@article{826695,
      author = {Fox, Charles.},
      url = {http://library.usi.edu/record/826695},
      title = {Data science for transport : a self-study guide with  computer exercises.},
      publisher = {Springer,},
      abstract = {This book offers a unique introduction to the application  of data science for transport professionals and students of  transport studies. Based on a course taught by the Leeds  Institute for Transport Studies, the world's leading center  for training transport professionals, it represents the  first textbook in this new area. As transportation planning  has become increasingly data-driven, all graduate students  and transport professionals urgently need to update their  skills to include databases, machine learning, Bayesian  statistics, geographic information system (GIS), and big  data tools. Similarly, transport professionals including  national and local government planners, transport  consultants, and car company engineers are called upon to  integrate these disparate areas with a specific focus on  transportation issues, such as maps. The textbook also  features a downloadable software package with all of the  open source tools and libraries used in code examples  throughout the book, including Python, Spyder, PostGIS,  PyMC and GPy installations. As such, it offers a unique  resource for graduate/advanced undergraduate students and  instructors in transportation studies, urban and regional  planning, engineering and geography, as well as  transportation professionals.},
      doi = {https://doi.org/10.1007/978-3-319-72953-4},
      recid = {826695},
      pages = {1 online resource.},
      address = {[S.l.] :},
      year = {2018},
}