Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
This work addresses travel time prediction for commuters, but it is incremental as it applies existing neural network methods to a specific dataset.
The study tackled travel time prediction for public transportation by developing MLP and LSTM neural network models using an Origin-Destination travel time matrix from historical GPS data, with results showing both models achieved near-accurate predictions but LSTM was more susceptible to noise over time.
Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.