A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems

TL;DR

This paper presents a novel data-driven method for inferring individual urban rail transit trajectories during peak hours using AFC and AVL data, achieving over 90% accuracy without relying on external data.

Contribution

It introduces a new adaptive trajectory inference approach with a KL divergence-based parameter estimation method that does not depend on external or survey data.

Findings

Achieves over 90% accuracy in trajectory inference during peak hours

Uses real travel data for validation, enhancing robustness

Eliminates reliance on external data through a novel parameter estimation method

Abstract

Refined trajectory inference of urban rail transit is of great significance to the operation organization. In this paper, we develop a fully data-driven approach to inferring individual travel trajectories in urban rail transit systems. It utilizes data from the Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems to infer key trajectory elements, such as selected train, access/egress time, and transfer time. The approach includes establishing train alternative sets based on spatio-temporal constraints, data-driven adaptive trajectory inference, and trave l trajectory construction. To realize data-driven adaptive trajectory inference, a data-driven parameter estimation method based on KL divergence combined with EM algorithm (KLEM) was proposed. This method eliminates the reliance on external or survey data for parameter fitting, enhancing the robustness and applicability of the model. Furthermore, to overcome the limitations of using synthetic data to validate the result, this paper employs real individual travel trajectory data for verification. The results show that the approach developed in this paper can achieve high-precision passenger trajectory inference, with an accuracy rate of over 90% in urban rail transit travel trajectory inference during peak hours.