Machine Learning in Transportation : Applications with Examples and Codes (De Gruyter Textbook) (2024. VII, 163 S. 4 b/w and 61 col. ill., 7 b/w tbl., 1 b/w maps. 240)

The book introduces the reader to machine learning in transportation. It discusses both simple and advanced concepts including core statistics, data wrangling, data visualization, supervised and unsupervised datamining techniques as well as text mining. The book prepares students to manage data, visualize data and apply appropriate machine learning techniques on transportation datasets to derive important insights.

Niharika Dayyala, Illinois State Uni., Nivedya M. Kottayi, San Diego Association of Governments, Rajib B. Mallick, Uni. of Texas, USA.