Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

TL;DR

This paper presents a probabilistic model to estimate passenger route choices in complex metro systems using only AFC data, enabling better transit management without additional equipment.

Contribution

It introduces a novel probabilistic approach to infer passenger route choices solely from AFC data in complex metro networks.

Findings

Model accurately estimates route distribution from AFC data.

Validated on large-scale Shenzhen metro data set.

Provides useful insights for transit planning and management.

Abstract

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.