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

arXiv: https://arxiv.org/abs/1605.08390 · HTML: https://mlsanity.com/paper/1605.08390/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Day-to-day and seasonal regularity of network passenger delay for metro networks](https://mlsanity.com/paper/2107.14094/) (2021)
- [Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach](https://mlsanity.com/paper/2108.09292/) (2021)
- [DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis](https://mlsanity.com/paper/2509.13633/) (2025)

## Abstract (arXiv)

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.