Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover

Yu, Yi; Chen, Mengwei; Qi, Hongsheng; Wang, Dianhai

doi:10.1109/access.2020.2970530

Cited by 7 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation

Carbonera,

Ciavotta,

Messina

2024

Data Sci. Transp.

View full text Add to dashboard Cite

When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model, but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real sensor data. To further enhance the performance of our Generative Adversarial Network model, we employed a Variational AutoEncoder for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model compared to state-of-the-art models, based on copulae. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions.

show abstract