# Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach

arXiv: https://arxiv.org/abs/2108.09292 · HTML: https://mlsanity.com/paper/2108.09292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit](https://mlsanity.com/paper/1912.12563/) (2019)
- [Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs](https://mlsanity.com/paper/1903.04681/) (2019)
- [Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems](https://mlsanity.com/paper/1605.08390/) (2016)
- [Assignment-based Path Choice Estimation for Metro Systems Using Smart Card Data](https://mlsanity.com/paper/2001.03196/) (2020)
- [TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data](https://mlsanity.com/paper/2005.01492/) (2020)

## Abstract (arXiv)

Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state. The emerging data-driven techniques, such as computational graph (CG) models in the machine learning field, provide a new solution for solving this problem. In this study, we first formulate a data-driven estimation optimization framework to estimate the link travel time and station waiting time. Then, we cast the estimation optimization model into a CG framework to solve the optimization problem and obtain the estimation results. The methodology is verified on a synthetic URT network and applied to a real-world URT network using the synthetic and real-world AFC data, respectively. Results show the robustness and effectiveness of the CG-based framework. To the best of our knowledge, this is the first time that the CG is applied to the URT. This study can provide critical insights to better understand the operational state in URT.