Traffic Flow Theory

Latest papers

12 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Agentic discovery of interpretable car-following laws from multi-source trajectory data
Oct 20, 2026Jiamei Xu, Jie Sun, Keshuang Tang +1Traffic Flow Theory Intelligent Transportation Systems
College of Transportation Engineering & Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, 201804, China
Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
Oct 7, 2026Federica Bragone, Matthieu BarreauTraffic Flow Theory Neural Cellular Automata
Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden
Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps
Sep 29, 2026Sheryl Paul, Kexin Wang, Ruolin Li +1Intelligent Transportation Systems Agent-Based Modeling
University of Southern California, Los Angeles, CA, USA.
Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach
Sep 28, 2026Guodong Ma, Baofeng Sun, Wenyu Yang +1Intelligent Transportation Systems Model Predictive Control
School of Transportation, Jilin University, Changchun 130022, China · School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Sustained Heterogeneity: an emergent collective mechanism in LLM-driven traffic
Aug 29, 2026Yujun Qi, Yangyang GuanTraffic Flow Theory Emergent Behavior in Multi-Agent Systems
College of Civil Engineering, Nanjing Tech University, Nanjing, China
Quantum-Inspired Modeling of Driving Behavior
Aug 26, 2026Mohammad Elayan, Omid Armantalab, Wissam KontarTraffic Flow Theory Intelligent Transportation Systems
Civil & Environmental Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data
Aug 1, 2026Shrey Agrawal, Gowri Asaithambi, Venkatesan Kanagaraj +3Traffic Flow Estimation Traffic Flow Theory
Indian Institute of Technology Kanpur, Kanpur, 208016, Uttar Pradesh, India · Indian Institute of Technology Tirupati, Tirupati, 517619, Andhra Pradesh, India · Technical University of Dresden, Dresden, 01062, Germany
A Semiparametric Framework for Stochastic Fundamental Diagram Modeling
Jul 17, 2026Pengnan Chi, Xiaoliang Ma, Magnus Jansson +1Intelligent Transportation Systems Physics-Informed ML
KTH Royal Institute of Technology, Stockholm, Sweden · Swedish Transport Administration, Borlänge, Sweden
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
Jul 3, 2026Chengyue Wang, Bin Rao, Haicheng Liao +3Multi-Agent Trajectory Prediction Autonomous Driving
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau · Department of Civil and Environmental Engineering, University of Macau, Macau
Revisiting mesoscopic traffic flow simulation in SUMO: Limitations, analysis, and an alternative
Jun 8, 2026Ying-Chuan Ni, Alina Akopian, Anastasios Kouvelas +1Intelligent Transportation Systems Traffic Flow Theory
Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, Zürich, 8093, Switzerland
SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
May 22, 2026Haopeng Deng, Fucheng Zheng, Xinhai XiaIntelligent Transportation Systems Traffic Flow Estimation
School of Future Transportation, Guangzhou Maritime University, Guangzhou, China
Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportation
Mar 10, 2026Wuping XinPhysics-Informed Generative Modeling Traffic Flow Estimation
Caliper Corporation

Oct 20, 2026J/K move · Enter open · S save

Agentic discovery of interpretable car-following laws from multi-source trajectory data

Jiamei Xu, Jie Sun, Keshuang Tang, Jian Sun

College of Transportation Engineering & Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, 201804, China

Traffic Flow Theory Intelligent Transportation Systems Autonomous Scientific Discovery

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Car-following (CF) models is a fundamental component of microscopic traffic simulation and underpin the analysis of collective traffic dynamics, while its modeling becomes more challenging due to diverse human driving behaviors and the increasing presence of automated vehicle. Analytical CF models offer interpretable representations of longitudinal vehicle interactions, but their prescribed functional structures may limit the representation of heterogeneous driving behavior. This study proposes an agent-centered, physics-aware framework for the empirical discovery of CF laws from multi-source trajectory data. The framework iteratively combines agent-guided hypothesis generation, informed by traffic flow knowledge and state-dependent reconstruction errors, with evolutionary symbolic search and physics-aware multi-objective evaluation. Using NGSIM US-101, highD, and exiD, the framework discovers the Adaptive Equilibrium Response Model (AERM), which couples state-dependent relative-speed response with spacing-dependent equilibrium-speed restoration. AERM admits analytical equilibrium speed-spacing and flow-density relationships; its stability conditions are derived for the zero-delay formulation, and platoon simulations examine disturbance propagation. Evaluated against seven classical CF models on Shanghai naturalistic driving data, NGSIM I-80, and Waymo trajectories, AERM attains the lowest spacing and speed reconstruction errors under trajectory-specific calibration, reducing spacing RMSE by 9.11%, 7.59%, and 21.73% relative to the strongest baselines, and remains competitive under dataset-level shared parameters. These results demonstrate the fitting flexibility and analytical tractability of the discovered CF law, providing an interpretable basis for microscopic traffic simulation.