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

2025-12-09

AI Summary

Urban rail transit faces challenges in inferring individual travel trajectories during peak hours, primarily due to reliance on sparse survey data and synthetic validation. To address this, we propose KLEM—a novel, purely data-driven method that jointly leverages Automated Fare Collection (AFC) and Automatic Vehicle Location (AVL) data. KLEM constructs a spatiotemporally constrained candidate train set for each trip and employs KL-divergence minimization coupled with the Expectation-Maximization (EM) algorithm to adaptively estimate model parameters and reconstruct complete origin–destination paths. Crucially, KLEM is the first method empirically validated on real-world individual trajectory data, overcoming the limitations of synthetic benchmarks. Experimental results demonstrate over 90% trajectory inference accuracy during peak periods, significantly enhancing the precision of passenger flow analysis and enabling finer-grained operational planning and service optimization.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Inferring individual travel trajectories in urban rail transit systems
• Eliminating reliance on external data for parameter fitting
• Achieving high-precision trajectory inference during peak hours

Innovation

• Uses AFC and AVL data for trajectory inference
• Employs KLEM algorithm for parameter estimation
• Validates with real data achieving over 90% accuracy