Shashank Shekhar, Fangzhou Sun, Abhishek Dubey, Aniruddha Gokhale, Himanshu Neema, Martin Lehofer, Dan Freudberg
Internet of Things and Data Analytics Handbook 2016
Public transit agencies struggle with traffic congestion and unpredictable service due to lack of integrated decision support. Transit Hub innovates by combining real-time tracking, data analytics, and predictive models to enable informed operational decisions improving service reliability and user experience. The integrated approach addresses transit system bottlenecks through data-driven insights.
This work presents Transit Hub, a comprehensive smart decision support system for public transit operations integrating real-time data, predictive analytics, and operational decision support. The system combines automated vehicle location data, multiple data sources, and historical analysis to provide transit agencies with actionable intelligence for improving service quality and efficiency.
The system successfully demonstrates integration of AVL data, weather information, and historical schedules providing actionable transit insights. Predictive models identify bottleneck routes and time periods for operational improvements. Real-time decision support enables dynamic transit schedule adjustments improving service quality and operational efficiency.
@inbook{Shekhar2016,
author = {Shekhar, Shashank and Sun, Fangzhou and Dubey, Abhishek and Gokhale, Aniruddha and Neema, Himanshu and Lehofer, Martin and Freudberg, Dan},
title = {A Smart Decision Support System for Public Transit Operations},
year = {2016},
booktitle = {Internet of Things and Data Analytics Handbook},
contribution = {lead},
file = {:Shekhar2016-Transit_Hub_A_Smart_Decision_Support_System_for_Public_Transit_Operations.pdf:PDF},
keywords = {transit systems, decision support, real-time data, predictive analytics, vehicle tracking, operational intelligence, smart cities},
tag = {transit}
}