Our network significantly outperforms the original graph pointer network for small and large-scale problems increasing its performance for TSP50 from 5. 959 to 5. 706 without utilizing 2opt, Pointer networks, Attention model, and a wide range of models, producing results comparable to highly tuned and specialized algorithms.
Ranked #1 on Traveling Salesman Problem on TSPLIB
Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures.
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.
This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms.
The univariate models were used to model the number of available bikes at each station.
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.
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.
The proposed algorithm then adds new points to the two nearest neighbours based on the distance and the angle between each new point and the orthogonal to the subspace.
The RF was used to compute the importance of the lane width predictors in our regression model based on two different measures.
This research aims to demonstrate the validity of this approach by comparing the accuracy of the new ML model for open-plan offices (OGE) to the accuracy of the existing glare metrics using local dataset.