Abstract
Real-time passenger flow prediction plays an important role in subway network design and management. Most of the existing prediction algorithms only consider the sequence of passenger flow volume, however, ignore the influence of other outer factors, for example, the weather conditions, air quality and temperature. In this paper, a systematic framework, MetroEye, is proposed for weather-aware prediction of real-time passenger flow. The framework contains an offline system and an online system. The offline system adopts a conditional random field (CRF) model to establish the relationship between passenger flow volume and weather factors. Experimental results show the superior prediction accuracy of the model, especially in large stations. The online system provides efficient methods to simulate the real-time passenger flow volume. Due to its high practicality, MetroEye has been adopted by Beijing Urban Rail Transit Control Center to monitor the passenger flow status of the Beijing subway system.
Highlights
The subway transportation is an important way to solve the urban traffic problems
In order to predict the real-time metro passenger flow volume, we propose an approach for simulating passengers’ travel paths from their origins to destinations based on collected historic records
The offline system adopts a conditional random field to model the influence of weather factors on subway passenger flow volume
Summary
The subway transportation is an important way to solve the urban traffic problems. In the developed cities, subway traffic occupies a very large proportion in public transportation especially in rush hours. This paper targets on predicting the real-time passenger flow volume at each station of a subway network with the consideration of weather conditions. In order to predict the real-time metro passenger flow volume, we propose an approach (called MetroEye) for simulating passengers’ travel paths from their origins to destinations based on collected historic records. The offline system adopts a conditional random field to model the influence of weather factors on subway passenger flow volume. We propose a practical systematic framework, MetroEye, to predict the real-time passenger flow volume in subway system given only the entrance information of passengers.
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