Data-driven transport modelling without overfit

• URL: http://arxiv.org/abs/2605.08801v1
• Date: Sat, 09 May 2026 08:46:31 GMT
• Title: Data-driven transport modelling without overfit
• Abstract summary: Macroscopic transport modelling aims to predict traffic flows after proposed public policy interventions.<n>Traditionally, building a transport model has relied on complex understanding of socio-economic characteristics of the population.<n>We present here an alternative: a data-driven modelling protocol with objective function based on traffic counts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Macroscopic transport modelling aims to predict traffic flows after proposed public policy interventions, such as a new road or railway section or a temporary road closure. As such, it is a vital step in infrastructure planning and development. Traditionally, building a transport model has relied on complex understanding of socio-economic characteristics of the population requiring expensive data collection via surveys, which are prone to biases. Previous numerical frameworks to optimize transport models to fit observed traffic flows are not easily-interpretable and can lead to overfit. We present here an alternative: a data-driven modelling protocol with objective function based on traffic counts, which can be nowadays cheaply and reliably obtained; explainable model weights; and a controlled path to increase model complexity and accuracy. We demonstrate our approach on several toy and realistic examples, and suggest ways to generalize to multimodal systems including public transport.

Related papers

• A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models [26.91571883554539]
  Traffic data imputation is a critical preprocessing step in intelligent transportation systems.<n>Despite substantial progress in imputation models, model selection and development for practical applications remains challenging.<n>This paper proposes practice-oriented for traffic data missing patterns and imputation models, systematically cataloging real-world traffic data loss scenarios.
  arXiv  Detail & Related papers  (2024-12-06T02:47:54Z)
• Diffusion-Based Environment-Aware Trajectory Prediction [3.1406146587437904]
  The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data.
  arXiv  Detail & Related papers  (2024-03-18T10:35:15Z)
• Language-Guided Traffic Simulation via Scene-Level Diffusion [46.47977644226296]
  We present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. We first propose a scene-level diffusion model equipped with atemporal backbone which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a users query into a loss function guiding the diffusion model towards query-compliant generation.
  arXiv  Detail & Related papers  (2023-06-10T05:20:30Z)
• Building Transportation Foundation Model via Generative Graph Transformer [12.660129805049664]
  We propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy.
  arXiv  Detail & Related papers  (2023-05-24T07:34:15Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models [76.32775745488073]
  We present a novel dataset and modeling framework designed to study motion planning in understructured environments.<n>We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset.
  arXiv  Detail & Related papers  (2022-09-19T05:06:57Z)
• 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)
• Large-scale Analysis and Simulation of Traffic Flow using Markov Models [0.0]
  A mathematically rigorous model that can be used for traffic analysis was proposed earlier by other researchers. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory.
  arXiv  Detail & Related papers  (2020-07-06T12:31:27Z)
• 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)
• Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data [0.8602553195689513]
  We propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways.
  arXiv  Detail & Related papers  (2020-02-14T16:03:04Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.