From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problem

• URL: http://arxiv.org/abs/2510.19889v1
• Date: Wed, 22 Oct 2025 16:45:12 GMT
• Title: From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problem
• Authors: Mostafa Ameli, Van Anh Le, Sulthana Shams, Alexander Skabardonis,
• Abstract summary: This study introduces a novel data-driven approach using deep neural networks to predict equilibrium path flows directly.<n>The Transformer-based model drastically reduces computation time, while adapting to changes in demand and network structure without the need for recalculation.<n>The model also adapts flexibly to varying demand and network conditions, supporting traffic management and enabling rapid what-if' analyses.
• Score: 39.36424353588699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The traffic assignment problem is essential for traffic flow analysis, traditionally solved using mathematical programs under the Equilibrium principle. These methods become computationally prohibitive for large-scale networks due to non-linear growth in complexity with the number of OD pairs. This study introduces a novel data-driven approach using deep neural networks, specifically leveraging the Transformer architecture, to predict equilibrium path flows directly. By focusing on path-level traffic distribution, the proposed model captures intricate correlations between OD pairs, offering a more detailed and flexible analysis compared to traditional link-level approaches. The Transformer-based model drastically reduces computation time, while adapting to changes in demand and network structure without the need for recalculation. Numerical experiments are conducted on the Manhattan-like synthetic network, the Sioux Falls network, and the Eastern-Massachusetts network. The results demonstrate that the proposed model is orders of magnitude faster than conventional optimization. It efficiently estimates path-level traffic flows in multi-class networks, reducing computational costs and improving prediction accuracy by capturing detailed trip and flow information. The model also adapts flexibly to varying demand and network conditions, supporting traffic management and enabling rapid `what-if' analyses for enhanced transportation planning and policy-making.

Related papers

• ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling [57.91760520589592]
  Scaling network depth has been a central driver behind the success of modern foundation models.<n>This paper revisits the default mechanism for deepening neural networks, namely residual connections.<n>We introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data.
  arXiv  Detail & Related papers  (2026-02-09T18:54:18Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment [1.078439500019266]
  The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks.<n>Traditional methods require iterative simulations to reach equilibrium, making real-time or large-scale scenario analysis challenging.<n>We propose a learning-based approach using Message-Passing Neural Networks as a metamodel to approximate the equilibrium flow of the User Equilibrium assignment.
  arXiv  Detail & Related papers  (2025-05-16T13:25:22Z)
• Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks [5.205252810216621]
  We develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved.<n>Our model adheres to flow conservation while delivering highly accurate predictions for link flows and utilization ratios.
  arXiv  Detail & Related papers  (2025-01-15T19:53:14Z)
• NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics [72.95483148058378]
  We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records.<n>We address challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection.
  arXiv  Detail & Related papers  (2024-12-30T00:47:49Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment [5.205252810216621]
  We leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin-destination node pairs. We show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy.
  arXiv  Detail & Related papers  (2023-10-19T23:04:09Z)
• Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs [47.18837782862979]
  This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. We demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues.
  arXiv  Detail & Related papers  (2023-03-10T02:22:33Z)
• STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction [12.223433627287605]
  Traffic data are highly nonlinear and have complex spatial correlations between road nodes. Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations. We propose a new prediction model which captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data.
  arXiv  Detail & Related papers  (2022-03-21T06:38:34Z)
• Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network [1.3706331473063877]
  We present a novel data-driven approach of learning traffic flow patterns of a transportation network. We develop a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network.
  arXiv  Detail & Related papers  (2022-02-21T19:45:15Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)

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.