Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems
This addresses a critical issue for public transit management in large cities by enabling route choice estimation without additional equipment, though it is incremental as it builds on existing methods for more complex situations.
The paper tackles the problem of estimating passenger route choices in complex metro systems using only smart card data, proposing a probabilistic model that estimates how passenger flows are dispatched to different routes and trains, validated with a large-scale dataset from the Shenzhen metro system.
Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of Automated Fare Collection(AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out timestamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, which are necessary to solve our problem. While existing methods work well in some specific situations, they don't work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large scale data set collected from the Shenzhen metro system. The measured results provide us with useful inputs when building the passenger path choice model.