Modeling of Merging Decision during Execution Period Based on Random Forest
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Modeling of Merging Decision during Execution Period Based on Random Forest
Authors
Keywords
-
Journal
JOURNAL OF ADVANCED TRANSPORTATION
Volume 2021, Issue -, Pages 1-11
Publisher
Hindawi Limited
Online
2021-02-04
DOI
10.1155/2021/6654096
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
- (2020) Gen Li et al. Journal of Transportation Engineering Part A-Systems
- Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time
- (2020) Zhihong Yao et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests
- (2019) Juan Cheng et al. JOURNAL OF ADVANCED TRANSPORTATION
- Cooperative Merging Strategy for Connected Vehicles at Highway On-Ramps
- (2019) Linghui Xu et al. Journal of Transportation Engineering Part A-Systems
- A tailored machine learning approach for urban transport network flow estimation
- (2019) Zhiyuan Liu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A Dynamic Predictive Traffic Signal Control Framework in a Cross-Sectional Vehicle Infrastructure Integration Environment
- (2019) Zhihong Yao et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Lane-changes prediction based on adaptive fuzzy neural network
- (2018) Jinjun Tang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis
- (2018) Gen Li et al. JOURNAL OF ADVANCED TRANSPORTATION
- Deviation between Actual and Shortest Travel Time Paths for Commuters
- (2018) Wenyun Tang et al. Journal of Transportation Engineering Part A-Systems
- Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach
- (2018) Kyungwon Kang et al. TRANSPORTATION RESEARCH RECORD
- Stability of CACC-manual heterogeneous vehicular flow with partial CACC performance degrading
- (2018) Hao Wang et al. Transportmetrica B-Transport Dynamics
- Application of Finite Mixture of Logistic Regression for Heterogeneous Merging Behavior Analysis
- (2018) Gen Li JOURNAL OF ADVANCED TRANSPORTATION
- Developing a disaggregate travel demand system of models using data mining techniques
- (2017) Milad Ghasri et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- Modeling Freeway Merging in a Weaving Section as a Sequential Decision-Making Process
- (2017) Xia Wan et al. Journal of Transportation Engineering Part A-Systems
- Generalized Gipps-Type Vehicle-Following Models
- (2017) Mostafa K. Ardakani et al. Journal of Transportation Engineering Part A-Systems
- Modeling Vehicle Merging Behavior in Work Zone Merging Areas During the Merging Implementation Period
- (2016) Jinxian Weng et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Random forest in remote sensing: A review of applications and future directions
- (2016) Mariana Belgiu et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Merging Preparation Behavior of Drivers: How They Choose and Approach Their Merge Positions at a Congested Weaving Area
- (2016) Xia Wan et al. JOURNAL OF TRANSPORTATION ENGINEERING
- A binary decision model for discretionary lane changing move based on fuzzy inference system
- (2016) Esmaeil Balal et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Merging Preparation Behavior of Drivers: How They Choose and Approach Their Merge Positions at a Congested Weaving Area
- (2016) Xia Wan et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease
- (2015) Tingting Ye et al. Brain Imaging and Behavior
- A gradient boosting method to improve travel time prediction
- (2015) Yanru Zhang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Incremental feature selection based on rough set in dynamic incomplete data
- (2014) Wenhao Shu et al. PATTERN RECOGNITION
- Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees
- (2013) Yi Hou et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Investigation of Discretionary Lane-Change Characteristics Using Next-Generation Simulation Data Sets
- (2013) Qi Wang et al. Journal of Intelligent Transportation Systems
- Merging behaviour: Empirical comparison between two sites and new theory development
- (2013) Florian Marczak et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Lane-Changing Decision Model for Heavy Vehicle Drivers
- (2012) Sara Moridpour et al. Journal of Intelligent Transportation Systems
- Classification and Regression Tree Approach for Predicting Drivers’ Merging Behavior in Short-Term Work Zone Merging Areas
- (2012) Qiang Meng et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Modeling speed-flow relationship and merging behavior in work zone merging areas
- (2011) Jinxian Weng et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data
- (2011) Vincenzo Punzo et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Variable selection using random forests
- (2010) Robin Genuer et al. PATTERN RECOGNITION LETTERS
- Natural Image Statistics and Low-Complexity Feature Selection
- (2009) M. Vasconcelos et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data
- (2009) Christian Thiemann et al. TRANSPORTATION RESEARCH RECORD
- Modeling Acceleration Decisions for Freeway Merges
- (2009) Charisma F. Choudhury et al. TRANSPORTATION RESEARCH RECORD
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now