This paper presents two novel approaches for uncertainty estimation adapted and extended the multi-link bus travel time problem. The is modeled directly as part of recurrent artificial neural networks, but using fundamentally different approaches: one based on Deep Quantile Regression other Bayesian network. Both use a network to predict multiple steps into future, handle time-dependent differe...

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing problems in metropolitan. Prediction travel time providing information significant factors this process. In research not only components Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model a regression prediction presen...

This paper aims at predicting bus travel time and its day-to-day variability using a range of independent variables including traffic flow data. Among many factors impacting bus travel time, existing prediction approaches have not considered a traffic measure making their predictions unresponsive to the time dependent fluctuations in traffic flow and dynamic changes in traffic congestion. In ad...