Mesa-Arango, Rodrigo
Rodrigo Mesa-Arango
Assistant Professor | College of Engineering and Science: Department of Mechanical and Civil Engineering
Contact Information
Expertise
Personal Overview
Rodrigo Mesa-Arango is a transportation engineering researcher and educator with a Ph.D. in Transportation Engineering and Infrastructure Systems from Purdue University.
He currently serves as an Assistant Professor at Florida Institute of Technology, where his work focuses on transportation planning, intelligent transportation systems, freight logistics, econometrics, machine learning, and data analytics.
His research encompasses intelligent transportation systems, statistical and behavioral modeling, digital twins, traffic and freight simulation, truck service pricing, evacuation behavior, and emerging areas such as space tourism demand.
He has led multidisciplinary research projects, published extensively in peer-reviewed journals, and contributed to national research committees, including TRB’s AT060 (Trucking Industry Research) and AT045 (Intermodal Freight Transport).
Educational Background
Ph.D. in Transportation Engineering and Infrastructure Systems, Civil Engineering.
Purdue University, West Lafayette IN
M.S. in Transportation Engineering and Infrastructure Systems, Civil Engineering.
National University of Colombia, Medellin, Colombia
B.S. in Civil Engineering.
National University of Colombia, Medellin, Colombia
Professional Experience
Assistant Professor of Civil Engineering, Florida Institute of Technology, Melbourne FL
Research Assistant, Purdue University, West Lafayette IN
Transportation and Construction Analyst, Medellín City Hall, Medellín, Colombia
Current Courses
CVE 4060 Transportation Engineering
CVE 5064 Transportation Planning
CVE 5062 Statistical and Econometric Methods for Transportation Data Analysis
ENM 5430 Strategic Analysis with Game Theory
Selected Publications
Bi, Jingchen, and Rodrigo Mesa-Arango. The Demand for Food Products with Food Supply Chain Traceability in the US: A Stated Preference Analysis. Available at SSRN 5026623(2024).
Pineda-Jaramillo, Juan, Claudia Munoz, Rodrigo Mesa-Arango, Carlos Gonzalez-Calderon, and Anne Lange. Integrating multiple data sources for improved flight delay prediction using explainable machine learning. Research in Transportation Business & Management56 (2024): 101161.
Alomari, Esraa and Wang, Junyu and Yu, Xuetao and Mesa-Arango, Rodrigo, Are Sports Fans Willing to Accept Post-Match Delays to Compensate for Free Parking? Factors Affecting Stadium Parking and Accessibility. Available at SSRN 4697508(2024).
Brenner, J. R., Weaver, R. J., Hamed, K., Mesa-Arango, R., & Demoret, K. A. Creating Research Ready Students with an Entrepreneurial Maker Engineering Curriculum. (2024).
Eissa, Taleb, Rodrigo Mesa-Arango, Hussin AM Yahia, and Saeed Alghamdi. Freight Mode Choice with Public Data from the United States. Journal of Civil Engineering and Urbanism13, no. 4 (2023): 65-84.
Mesa-Arango, R., Pineda-Jaramillo, J., Araujo, D.S., Bi, J., Basva, M. & Viti, F. Missions and factors determining the demand for affordable mass space tourism in the United States: A machine learning approach. Acta Astronautica 204, 307–320 (2023).
Pineda-Mendez, R. A., & Mesa-Arango, R. (2022). Understanding the Impact of Operational, Sociodemographic, and Seasonal Determinants on Bicycle Service Life in a Large Bike-Sharing System: A Weibull-Based Accelerated Failure Time Model. Social Science Research Network
Fabregas, A.D., Crawford, P., Mesa-Arango R., & Calatayud A. Parametric Evaluation of Internet of Things Applications to Freight Transportation Using Model-Based Systems Engineering. Transportation Research Record 2676(3) 38-48 (2022).
Dai, W., Yeleussizov, T., & Mesa-Arango, R. Taxonomy and Abstraction of the On-demand Mobility Aviation System for Smart Cities. Social Science Research Network 4247309. (2022).
Pineda-Jaramillo, J., Barrera-Jiménez, H., & Mesa-Arango, R. Unveiling the relevance of traffic enforcement cameras on the severity of vehicle–pedestrian collisions in an urban environment with machine learning models. Journal of safety research, 81, 225-238 (2022).
Wang, J., Yu, X., & Mesa-Arango, R. (2022). Microsimulation-Based Analysis and Selection of a Delayed-Exit Parking Lot for Professional Sports Stadiums. Social Science Research Network
Mesa-Arango, R., Narayanan, B. & Ukkusuri, S. V. The Impact of International Crises on Maritime Transportation Based Global Value Chains. Networks and Spatial Economics 19, 381–408 (2019).
Mesa-Arango, R., Valencia-Alaix, V. G., Pineda-Mendez, R. A. & Eissa, T. Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country. Transportation Research Record 2672, 41–53 (2018).
Mesa-Arango, R. & Ukkusuri, S. V. Minimum Cost Flow Problem Formulation for the Static Vehicle Allocation Problem with Stochastic Lane Demand in Truckload Strategic Planning. Transportmetrica A: Transport Science 13, 893–914 (2017).
Mesa-Arango, R. & Kumar, I. Hierarchical Value Chains Encompassing Freight Transportation and Logistics Sectors in the United States: Network Analysis Approach. Transportation Research Record 2609, 1–10 (2017).
Ukkusuri, S. V., Mesa-Arango, R., Narayanan, B., Sadri, A. M. & Qian, X. Evolution of the Commonwealth Trade Network: Hubs, Criticality and Global Value Chains. International Trade Working Paper, (2016) doi: https://doi.org/10.14217/5jm3mbfw26jg-en.
Mesa-Arango, R., Zhan, X., Ukkusuri, S. V. & Mitra, A. Direct transportation Economic Impacts of Highway Networks Disruptions Using Public Data from the United States. Journal of Transportation Safety & Security 8, 36–55 (2016).
Mesa-Arango, R. & Ukkusuri, S. V. Pricing and Segmentation of Stochastic Demand in Less-Than-Truckload Combinatorial Bids. Transportation Research Record 2567, 28–37 (2016).
Mesa-Arango, R. & Ukkusuri, S. V. Demand Clustering in Freight Logistics Networks. Transportation Research Part E: Logistics and Transportation Review 81, 36–51 (2015).
Mesa-Arango, R. & Ukkusuri, S. V. Attributes Driving the Selection of Trucking Services and the Quantification of the Shipper’s Willingness to Pay. Transportation Research Part E: Logistics and Transportation Review 71, 142–158 (2014).
Mesa-Arango, R. & Ukkusuri, S. V. Modeling the Car-Truck Interaction in a System-Optimal Dynamic Traffic Assignment Model. Journal of Intelligent Transportation Systems 18, 327–338 (2014).
Mesa-Arango, R. & Ukkusuri, S. V. Benefits of In-Vehicle Consolidation in Less than Truckload Freight Transportation Operations. Transportation Research Part E: Logistics and Transportation Review 60, 113–125 (2013).
Mesa-Arango, R., Hasan, S., Ukkusuri, S. V. & Murray-Tuite, P. Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data. Natural Hazards Review 14, 11–20 (2013).
Hasan, S., Mesa-Arango, R. & Ukkusuri, S. A Random-Parameter Hazard-Based Model to Understand Household Evacuation Timing Behavior. Transportation Research Part C: Emerging Technologies 27, 108–116 (2013).
Mesa-Arango, R., Ukkusuri, S. & Sarmiento, I. Network Flow Methodology for Estimating Empty Trips in Freight Transportation Models. Transportation Research Record 2378, 110–119 (2013).
Hasan, S., Mesa-Arango, R., Ukkusuri, S. & Murray-Tuite, P. Transferability of Hurricane Evacuation Choice Model: Joint Model Estimation Combining Multiple Data Sources. Journal of Transportation Engineering 138, 548–556 (2012).
Recognition & Awards
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Member of Chi-Epsilon, The civil engineering honor society. |
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Civil Engineering Best Dissertation Award (Spring/Summer 2015) for PhD dissertation. Purdue University. |
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Eldon J. Yoder Memorial Award. Outstanding Graduate Student in Transportation Engineering. Civil Engineering Purdue University. |
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International Road Federation (IRF). Executive Fellow. |
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Paper selected for Podium Presentation in the 20th International Symposium on Transportation and Traffic Theory (ISTTT20), Noordwijk – Netherlands. |
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Travel Grant, Colombian Student Association at Purdue (CSAP). |
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Selected for Excellence Internship Program, Medellín City Hall. |
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BS First semester scholarship, Universidad Nacional de Colombia. |
Research
Currently, Dr. Mesa is exploring the next frontier of ITS using advanced data fusion, machine learning, and digital twin technologies. He is investigating the deployment of LiDAR, computer vision, and edge computing to develop real-time, adaptive traffic management solutions for urban corridors and Traffic Management Centers (TMCs). These efforts are grounded in multimodal sensor integration and predictive analytics, aimed at enabling dynamic control strategies and network optimization under uncertainty.
Dr. Mesa’s research is grounded in quantitative analysis of transportation systems, with an initial focus on household behavior under emergency conditions. His early work applied advanced statistical and econometric techniques to study evacuation choices during hurricanes, using empirical data. This line of inquiry expanded into freight logistics, where he developed methodologies for trucking optimization and freight traffic modeling. His research has since progressed to address macro-level system dynamics, including the resilience of global value chains and the economic impacts of intermodal freight disruptions. Recent efforts incorporate machine learning and data fusion to study transportation safety, urban air mobility, and emerging travel behavior trends such as space tourism demand and Internet-of-Things for transportation systems.