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Xiucheng Guo, Cong Qi, Ye Zhang
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Predicting and explaining lane-changing behaviour using machine learning: a comparative study
10.1016/j.trc.2022.103931 · 2022
Calibrating lane-changing models: two data-related issues and a general method to extract appropriate data
10.1016/j.trc.2023.104182 · 2023
Detecting, analysing, and modelling failed lane-changing attempts in traditional and connected environments
2020
CLACD: a complete lane-changing decision modeling framework for the connected and traditional environments
10.1016/j.trc.2021.103162 · 2021
On the new era of urban traffic monitoring with massive drone data: the pNEUMA large-scale field experiment
10.1016/j.trc.2019.11.023 · 2020
An integrated method based on wavelet modulus maxima and local hölder exponents for automatic phase detection and labelling of lane-changing execution
10.1016/j.trc.2025.105285 · 2025
MITRA: a drone-based trajectory data for an all-traffic-state inclusive freeway with ramps
10.1038/s41597-025-05472-0 · 2025
Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories
10.1016/j.aap.2024.107622 · 2024
Analyzing differences of highway lane-changing behavior using vehicle trajectory data
10.1016/j.physa.2023.128980 · 2023
Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data
10.1016/j.aap.2019.05.017 · 2019
A critical evaluation of the next generation simulation (NGSIM) vehicle trajectory dataset
10.1016/j.trb.2017.09.018 · 2017
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Quantifying risks of lane-changing behavior in highways with vehicle trajectory data under different driving environments
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Examining lane change gap acceptance, duration and impact using naturalistic driving data
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Applications of wavelet transform for analysis of freeway traffic
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Recent developments and research needs in modeling lane changing
10.1016/j.trb.2013.11.009 · doi-reference
Examining lane change gap acceptance, duration and impact using naturalistic driving data
10.1016/j.trc.2019.05.024 · doi-reference
Investigation of discretionary lane-change characteristics using next-generation simulation data sets
10.1080/15472450.2013.810994 · doi-reference
Modeling duration of lane changes
10.3141/1999-08 · doi-reference
B-spline curve fitting based on adaptive curve refinement using dominant points
10.1016/j.cad.2006.12.006 · doi-reference
An approach for accurately extracting vehicle trajectory from aerial videos based on computer vision
10.1016/j.measurement.2024.116212 · doi-reference
Trajectory data-based traffic flow studies: a revisit
10.1016/j.trc.2020.02.016 · doi-reference
Quantifying risks of lane-changing behavior in highways with vehicle trajectory data under different driving environments
10.1142/s0129183124501419 · doi-reference
Processing, assessing, and enhancing the waymo autonomous vehicle open dataset for driving behavior research
10.1016/j.trc.2021.103490 · doi-reference
A critical evaluation of the next generation simulation (NGSIM) vehicle trajectory dataset
10.1016/j.trb.2017.09.018 · doi-reference
Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data
10.1016/j.aap.2019.05.017 · doi-reference
Analyzing differences of highway lane-changing behavior using vehicle trajectory data
10.1016/j.physa.2023.128980 · doi-reference
Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories
10.1016/j.aap.2024.107622 · doi-reference
MITRA: a drone-based trajectory data for an all-traffic-state inclusive freeway with ramps
10.1038/s41597-025-05472-0 · doi-reference
An integrated method based on wavelet modulus maxima and local hölder exponents for automatic phase detection and labelling of lane-changing execution
10.1016/j.trc.2025.105285 · doi-reference
On the new era of urban traffic monitoring with massive drone data: the pNEUMA large-scale field experiment
10.1016/j.trc.2019.11.023 · doi-reference
CLACD: a complete lane-changing decision modeling framework for the connected and traditional environments
10.1016/j.trc.2021.103162 · doi-reference
Calibrating lane-changing models: two data-related issues and a general method to extract appropriate data
10.1016/j.trc.2023.104182 · doi-reference
Predicting and explaining lane-changing behaviour using machine learning: a comparative study
10.1016/j.trc.2022.103931 · doi-reference