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% on average when predicting the arrival delay an hour ahead and 47% when predicting within a 15 minute future time window.
Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System
2016-05-01
815609 byte
Conference paper
Electronic Resource
English
Smart transportation and analytics
Taylor & Francis Verlag | 2018
|Predictive analytics for safer smart cities
IEEE | 2017
|Enabling Sustainable Urban Transportation with Predictive Analytics and IoT
DOAJ | 2024
|Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications
Transportation Research Record | 2021
|