Abstract
Kangning Hou, Jia Zou, Fangfang Zheng, Zhengbing He
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
europepmc
Confidence 96%
openalex
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Predicting and explaining lane-changing behaviour using machine learning: A comparative study
10.1016/j.trc.2022.103931 · 2022
CLACD: A complete lane-changing decision modeling framework for the connected and traditional environments
10.1016/j.trc.2021.103162 · 2021
Lane-changing trajectory optimization to minimize traffic flow disturbance in a connected automated driving environment
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Vehicle platooning for merge coordination in a connected driving environment: A hybrid ACC-DMPC approach
10.1109/tits.2023.3252567 · 2023
How gaps are created during anticipation of lane changes
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Modeling the impact of lane-changing’s anticipation on car-following behavior
10.1016/j.trc.2023.104110 · 2023
Fundamental diagram estimation through passing rate measurements in congestion
10.1109/tits.2009.2018963 · 2009
Impact of lane-change maneuvers on congested freeway segment delays: Pilot study
10.1177/0361198106196500116 · 2006
Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp
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Traffic paradox on a road segment based on a cellular automaton: Impact of lane-changing behavior
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Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment
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The impact of a single discretionary lane change on surrounding traffic: An analytic investigation
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A generalised stability criterion for motorway traffic
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Enhancing mixed traffic safety assessment: A novel safety metric combined with a comprehensive behavioral modeling framework
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Cooperative vehicle platoon control considering longitudinal and lane-changing dynamics
2024
Cooperative on-ramp merging control model for mixed traffic on multi-lane freeways
10.1109/tits.2023.3274586 · 2023
Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system
10.1016/j.trc.2024.104497 · 2024
A kinematic wave theory of lane-changing traffic flow
10.1016/j.trb.2009.12.014 · 2010
A multi-commodity lighthill–whitham–richards model of lane-changing traffic flow
10.1016/j.trb.2013.06.002 · 2013
General lane-changing model MOBIL for car-following models
10.3141/1999-10 · 2007
Are facilities to support alternative modes effective in reducing congestion?: Modeling the effect of heterogeneous traffic conditions on vehicle delay at intersections
10.1016/j.multra.2022.100050 · 2023
Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model
10.1016/j.trb.2007.10.004 · 2008
Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways
10.1016/j.aap.2016.09.015 · 2017
Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories
10.1016/j.aap.2019.105345 · 2020
Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact
10.1016/j.physa.2016.09.022 · 2017
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
10.1016/j.trc.2021.103452 · 2022
Dynamic lane-changing trajectory planning for autonomous vehicles based on discrete global trajectory
10.1109/tits.2021.3083541 · 2022
Performance evaluation of surrogate measures of safety with naturalistic driving data
10.1016/j.aap.2021.106403 · 2021
Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns
10.1016/j.trb.2015.06.010 · 2015
A simplified car-following theory: a lower order model
10.1016/s0191-2615(00)00044-8 · 2002
Modeling the impacts of mandatory and discretionary lane-changing maneuvers
10.1016/j.trc.2016.05.002 · 2016
Lighthill-whitham-richards model for traffic flow mixed with cooperative adaptive cruise control vehicles
10.1287/trsc.2021.1057 · 2021
Longitudinal safety evaluation of connected vehicles’ platooning on expressways
10.1016/j.aap.2017.12.012 · 2018
Safety benefits of arterials’ crash risk under connected and automated vehicles
10.1016/j.trc.2019.01.029 · 2019
A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns
10.1016/j.aap.2021.106500 · 2022
DeepAD: An integrated decision-making framework for intelligent autonomous driving
2024
Interrupted and uninterrupted lane changes: a microscopic outlook of lane-changing dynamics
2022
Stochastic bottleneck capacity, merging traffic and morning commute
10.1016/j.tre.2014.02.003 · 2014
A data-driven lane-changing model based on deep learning
10.1016/j.trc.2019.07.002 · 2019
An ensemble deep learning approach for driver lane change intention inference
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Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas
10.1016/j.multra.2022.100005 · doi-reference
The effects of lane-changing on the immediate follower: Anticipation, relaxation, and change in driver characteristics
10.1016/j.trc.2012.10.007 · doi-reference
Recent developments and research needs in modeling lane changing
10.1016/j.trb.2013.11.009 · doi-reference
A learning-based discretionary lane-change decision-making model with driving style awareness
10.1109/tits.2022.3217673 · doi-reference
Analysis of the impact of maximum platoon size of CAVs on mixed traffic flow: An analytical and simulation method
10.1016/j.trc.2022.103989 · doi-reference
Examining lane change gap acceptance, duration and impact using naturalistic driving data
10.1016/j.trc.2019.05.024 · doi-reference
An ensemble deep learning approach for driver lane change intention inference
10.1016/j.trc.2020.102615 · doi-reference
A data-driven lane-changing model based on deep learning
10.1016/j.trc.2019.07.002 · doi-reference
Stochastic bottleneck capacity, merging traffic and morning commute
10.1016/j.tre.2014.02.003 · doi-reference
A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns
10.1016/j.aap.2021.106500 · doi-reference
Safety benefits of arterials’ crash risk under connected and automated vehicles
10.1016/j.trc.2019.01.029 · doi-reference
Longitudinal safety evaluation of connected vehicles’ platooning on expressways
10.1016/j.aap.2017.12.012 · doi-reference
Lighthill-whitham-richards model for traffic flow mixed with cooperative adaptive cruise control vehicles
10.1287/trsc.2021.1057 · doi-reference
Modeling the impacts of mandatory and discretionary lane-changing maneuvers
10.1016/j.trc.2016.05.002 · doi-reference
A simplified car-following theory: a lower order model
10.1016/s0191-2615(00)00044-8 · doi-reference
Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns
10.1016/j.trb.2015.06.010 · doi-reference
Performance evaluation of surrogate measures of safety with naturalistic driving data
10.1016/j.aap.2021.106403 · doi-reference
Dynamic lane-changing trajectory planning for autonomous vehicles based on discrete global trajectory
10.1109/tits.2021.3083541 · doi-reference
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
10.1016/j.trc.2021.103452 · doi-reference
Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact
10.1016/j.physa.2016.09.022 · doi-reference
Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories
10.1016/j.aap.2019.105345 · doi-reference
Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways
10.1016/j.aap.2016.09.015 · doi-reference
Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model
10.1016/j.trb.2007.10.004 · doi-reference
Are facilities to support alternative modes effective in reducing congestion?: Modeling the effect of heterogeneous traffic conditions on vehicle delay at intersections
10.1016/j.multra.2022.100050 · doi-reference
General lane-changing model MOBIL for car-following models
10.3141/1999-10 · doi-reference
A multi-commodity lighthill–whitham–richards model of lane-changing traffic flow
10.1016/j.trb.2013.06.002 · doi-reference
A kinematic wave theory of lane-changing traffic flow
10.1016/j.trb.2009.12.014 · doi-reference
Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system
10.1016/j.trc.2024.104497 · doi-reference
Cooperative on-ramp merging control model for mixed traffic on multi-lane freeways
10.1109/tits.2023.3274586 · doi-reference
Enhancing mixed traffic safety assessment: A novel safety metric combined with a comprehensive behavioral modeling framework
10.1016/j.aap.2024.107766 · doi-reference
A generalised stability criterion for motorway traffic
10.1016/s0191-2615(97)00021-0 · doi-reference
The impact of a single discretionary lane change on surrounding traffic: An analytic investigation
10.1109/tits.2022.3209668 · doi-reference
A bi-level cooperative driving strategy allowing lane changes
10.1016/j.trc.2020.102773 · doi-reference
Traffic paradox on a road segment based on a cellular automaton: Impact of lane-changing behavior
10.1016/j.physa.2015.02.043 · doi-reference
Impact of lane-change maneuvers on congested freeway segment delays: Pilot study
10.1177/0361198106196500116 · doi-reference
Fundamental diagram estimation through passing rate measurements in congestion
10.1109/tits.2009.2018963 · doi-reference
Modeling the impact of lane-changing’s anticipation on car-following behavior
10.1016/j.trc.2023.104110 · doi-reference
Vehicle platooning for merge coordination in a connected driving environment: A hybrid ACC-DMPC approach
10.1109/tits.2023.3252567 · doi-reference
CLACD: A complete lane-changing decision modeling framework for the connected and traditional environments
10.1016/j.trc.2021.103162 · doi-reference
Predicting and explaining lane-changing behaviour using machine learning: A comparative study
10.1016/j.trc.2022.103931 · doi-reference