Transportation Engineering

Transportation Engineering (hereafter referred to as The Journal)is an interdisciplinary, open-access journal dedicated to advancing transportation research that is transparent, reproducible, transferrable, and trustworthy. The journal serves as a venue for both novel transportation research and research outputs that strengthen the foundations of transportation science and engineering, including open data, open methods, open software, reproducible workflows, benchmarks, replication studies, and evidence-based research practices. Transportation research increasingly depends on complex computational models, large and heterogeneous datasets, advanced sensing technologies, artificial intelligence (AI), and collaborations among public agencies, industry, and academia. While these advances create new opportunities, they also present challenges for verification, reproducibility, and transferability. Transportation Engineering addresses these challenges by recognizing data, software, protocols, benchmarks, and reproducible research artifacts as valuable scholarly contributions that enable cumulative scientific progress. The Journal treats openness not merely as a publication policy, but as a scientific contribution in its own right.The Journal welcomes both Original Research Articles and Open Science Articles. Original Research Articles should present novel transportation science and engineering and be accompanied, where appropriate, by openly accessible data, software, workflows, benchmarks, protocols, or other resources that enable verification, reuse, and extension of the work. In particular, the Journal welcomes Open Science Articles that strengthen the transparency, reproducibility, and practical value of transportation science and engineering, including but not limited to: Data Papers and Data Descriptors documenting transportation datasets, metadata, provenance, privacy protections, limitations, use cases, and opportunities for reuse. Software and Code Papers describing open-source tools, computational workflows, model implementations, use cases, simulation environments, and research infrastructure. Benchmark and Challenge Papers introducing shared datasets, challenge problems, metrics, baselines, and evaluation protocols that enable consistent comparison of methods and models. Replication, Reproducibility, and Robustness Studies, including direct replications, computational reproductions, sensitivity analyses, multiverse analyses, and reassessments of influential findings. Practice and Translation Case Studies demonstrating the implementation, adaptation, or evaluation of open research products in transportation science and engineering, as well as in planning, policy, and industry settings. Reviews, Perspectives, and Scholarly Discussions addressing open science practices, FAIR and FAIR² data, research standards, governance frameworks, research infrastructure, software sustainability, equity, and emerging technologies. Science for Open Transportation Engineering examining the theories, methods, incentives, governance structures, and technologies that help support openness, reproducibility, and transparency in transportation research. The Journal also welcomes studies examining how AI and other emerging technologies influence transparency, reproducibility, accessibility, and trust in transportation research.The journal covers all areas of transportation research and engineering, including but not limited to, traffic operations, safety, public transportation, active transportation, multimodal transportation, travel behavior, freight and logistics, air transportation, infrastructure systems, resilience, accessibility, environment and energy, intelligent transportation systems, connected and automated mobility, and emerging mobility services. What distinguishes the Journal is not a specific transportation domain, but its commitment to advancing the scientific infrastructure that enables transportation research to be transparent, r