.png){kind=icon}
Return
Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs
.png){kind=icon}
OA
AI
DOI：10.1080/21680566.2022.2164529.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a framework for short-term traffic breakdown probability calculation using a Variational LSTM neural network model. Considering that traffic breakdown is a stochastic event, this forecast framework was devised to produce distributions as outputs, which cannot be achieved using standard deterministic recurrent neural networks. Therefore, the model counts on the robustness of neural networks but also includes the stochastic characteristics of highway capacity. The framework consists of three main steps: (i) build and train a probabilistic speed forecasting neural network, (ii) forecast speed distributions with the trained model using Monte Carlo (MC) dropout, and therefore perform Bayesian approximation, and (iii) establish a speed threshold that characterizes breakdown occurrence and calculate breakdown probabilities based on the speed distributions. The proposed framework produced an efficient control over traffic breakdown occurrence, can deal with many independent variables or features, and can be combined with traffic management strategies.
Keywords:
Traffic breakdown
traffic forecasting
neural networks
Bayesian statistics
machine learning
Journal
IF:
3.4
Papers:
568
Citations:
1.2K
Organization
Cited Papers
ShareSaveShareSave
ShareSaveDelayed metamorphosis by larvae of benthic marine invertebrates: Does it occur? Is there a price to pay?.png)
Ophelia
OpheliaIF0
PREAI
ShareSave
View more