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Found 9 papers, 0 papers with code
Effect of roundabout design on the behavior of road users: A case study of roundabouts with application of Unsupervised Machine Learning
In recent years, rotors have been increasingly used between countries due to their safety, capacity, and environmental advantages, and because they provide safe and fluid flows of vehicles for transit and integration.
How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning
This study investigates driving behavior at roundabouts in a mixed traffic environment using a data-driven unsupervised machine learning to classify driving behavior at three roundabouts in Germany.
Detection of DDoS Attacks in Software Defined Networking Using Machine Learning Models
The concept of Software Defined Networking (SDN) represents a modern approach to networking that separates the control plane from the data plane through network abstraction, resulting in a flexible, programmable and dynamic architecture compared to traditional networks.
Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data
A dataset of credit card transactions was used in this study.
Modeling bike counts in a bike-sharing system considering the effect of weather conditions
The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System.
Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains
This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms.
Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis
RQA features are added to traditional time domain features to investigate the classification accuracy when using binary, four-class, and five-class Random Forest classifiers.
Modeling bike availability in a bike-sharing system using machine learning
The univariate models were used to model the number of available bikes at each station.
A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin, TX
Results also show a similar usage pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different usage pattern for the average speed of e-bikes and e-scooters over the hours of the day.
