A Python tool and database of Amtrak departure and arrival times with weather information

Abstract

This article proposes a Python-based Amtrak and Weather Underground (PAWU) tool to collect data on Amtrak (the main passenger train operator in the United States) departure and arrival times with weather information. In addition, this article offers a database, developed with PAWU, of the operating characteristics of 16 Amtrak routes from 2008 to 2019. More specifically, PAWU enables users to retrieve Amtrak departure and arrival times of any train number throughout the United States. It then automatically retrieves weather information from Weather Underground for each rail station and stores the data collected in a local MySQL database. Users can easily select any desired train number(s) and date range(s) without dealing with the code and the raw data from those sources that are in different formats. The database itself can be used, in part, to develop, apply, and benchmark models that assess the performance of rail services such as the one offered by Amtrak.

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

“…Because the data structure and format from the two sources are different, they initially need to be manipulated to be suitable for building, training, and testing the train delay prediction models. Therefore, in this work, we apply the Python-based Amtrak and Weather Underground (PAWU) data collecting tool provided by Lapamonpinyo, Derrible, and Corman [20] to retrieve, convert, and simplify raw data from the two sources to be the same format and store it on MySQL database. The data retrieved from ASMAD and Weather Underground stored in MySQL database is queried to construct a data-frame for building, training, and testing the train delay prediction models via the Python-based Pandas data analysis and manipulation tool.…”