Improving Crash Frequency Prediction from Simulated Traffic Conflicts Using Machine Learning Based Microsimulation

• URL: http://arxiv.org/abs/2606.12500v2
• Date: Thu, 18 Jun 2026 15:13:15 GMT
• Title: Improving Crash Frequency Prediction from Simulated Traffic Conflicts Using Machine Learning Based Microsimulation
• Abstract summary: Rule-based microsimulation models fail to generate realistic conflict dynamics, limiting crash prediction accuracy.<n>Recent advances in machine learning (ML) offer a promising opportunity to potentially improve microsimulation realism.<n>Traffic microsimulation was conducted for five real-world signalised intersections in Leeds, UK.<n>Conflicts from the ML model yielded crash predictions in line with the real-world crash data, whereas the rule-based model did not permit meaningful predictions.
• Score: 16.894046445341676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic microsimulation combined with surrogate safety measures has increasingly been used as a proactive alternative to historical crash data for predicting crash frequency for current or planned road infrastructure designs. However, existing microsimulation-based safety studies have adopted simplified rule-based behaviour models, which reproduce traffic flow reasonably well but often fail to generate realistic conflict dynamics, limiting crash prediction accuracy. Recent advances in machine learning (ML)-based behaviour models offer a promising opportunity to potentially improve microsimulation realism and crash frequency predictions by learning human driving behaviour directly from large-scale trajectory datasets. To investigate this possibility, traffic microsimulation was conducted for five real-world signalised intersections in Leeds, UK, using both a standard rule-based model and a state-of-the-art ML model. Simulated vehicle trajectories were analysed using a two-dimensional Time-to-Collision metric to identify simulated conflicts, which were then modelled using Extreme Value Theory to predict crash frequency. Results show that conflicts from the ML model yielded crash predictions in line with the real-world crash data, whereas the rule-based model did not permit meaningful predictions, presumably due to a lack of model calibration to the specific simulated intersections. Directly using ML-generated simulated crashes to predict real-world crash frequency also yielded poor results, suggesting that while current ML models can realistically reproduce conflicts, they are not yet able to generate realistic crashes. Overall, the findings demonstrate that ML-based behaviour models are promising for improving crash prediction from simulated conflicts, without a need for location-specific model calibration, and suggest clear future directions for ML-based traffic microsimulation.

Related papers

• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap [9.148510140521706]
  We introduce Collision Learning via Augmented Sim-to-real Hybridization (CLASH), a data-efficient framework that learns a parameter-conditioned impulsive collision surrogate model.<n>We demonstrate that policies obtained with our hybrid simulator transfer more robustly to the real world.
  arXiv  Detail & Related papers  (2026-02-21T03:39:33Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections [8.484294935626224]
  Traffic simulators are widely used to study the operational efficiency of road infrastructure.<n>Their rule-based approach limits their ability to mimic real-world driving behavior.<n>We propose traffic engineering-related metrics to evaluate generative trajectory prediction models.
  arXiv  Detail & Related papers  (2025-06-10T16:27:42Z)
• Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by Hybrid VAE-Diffusion-Based Generative Neural Networks [25.772405506451204]
  A key challenge in crash frequency modelling is the prevalence of excessive zero observations.<n>We propose a hybrid VAE-Diffusion neural network, designed to reduce zero observations.<n>We assess the synthetic data quality generated by this model through metrics like similarity, accuracy, diversity, and structural consistency.
  arXiv  Detail & Related papers  (2025-01-17T07:53:27Z)
• MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction [24.3907895281179]
  This paper presents a novel deep learning model designed for counterfactual post-crash traffic prediction. The proposed model is treatment-aware, with a specific focus on comprehending and predicting traffic speed under hypothetical crash intervention strategies. The model is validated using both synthetic and real-world data, demonstrating that MSCT outperforms state-of-the-art models in multi-step-ahead prediction performance.
  arXiv  Detail & Related papers  (2024-07-19T06:42:41Z)
• Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study [71.04084063541777]
  Counterfactual learning to rank has attracted extensive attention in the IR community.<n>Models can be theoretically unbiased when the user behavior assumption is correct and the propensity estimation is accurate.<n>Their effectiveness is usually empirically evaluated via simulation-based experiments due to a lack of widely available, large-scale, real click logs.
  arXiv  Detail & Related papers  (2024-04-04T10:54:38Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving [0.0]
  We benchmark machine learning models against perturbations that simulate functional insufficiencies observed during model deployment in a vehicle. Training the models with similar perturbations effectively reduces performance degradation, with error increases of up to +87.5%. We argue that despite being an effective mitigation strategy, data augmentation through perturbations during training does not guarantee robustness towards unforeseen perturbations.
  arXiv  Detail & Related papers  (2023-04-04T15:47:42Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Fast and Accurate Error Simulation for CNNs against Soft Errors [64.54260986994163]
  We present a framework for the reliability analysis of Conal Neural Networks (CNNs) via an error simulation engine. These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults. We show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t.FI, that only implements a limited set of error models.
  arXiv  Detail & Related papers  (2022-06-04T19:45:02Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)

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.