ahmad Lali and Dr. Syed Waqar Haider from Michigan State University.
Second Place (Pavement): Verifying Existing HMA Characterization in Mechanistic-Empirical Pavement Design (PMED) Using the LTPP Data.
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Anthony Brenes-Calderon, Josue Garita-Jimenez, Dr. Adriana Vargas-Nordbeck, and Dr. Surendra Chowdari Gatiganti from Auburn University.
Third Place (Pavement): Holistic Assessment of Asphalt Pavement Preventive Maintenance: Determining Environmental, Economic and Performance Benefits of Pavement Preservation Treatments.
Winners of the 2023 Student Data Analysis Contest
Three papers were selected for awards in the 2023 LTIP Student Data Analysis Contest. The following students were the winners:
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Lawrencia Akuffo, Dr. Adriana Trias Blanco from Rowan University.
First Place (Bridge): Quantification of the Correlation Between Bridge Skew Angle and Deterioration Rate.
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Bingyan Cui, Xiao Chen, Zhe Wan, and Hao Wang from Rutgers University.
First Place (Pavement): Predicting Asphalt Pavement Deterioration under Climate Change Uncertainty Using Bayesian Neural Network.
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Jian Liu, Daodao Zhou, Fangyu Liu, and Dr. Linbing Wang from Virginia Polytechnic Institute and State University.
Second Place (Pavement): Accelerated balanced asphalt mix design based on Machine learning and non-dominated Sorting genetic algorithm-II (NSGA-II).
Winners of the 2022 Student Data Analysis Contest
Four papers were selected for awards in the 2022 LTIP Student Data Analysis Contest. The following students were the winners:
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Chan Yang, Drs. Peng Lou and Hani Nassif from Rutgers University.
First Place (Bridge): Correlation of Bridge Deck Deterioration With Truck Load Spectra Based on Weigh-in-Motion Data.
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Chuang Chen, Yong Deng, Drs. Mengyan Li and Xianming Shi from Washington State University.
First Place (Pavement): Key Climatic Factors Affecting Asphalt Pavement Roughness Differ in Different Climate Regions: Exploratory Analyses.
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Jian Liu and Dr. Linging Wang from Virginia Polytechnic Institute and State University.
Second Place (Pavement): Optimizing Asphalt Mix Design Considering IRI of Asphalt Pavement Predicted Using Autoencoders and Machine Learning.
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Muhamad Munum Masud and Dr. Syed Waqar Haider from Michigan State University.
Third Place (Pavement): “Relationship Between Gross Vehicle Weight With Commercial Freight Tonnage—Case Studies Based on LTPP WIM Data.
Winners of the Inaugural 2020–2021 Student Data Analysis Contest
Four papers were selected for awards in the first LTIP Student Data Analysis Contest. The following students were the winners:
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Agnimitra Sengupta, Drs. Sudeepta Mondal, S. Ilgin Guler, Parisa Shokouh from Pennsylvania State University.
First Place (Bridge): A State-Based Hidden Markov Model Approach to Impact Echo Signal Classification.
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Miaomiao Zhang, Hongren Gong, Yuetan Ma, Xi Jiang, and Baoshan Huang from University of Tennessee.
First Place (Pavement): Nomogram for Predicting Asphalt Pavement Roughness After Preventive Maintenance Based on LTPP Longitudinal Analysis.
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Muhammad Munum Masud from Michigan State University.
Second Place (Pavement): Guidelines for Effective Weigh-in-Motion (WIM) Equipment Calibration, Application for Modeling WIM Errors, and Comparison of the ASTM and LTPP Protocols.
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Greg Seleznev from Howard Community College.
Third Place (Pavement): Diagnosing Changes in WIM Measurement Accuracy and WIM Calibration Needs Using Data Visualization and Statistical Modeling Tools.
