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Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks
Vehicle acceleration and deceleration maneuvers at traffic signals results in significant fuel and energy consumption levels.
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.
