Time and Distance Gaps of Primary-Secondary Crashes Prediction and Analysis Using Random Forests and SHAP Model

Exploring built environment factors that influence the market share of ridesourcing service

(2022) Hongtai Yang et al.   APPLIED GEOGRAPHY

Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree

(2021) Longzhu Xiao et al.   Sustainable Cities and Society

Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP

(2021) Xiao Wen et al.   ACCIDENT ANALYSIS AND PREVENTION

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study

(2021) Arshad Jamal et al.   International Journal of Injury Control and Safety Promotion

How does the suspension of ride-sourcing affect the transportation system and environment?

(2021) Hongtai Yang et al.   TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT

A hybrid machine learning model for predicting Real-Time secondary crash likelihood

(2021) Pei Li et al.   ACCIDENT ANALYSIS AND PREVENTION

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

(2019) Gaowei Xu et al.   SENSORS

Lane-changes prediction based on adaptive fuzzy neural network

(2018) Jinjun Tang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Multi-model Ensemble for Short-Term Traffic Flow Prediction under Normal and Abnormal Conditions

(2018) Xiqun (Michael) Chen et al.   IET Intelligent Transport Systems

Random forest regression for online capacity estimation of lithium-ion batteries

(2018) Yi Li et al.   APPLIED ENERGY

Use of ubiquitous probe vehicle data for identifying secondary crashes

(2017) Hong Yang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Probability of Secondary Crash Occurrence on Freeways with the Use of Private-Sector Speed Data

(2017) Noah J. Goodall   TRANSPORTATION RESEARCH RECORD

Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

(2016) Haoyuan Hong et al.   GEOMORPHOLOGY

Identification of freeway secondary accidents with traffic shock wave detected by loop detectors

(2016) Junhua Wang et al.   SAFETY SCIENCE

A novel methodology to predict urban traffic congestion with ensemble learning

(2016) G. Asencio-Cortés et al.   SOFT COMPUTING

Real-time prediction of secondary incident occurrences using vehicle probe data

(2016) Hyoshin Park et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data

(2016) Chengcheng Xu et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Development of a Secondary Crash Identification Algorithm and occurrence pattern determination in large scale multi-facility transportation network

(2015) Afrid A. Sarker et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Methods for Defining Spatiotemporal Influence Areas and Secondary Incident Detection in Freeways

(2013) Maria-Ioanna M. Imprialou et al.   JOURNAL OF TRANSPORTATION ENGINEERING

Multivariate random forest models of estuarine-associated fish and invertebrate communities

(2013) K Miller et al.   MARINE ECOLOGY PROGRESS SERIES

Modeling the Effects of Weather and Traffic on the Risk of Secondary Incidents

(2012) Eleni I. Vlahogianni et al.   Journal of Intelligent Transportation Systems

Spatiotemporal Patterns of Primary and Secondary Incidents on Urban Freeways

(2011) Hongbing Zhang et al.   TRANSPORTATION RESEARCH RECORD

Multivariate random forests

(2011) Mark Segal et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery