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[2412.06871] Predicting Subway Passenger Flows under Incident Situation with Causality
Summary
This paper presents a two-stage method for predicting subway passenger flows during incidents, addressing challenges in data scarcity and interpretability, and demonstrating improved accuracy through causal analysis.
Why It Matters
Understanding passenger flow during incidents is crucial for effective subway management. This research enhances predictive capabilities, enabling transit authorities to respond proactively to disruptions, ultimately improving service reliability and passenger safety.
Key Takeaways
- Introduces a two-stage prediction method for subway passenger flows during incidents.
- Combines normal condition predictions with causal effect analysis for improved accuracy.
- Enhances interpretability by identifying key factors influencing passenger flow during incidents.
- Validates the approach using real-world data, demonstrating its practical applicability.
- Aims to assist subway managers in making informed decisions during operational disruptions.
Computer Science > Machine Learning arXiv:2412.06871 (cs) [Submitted on 9 Dec 2024 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Predicting Subway Passenger Flows under Incident Situation with Causality Authors:Xiannan Huang, Shuhan Qiu, Quan Yuan, Chao Yang View a PDF of the paper titled Predicting Subway Passenger Flows under Incident Situation with Causality, by Xiannan Huang and 3 other authors View PDF HTML (experimental) Abstract:In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction during incidents, such as a lack of interpretability and data scarcity. To address these challenges, we propose a two-stage method that separates predictions under normal conditions and the causal effects of incidents. First, a normal prediction model is trained using data from normal situations. Next, the synthetic control method is employed to identify the causal effects of incidents, combined with placebo tests to determine significant levels of these effects. The significant effects are then utilized to train a causal effect prediction model, which can forecast the impact of incidents based on features of the incidents and passenger flows. During the prediction phase, the results from both the normal situation model and the causal effect prediction model are i...
