Transit system delays frustrate riders and reduce adoption of public transportation. DelayRadar innovates by integrating diverse data sources and using multivariate modeling to explain over 70% of delay variance, enabling accurate transit predictions that improve user experience and operational planning. The clustering approach identifies seasonal and time-of-day patterns for more accurate forecasting.
This paper presents DelayRadar, a multivariate predictive model for transit systems using machine learning to forecast bus arrival times. The approach integrates real-time transit data, static schedules, historical data, and weather information to predict delays. It combines clustering analysis with regression and tree-based models to identify patterns and make accurate predictions.
The system achieves 4-5 minute prediction errors with real-time data and 47% improvement when predicting 15-minute future delays. Clustering analysis reveals distinct morning and afternoon delay patterns. Regression and tree-based models outperform simple baselines, demonstrating feasibility of accurate transit delay prediction for operational decision support.
@inproceedings{Oruganti2016,
author = {Oruganti, Aparna and Sun, Fangzhou and Baroud, Hiba and Dubey, Abhishek},
booktitle = {2016 {IEEE} International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016},
title = {DelayRadar: {A} multivariate predictive model for transit systems},
year = {2016},
pages = {1799--1806},
abstract = {Effective public transit operations are one of the fundamental requirements for a modern community. Recently, a number of transit agencies have started integrating automated vehicle locators in their fleet, which provides a real-time estimate of the time of arrival. In this paper, we use the data collected over several months from one such transit system and show how this data can be potentially used to learn long term patterns of travel time. More specifically, we study the effect of weather and other factors such as traffic on the transit system delay. These models can later be used to understand the seasonal variations and to design adaptive and transient transit schedules. Towards this goal, we also propose an online architecture called DelayRadar. The novelty of DelayRadar lies in three aspects: (1) a data store that collects and integrates real-time and static data from multiple data sources, (2) a predictive statistical model that analyzes the data to make predictions on transit travel time, and (3) a decision making framework to develop an optimal transit schedule based on variable forecasts related to traffic, weather, and other impactful factors. This paper focuses on identifying the model with the best predictive accuracy to be used in DelayRadar. According to the preliminary study results, we are able to explain more than 70\% of the variance in the bus travel time and we can make future travel predictions with an out-of-sample error of 4.8 minutes with information on the bus schedule, traffic, and weather.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/bigdataconf/OrugantiSBD16},
category = {selectiveconference},
contribution = {colab},
doi = {10.1109/BigData.2016.7840797},
file = {:Oruganti2016-DelayRadar_A_multivariate_predictive_model_for_transit_systems.pdf:PDF},
keywords = {transit systems, delay prediction, machine learning, clustering, weather analysis, real-time prediction, decision support},
project = {smart-transit,smart-cities},
tag = {transit},
timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
url = {https://doi.org/10.1109/BigData.2016.7840797}
}