Transit-hub: a smart public transportation decision support system with multi-timescale analytical services

Fangzhou Sun, Abhishek Dubey, Jules White, Aniruddha Gokhale

Cluster Computing 2019

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Why This Matters

Public transit agencies struggle with providing accurate real-time information and optimizing schedules based on actual demand patterns, particularly with heterogeneous data quality. Transit-Hub is innovative because it integrates data cleaning and management with advanced analytical models that address data quality issues while providing decision support to transit authorities. The multi-timescale approach enables both immediate customer-facing predictions and longer-term operational optimization.

What We Did

This paper presents Transit-Hub, a decision support system for public transportation that integrates multi-timescale analytical services for real-time bus arrival prediction and schedule optimization. The system combines historical and real-time transit data from multiple sources including GTFS data feeds and live vehicle location tracking. Advanced analytics including SVM-Kalman models enable both short-term and long-term delay prediction.

Key Results

The system reduced root-mean-square deviation in travel time prediction by 30-65% when predicting 15 minutes ahead using the integrated short-term prediction model. The approach demonstrated ability to integrate and manage heterogeneous data sources from multiple transit systems while providing reliable transit information. The system achieved significant improvements over basic average models in delay prediction accuracy.

Full Abstract

Cite This Paper

@article{Sun2019,
  author = {Sun, Fangzhou and Dubey, Abhishek and White, Jules and Gokhale, Aniruddha},
  journal = {Cluster Computing},
  title = {Transit-hub: a smart public transportation decision support system with multi-timescale analytical services},
  year = {2019},
  month = {jan},
  number = {Suppl 1},
  pages = {2239--2254},
  volume = {22},
  abstract = {Public transit is a critical component of a smart and connected community. As such, citizens expect and require accurate information about real-time arrival/departures of transportation assets. As transit agencies enable large-scale integration of real-time sensors and support back-end data-driven decision support systems, the dynamic data-driven applications systems (DDDAS) paradigm becomes a promising approach to make the system smarter by providing online model learning and multi-time scale analytics as part of the decision support system that is used in the DDDAS feedback loop. In this paper, we describe a system in use in Nashville and illustrate the analytic methods developed by our team. These methods use both historical as well as real-time streaming data for online bus arrival prediction. The historical data is used to build classifiers that enable us to create expected performance models as well as identify anomalies. These classifiers can be used to provide schedule adjustment feedback to the metro transit authority. We also show how these analytics services can be packaged into modular, distributed and resilient micro-services that can be deployed on both cloud back ends as well as edge computing resources.},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/journals/cluster/SunDWG19},
  contribution = {lead},
  doi = {10.1007/s10586-018-1708-z},
  file = {:Sun2019-Transit-hub_a_smart_public_transportation_decision_support_system_with_multi-timescale_analytical_services.pdf:PDF},
  keywords = {public transit, decision support, real-time prediction, schedule optimization, data integration, Kalman filtering},
  project = {smart-cities,smart-transit},
  tag = {transit},
  timestamp = {Wed, 21 Aug 2019 01:00:00 +0200},
  url = {https://doi.org/10.1007/s10586-018-1708-z},
  month_numeric = {1}
}
Quick Info
Year 2019
DOI 10.1007/s10586-018-1708-z
Keywords
public transit decision support real-time prediction schedule optimization data integration Kalman filtering
Research Areas
transit planning
Search Tags

Transit, smart, public, transportation, decision, support, system, multi, timescale, analytical, services, public transit, decision support, real-time prediction, schedule optimization, data integration, Kalman filtering, transit, planning, 2019, Sun, Dubey, White, Gokhale