Accurate transit predictions require understanding complex delay patterns influenced by traffic, weather, and time-of-day factors. This work innovates by combining clustering analysis revealing distinct delay patterns with predictive models achieving 25% improvement in average arrival time prediction, enabling better user information and operational planning.
This paper presents real-time and predictive analytics for smart public transportation decision support systems. The work develops clustering models and predictive approaches for analyzing historical bus delay patterns and transit performance. It combines real-time vehicle scheduling with Kalman filtering for accurate arrival time predictions.
The system achieves accurate transit arrival predictions with 25% error reduction using clustering-based models. Real-time Kalman filtering integrates schedule adherence and actual vehicle locations. Experimental validation using Nashville transit data demonstrates 47% improvement when predicting next 15-minute arrivals with integrated real-time information.
@inproceedings{Sun2016,
author = {Sun, Fangzhou and Pan, Yao and White, Jules and Dubey, Abhishek},
booktitle = {2016 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2016, St Louis, MO, USA, May 18-20, 2016},
title = {Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System},
year = {2016},
pages = {1--8},
acceptance = {34},
abstract = {Public bus transit plays an important role in city transportation infrastructure. However, public bus transit is often difficult to use because of lack of real- time information about bus locations and delay time, which in the presence of operational delays and service alerts makes it difficult for riders to predict when buses will arrive and plan trips. Precisely tracking vehicle and informing riders of estimated times of arrival is challenging due to a number of factors, such as traffic congestion, operational delays, varying times taken to load passengers at each stop. In this paper, we introduce a public transportation decision support system for both short-term as well as long-term prediction of arrival bus times. The system uses streaming real-time bus position data, which is updated once every minute, and historical arrival and departure data - available for select stops to predict bus arrival times. Our approach combines clustering analysis and Kalman filters with a shared route segment model in order to produce more accurate arrival time predictions. Experiments show that compared to the basic arrival time prediction model that is currently being used by the city, our system reduces arrival time prediction errors by 25 percent on average when predicting the arrival delay an hour ahead and 47 percent when predicting within a 15 minute future time window.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunPWD16},
category = {selectiveconference},
contribution = {lead},
doi = {10.1109/SMARTCOMP.2016.7501714},
file = {:Sun2016-Real-Time_and_Predictive_Analytics_for_Smart_Public_Transportation_Decision_Support_System.pdf:PDF},
keywords = {transit systems, predictive analytics, Kalman filtering, clustering, real-time prediction, decision support, transportation planning},
project = {smart-transit,smart-cities},
tag = {transit},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2016.7501714}
}