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

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This paper cites Dynamical model of traffic congestion and numerical simulation,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Dynamical model of traffic congestion and numerical simulation,

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This paper cites Congested Traffic States in Empirical Observations and Microscopic Simulations,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Congested Traffic States in Empirical Observations and Microscopic Simulations,

Reference 2

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This paper cites Full velocity difference model for a car- following theory,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Full velocity difference model for a car- following theory,

Reference 3

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This paper cites A simplified car-following theory: a lower order model,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A simplified car-following theory: a lower order model,

Reference 4

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This paper cites Trajectory data- based traffic flow studies: A revisit,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Trajectory data- based traffic flow studies: A revisit,

Reference 5

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This paper cites Analysis of asymmetric driving behavior using a self-learning approach,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Analysis of asymmetric driving behavior using a self-learning approach,

Reference 6

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This paper cites Towards Data-Driven Car- Following Models,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Towards Data-Driven Car- Following Models,

Reference 7

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This paper cites Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology,

Reference 8

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This paper cites A Car-Following Model Considering Asymmetric Driving Behavior Based on Long Short-Term Memory Neu- ral Networks,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A Car-Following Model Considering Asymmetric Driving Behavior Based on Long Short-Term Memory Neu- ral Networks,

Reference 9

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This paper cites Memory, attention and prediction: a deep learning architecture for car-following,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Memory, attention and prediction: a deep learning architecture for car-following,

Reference 10

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This paper cites Human-like Autonomous Car- Following Model with Deep Reinforcement Learning,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Human-like Autonomous Car- Following Model with Deep Reinforcement Learning,

Reference 11

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This paper cites A sequence to sequence learning based car- following model for multi-step predictions considering reaction delay,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A sequence to sequence learning based car- following model for multi-step predictions considering reaction delay,

Reference 12

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This paper cites On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications,

Reference 13

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This paper cites Capturing Car-Following Behaviors by Deep Learning,.

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

Reference 14

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Observation 90f2f693-1c69-4b63-8b5d-a0457f3b9649 · outbound

This paper cites Incorporating human-factors in car- following models: A review of recent developments and research needs,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Incorporating human-factors in car- following models: A review of recent developments and research needs,

Reference 15

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Observation 59fbdba0-da2b-48f8-94dd-9e182bb2a28c · outbound

This paper cites Hysteresis in traffic flow revisited: An improved measure- ment method,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Hysteresis in traffic flow revisited: An improved measure- ment method,

Reference 16

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Observation c473e757-0cc1-41b1-8692-c9bc0b5644a4 · outbound

This paper cites On the periodicity of traffic oscillations and capacity drop: The role of driver characteristics,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics On the periodicity of traffic oscillations and capacity drop: The role of driver characteristics,

Reference 17

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This paper cites A behavioral car-following model that captures traffic oscillations,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A behavioral car-following model that captures traffic oscillations,

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This paper cites Microscopic traffic hys- teresis in traffic oscillations: A behavioral perspective,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Microscopic traffic hys- teresis in traffic oscillations: A behavioral perspective,

Reference 19

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This paper cites Heterogeneity in car-following behav- ior: Theory and empirics,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Heterogeneity in car-following behav- ior: Theory and empirics,

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Observation 37aae774-b0be-41db-9c1a-c746686a9c5a · outbound

This paper cites Formalizing the heterogeneity of the vehicle-driver system to reproduce traffic oscillations,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Formalizing the heterogeneity of the vehicle-driver system to reproduce traffic oscillations,

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This paper cites Investigating the long- and short-term driving characteristics and incorporating them into car-following models,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Investigating the long- and short-term driving characteristics and incorporating them into car-following models,

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Observation 4943b9fe-7ecb-4898-8a3a-f4127c326e37 · outbound

This paper cites Multi-anticipation and heterogeneity in car-following empirics and a first exploration of their implications,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Multi-anticipation and heterogeneity in car-following empirics and a first exploration of their implications,

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This paper cites Analyzing fluctuations in car-following,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Analyzing fluctuations in car-following,

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Observation 6ea51e8e-1147-4c57-934f-830d306e90c3 · outbound

This paper cites A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic,

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This paper cites Experimental study and modeling of car-following behavior under high speed situation,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Experimental study and modeling of car-following behavior under high speed situation,

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Observation 5c080072-3f83-413b-afc0-f5381df5f964 · outbound

This paper cites Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches,

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This paper cites Method for investigating intradriver heterogeneity using vehi- cle trajectory data: A Dynamic Time Warping approach,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Method for investigating intradriver heterogeneity using vehi- cle trajectory data: A Dynamic Time Warping approach,

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Observation cb35116a-1cbe-4b1b-9eec-c90b30216c00 · outbound

This paper cites Treiber and A.

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

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This paper cites Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation,

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This paper cites A pattern recognition algorithm for assessing trajectory completeness,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A pattern recognition algorithm for assessing trajectory completeness,

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Observation be3a93d5-3765-4cef-8b19-9efcdf9f573a · outbound

This paper cites Behavior Measurement, Analysis, and Regime Classification in Car Following,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Behavior Measurement, Analysis, and Regime Classification in Car Following,

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Observation 0f4fe4e2-722b-4398-8195-1fa486e1479a · outbound

This paper cites Statistical Analysis of Driver Behavior Data in Different Regimes of the Car-Following Stage,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Statistical Analysis of Driver Behavior Data in Different Regimes of the Car-Following Stage,

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Observation cf04b6ff-8d75-44bc-a1de-8478323506f4 · outbound

This paper cites Statistical inference for two-regime stochastic car- following models,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Statistical inference for two-regime stochastic car- following models,

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Observation b2b3d738-066f-43cf-9a60-3ea7679b1330 · outbound

This paper cites A Recurrent Neural Network Based Microscopic Car Following Model to Predict Traffic Oscillation,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A Recurrent Neural Network Based Microscopic Car Following Model to Predict Traffic Oscillation,

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This paper cites Learning Car-Following Behaviors for a Connected Automated Vehicle System: An Improved Sequence-to-Sequence Deep Learning Model,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Learning Car-Following Behaviors for a Connected Automated Vehicle System: An Improved Sequence-to-Sequence Deep Learning Model,

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This paper cites A Sequence-to- Sequence Car-Following Model for Addressing Driver Reaction Delay and Cumulative Error in Multi-Step Prediction,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics A Sequence-to- Sequence Car-Following Model for Addressing Driver Reaction Delay and Cumulative Error in Multi-Step Prediction,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.940513Z

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Observation cebe32aa-108c-44c5-b46e-4bcccdda7a42 · outbound

This paper cites On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.786761Z

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Observation 3c63f393-ffd8-40ea-98df-3a21c4e897c9 · outbound

This paper cites Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.694885Z

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This paper cites SEGMENTING TIME SERIES: A SURVEY AND NOVEL APPROACH,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics SEGMENTING TIME SERIES: A SURVEY AND NOVEL APPROACH,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.581003Z

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Observation 742ac0ba-40f8-4ff3-93c1-9284158b1919 · outbound

This paper cites Statistical Test for 85th and 15th Percentile Speeds with Asymptotic Distribution of Sample Quantiles,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Statistical Test for 85th and 15th Percentile Speeds with Asymptotic Distribution of Sample Quantiles,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.427859Z

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Observation 2f033e34-7e6b-4199-a605-05e743ab66be · outbound

This paper cites Long memory is important: A test study on deep-learning based car-following model,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Long memory is important: A test study on deep-learning based car-following model,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.318627Z

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Observation ffc6f17f-d025-4700-93df-dae7e164a935 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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This paper cites Analyses of the stability and wave properties of a new con- tinuum traffic theory,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Analyses of the stability and wave properties of a new con- tinuum traffic theory,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.196474Z

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Observation 2d27a249-e8aa-484e-8463-23acf85ca1a5 · outbound

This paper cites Learning-Based Adaptive Opti- mal Control for Connected Vehicles in Mixed Traffic: Robustness to Driver Reaction Time,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Learning-Based Adaptive Opti- mal Control for Connected Vehicles in Mixed Traffic: Robustness to Driver Reaction Time,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:23:00.091718Z

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Observation ebe71d21-16f3-41f5-8d82-7a150a1dc085 · outbound

This paper cites Integrated Decision and Control: Toward Interpretable and Computationally Efficient Driving Intelligence,.

A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics Integrated Decision and Control: Toward Interpretable and Computationally Efficient Driving Intelligence,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:59.968536Z

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