Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach

• URL: http://arxiv.org/abs/2510.03121v1
• Date: Fri, 03 Oct 2025 15:50:01 GMT
• Title: Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
• Authors: Muhammad Usama, Haris Koutsopoulos,
• Abstract summary: This study presents a deep learning framework designed to predict the complex propagation of train headways across an entire metro line.<n>By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics.<n>This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies.
• Score: 3.3246021992391874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics, effectively capturing both their temporal evolution and spatial dependencies across all stations. This capability empowers dispatchers to evaluate the impact of various terminal headway control decisions without resorting to computationally intensive simulations. We introduce a flexible methodology to simulate diverse dispatcher strategies, ranging from maintaining even headways to implementing custom patterns derived from observed terminal departures. In contrast to existing research primarily focused on passenger load predictioning or atypical disruption scenarios, our approach emphasizes proactive operational control. Evaluated on a large-scale dataset from an urban metro line, the proposed ConvLSTM model demonstrates promising headway predictions, offering actionable insights for real-time decision-making. This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies, thereby significantly improving service consistency and passenger satisfaction.

Related papers

• Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling [54.94692733670454]
  Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles.<n>We propose a lightweight yet highly accurate streaming-based trajectory forecasting approach.<n>Our approach significantly reduces inference latency, making it well-suited for real-world deployment.
  arXiv  Detail & Related papers  (2026-03-02T13:44:23Z)
• Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach [12.018920884898215]
  We introduce Drift-Corrected Imitation Learning (DCIL), a novel self-supervised algorithm that extends DAgger by incorporating distance-based drift correction.<n>We evaluate DCIL using a comprehensive real-world dataset from textscInfrabel, the Belgian railway infrastructure manager.
  arXiv  Detail & Related papers  (2025-12-17T14:06:26Z)
• DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models [31.509436717815102]
  This paper proposes a novel Passenger Travel Choice prediction framework under metro delays with the Large Language Model (DelayPTC-LLM) A comparative analysis of DelayPTC-LLM with traditional prediction models demonstrates the superior capability of LLMs in handling complex, sparse datasets commonly encountered under disruption of transportation systems.
  arXiv  Detail & Related papers  (2024-09-28T13:09:15Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  We present nuPlan, a real-world motion planning benchmark that captures multi-agent interactions.<n>We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN)<n>We also present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction [24.26429523848735]
  Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services. We introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the challenges. CP-MoE has been deployed in DiDi to improve the accuracy and reliability of the travel time estimation system.
  arXiv  Detail & Related papers  (2024-06-14T12:57:17Z)
• Valeo4Cast: A Modular Approach to End-to-End Forecasting [93.86257326005726]
  Our solution ranks first in the Argoverse 2 End-to-end Forecasting Challenge, with 63.82 mAPf. We depart from the current trend of tackling this task via end-to-end training from perception to forecasting, and instead use a modular approach. We surpass forecasting results by +17.1 points over last year's winner and by +13.3 points over this year's runner-up.
  arXiv  Detail & Related papers  (2024-06-12T11:50:51Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems [0.43012765978447565]
  Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability. We focus on the problem of hourly prediction of the fraction of travelers choosing one mode of transportation over another using high-dimensional travel time data. We employ various regularization techniques for variable selection to prevent overfitting and resolve multicollinearity issues.
  arXiv  Detail & Related papers  (2022-03-27T02:59:00Z)
• Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems [9.725264855780482]
  The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management. Most of the existing models mainly predict the passenger flow on general weekdays or weekends. We propose a deep learning-based model named Spatial Temporal Attention Fusion Network for short-term passenger flow prediction on holidays.
  arXiv  Detail & Related papers  (2022-02-27T01:06:24Z)
• Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management [8.441020454345932]
  We introduce a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA.
  arXiv  Detail & Related papers  (2021-07-28T14:11:34Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.