An artificial neural network-based model for predicting paratransit travel time

Abstract

The nature of paratransit services makes for increased uncertainty in trip time, leading to reported unreliability and dissatisfaction by the users. While providing travel information has proved helpful in formal bus services and has been recommended for paratransit setup, little is reported about efforts at providing information to paratransit users. This study focused on one strand of possible travel information that can be provided – Travel Time. An artificial neural network (ANN)-based model was developed to predict paratransit travel times, geared towards providing information to improve user experiences. The developed model was tested on a real-world paratransit bus route (minibus taxi) in Kumasi. A travel time survey that employed a mobile phone application was used to collect data onboard the vehicles on the study route. Two ANN models were trained. The first used only historical datasets, while the second incorporated real-time information. The results show that the model in which real-time information was included performed better than that trained with only historical data. The developed models were compared with a historical average model and a regression-based model, and the results showed that the ANN models outperformed the others. The study showed that the nature of paratransit services and the limitations of continuous data collection, notwithstanding, travel times of paratransit trips can be predicted to a reasonable level of accuracy, as can be relied upon in providing information to the users.