Data Science for Transportation publishes high-quality original research and reviews in a wide range of topics related to Data Science for Transportation. This includes classical approaches when data sources are used to unravel underlying physical mechanisms leading to general laws and new modelling frameworks. It also includes new data-driven approaches when AI plays a central role. The goal of the journal is to showcase the latest methodological advances and applications of data science methods in transportation and appropriate implications for policy making. The journal is also interested in the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where big data intelligence can augment other methods in transportation systems planning, operations, freight, safety analysis, transit, safe and sustainable cities and emergency management. There are many emerging questions of relevance on ethical, social and privacy, that are also relevant in this domain. The focus is primarily on analytical data driven methods. High quality application based studies will also be considered. Data Science for Transportation provides a platform to discuss these wide implications - encouraging a cross-disciplinary dialogue - with original research articles, review papers and commentary articles. .