Real time transit demand prediction capturing station interactions and impact of special events

Authors

Peyman Noursalehi
Haris N. Koutsopoulos
Jinhua Zhao

Document Type

Journal Article

Publication Date

2018

Subject Area

place - europe, place - urban, mode - subway/metro, planning - service level, planning - methods, planning - travel demand management, planning - service quality, technology - intelligent transport systems, ridership - demand

Keywords

AFC data, Real time prediction, Station arrivals, State-space models, Dynamic factor models, Correlation clustering

Abstract

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

Recommended Citation

Noursalehi, P., Koutsopoulos, H.N., & Zhao, J. (2018). Real time transit demand prediction capturing station interactions and impact of special events. Transportation Research Part C: Emerging Technologies, Vol. 97, pp. 277-300.