Real time transit demand prediction capturing station interactions and impact of special events |
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| Affiliation: | 1. Northeastern University, Boston, MA 02115, USA;2. Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Massachusetts Institute of Technology, Cambridge, MA, USA;2. Northeastern University, Boston, MA, USA;1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China;2. School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China;3. Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA;4. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China;1. Department of Civil & Environmental Engineering, National Unversity of Singapore, Singapore 117576, Singapore;2. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China |
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| 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. |
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| Keywords: | AFC data Real time prediction Station arrivals State-space models Dynamic factor models Correlation clustering |
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