A dynamic prediction of travel time for transit time for transit vehicles in Brazil using GPS data

Abstract

The objective of this research is to develop a dynamic model that can provide accurate prediction for the Estimated Time of Arrival of a bus at a given bus stop using global positioning system (GPS) data which is collected in Macae, Brazil. The provision of timely and accurate travel time information of transit vehicles is valuable for both operators and passengers. It helps operators to monitor and manage their fleets in real time. It also allows passengers to plan their trips to minimize waiting times. Here, an artificial neural network (ANN) is developed for prediction due to its ability to solve complex non-linear relationships. The results obtained from the overall study are promising and the proposed ANN model can be used to implement an Advanced Public Transport System . The implementation of this system will improve the reliability of the public transport system, thus attracting more travelers to transit vehicles and helping relieve congestion. The performance of the proposed ANN model was compared with a historical average model under two criteria: overall precision and robustness. It was shown that the ANN outperformed the average approach

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