http://export.arxiv.org/pdf/1803.01254v1 SpletAbstract Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, which has attracted attention from the taxi industry and Mobility-on-Demand systems. Accurate predictions enable operators to dispatch their vehicles in advance, satisfying both drivers and passenger Show more Permanent link
Data-Driven Traffic Assignment: A Novel Approach for Learning …
Splet15. jul. 2024 · Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, which has attracted attention from the taxi industry and Mobility-on … Splet05. sep. 2024 · Li A, Axhausen K W. Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks. AGILE: GIScience Series, 2024, 1: 1-14. ... Zhang Y, Wang S, Chen B, et al. GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction, 2024 International Joint Conference on Neural Networks (IJCNN). … giveaway alert png
GitHub - jwwthu/GNN4Traffic: This is the repository for the collection
SpletTo apply CNN for large-scale spatio-temporal transportation prediction, some data prepro-cessing work is necessary. Zhang et al. (2024) split the whole city into grids with a pre-defined grid size and calculated the bike-sharing demand for each grid. The demand data, represented using grid maps, was converted into images by defining a color scale. Splet(GCNN) is an extension of CNN, which can deal with the data on an irregular domain [8]. By using differentaph gr structures, GCNN can capture the relationship between different … Splet27. nov. 2024 · To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, … giveawayandsweepstakes.com