Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data

Document Type

Journal Article

Publication Date


Subject Area

place - north america, place - urban, mode - bus, technology - automatic vehicle monitoring, technology - intelligent transport systems, operations - performance, planning - methods, planning - service improvement


Washington Metropolitan Area Transit Authority (WMATA), big data, real time data, transit performance


The Washington Metropolitan Area Transit Authority (WMATA) operates 1,250 buses on 168 different routes between 10,600 bus stops to support around 370,000 passengers each day. Utilizing sensors on vehicles and analyzing their location and movements throughout an hour, trip, or day can provide valuable information to a transit authority as well as to the users of a transit system. This amount of information can be overwhelming, but utilizing big data techniques can empower the data and the transit agency. First, this paper develops a methodology for assessing previous delays in the system by applying big data structure and statistical analysis to the data constantly collected by WMATA buses. This method of analysis also helps quantify the impact of potential transit system improvements. Second, the paper describes a model that uses the real-time data, that represents potential delays, to provide future passengers with more accurate arrival predictions despite delays. These analyses are powerful tools for agencies and planners to assess and improve transit service performance using big data analytics and real-time predictions.


Permission to publish the abstract has been given by SAGE, copyright remains with them.