A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics

2025-06-06

AI Summary

To address the challenge of modeling intra-driver heterogeneity—i.e., dynamic behavioral variations exhibited by a single driver across diverse traffic scenarios—this paper proposes a deep learning framework integrating driving condition recognition with kinematic prediction. Methodologically, it explicitly embeds discrete driving conditions (e.g., car-following, acceleration, cruising) into the model architecture, enabling the first systematic characterization of intra-driver heterogeneity. Robust condition identification is achieved via bottom-up trajectory segmentation coupled with Dynamic Time Warping (DTW). A dual-module architecture synergistically models decision-making and vehicle dynamics: a GRU-based classifier identifies driving conditions, while an LSTM-based predictor forecasts kinematic states. Experimental results demonstrate substantial improvements: up to 58.47% reduction in acceleration prediction MSE, along with significantly enhanced accuracy in speed and headway prediction. Crucially, the model faithfully reproduces critical traffic phenomena—including stop-and-go wave propagation and oscillatory dynamics—validating its physical interpretability and predictive fidelity.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

A fundamental challenge in car-following modeling lies in accurately representing the multi-scale complexity of driving behaviors, particularly the intra-driver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize inter-driver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven car-following framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines Gated Recurrent Units for discrete driving regime classification with Long Short-Term Memory networks for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent inter- and intra-driver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and Dynamic Time Warping, ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for acceleration (maximum MSE improvement reached 58.47%), speed, and spacing metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.

Problem

• Modeling intra-driver heterogeneity in car-following dynamics
• Capturing dynamic driver behavior under varying conditions
• Unifying discrete and continuous aspects of driving behavior

Innovation

• Hybrid deep learning for car-following dynamics
• Discrete driving regimes embedded in predictions
• Combines GRU and LSTM for heterogeneity modeling