Online Program Home
My Program

Abstract Details

Activity Number: 89 - SPEED: Survey Methods, Transportation Studies, SocioEconomics, and General Statistical Methods Part 2
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 5:05 PM to 5:50 PM
Sponsor: Transportation Statistics Interest Group
Abstract #307918
Title: Comparing the Performance of Machine Learning and Semiparametric Regression Methods for Prediction of Travel Times and Flows on Urban Mass Transit Systems
Author(s): Daniel Graham*
Companies: Imperial College London
Keywords: mass transit; smart card; vehicle location; prediction; semiparametric regression; machine learning

Urban mass transit systems generate large volumes of data via automated systems for smart card transactions, signaling, and operations. There is considerable motivation within the transit industry to harness these data for performance analytics and real-time prediction. In this paper we compare machine learning (ML) and semiparametric (SP) regression techniques for analyses of travel times and flows across different lines, times of day, and operating conditions. The ML algorithms considered include Regression Trees, Kalman filters, Random Vector Functional Link Networks, Fuzzy Descriptive Logic, and Random Forests. Aspects of system performance to be modeled include travel times, reliability, crowding, capacity utilization and passenger flows. Computational expense for the ML and SP methods are assessed to facilitate the choice of appropriate methods for real-time prediction. The results can help improve the planning and implementation of transit services and reduce the potential for mis-match between passengers flows and capacity supplied under varying travel conditions.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program