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Bus transportation plays a vital role in recent society especially in urban area. If the arrival time of buses at their respective destinations is accurate, the usage of private vehicles, fuel consumption, and traffic congestion can be reduced. In today era, most of the industries, universities, colleges, etc., provide the facility of transportation to their employees, staff, and students to pick up and drop from the pre-scheduled stoppages.
Researchers at Lovely Professional University (LPU) in Jalandhar, India, have recently developed an artificial neural network (ANN)-based model that can predict bus arrival times by analyzing historical GPS data. Their method, outlined in a paper published in Springer’s Pervasive Computing: A Networking Perspective and Future Directions employs ANNs and radial basis function (RBF) techniques to predict bus arrival and departure times by analyzing data collected using GPS technology.
“In this work, artificial neural networks (ANNs) and radial basis function (RBF) have been applied to data collected through GPS,” the researchers wrote in their paper. “Real-time prediction of bus arrival time has a number of applications for cargo delivery, transit services and areas of logistics.”
The researchers trained and evaluated these methods on two specific bus routes, that from Amritsar to the LPU campus and vice versa. For each model, they calculated the mean absolute error (MAE), which essentially measures the difference between the target time and predicted time, and root-mean-square error (RMSE), which measures the average magnitude of the error.
“While the results are encouraging, there are still a number of extensions to the model that should be studied,” the researchers wrote in their paper. “In future work, researchers could propose a new scheme that can compute the real-time predictions of bus arrival or departure time, such as variability in passenger demand at any given bus stop, traffic congestion measures, signals including progression, delay due to traffic congestion or accident, incident information etc.”
ANN-based models could greatly enhance the performance and efficiency of current transportation systems, enabling more accurate arrival time predictions. In their study, Aditya Khamparia and Rubina Choudhary, two researchers at LPU, set out to develop a model that can predict bus arrival times with minimum error, which could significantly reduce passenger waiting times.
In their research they considered major points that affects bus arrival time and their delays like road conditions, traffic, speed, distance, stop times and time spent into boarding and deboarding and weather conditions and then they mapped this with route of the bus.
Khamparia and Choudhary fed the data collected to both a feed-forward back-propagation algorithm (BPA) and RBF, training them to make predictions about future bus arrival times. Finally, they used these two models to forecast bus arrival times and compared their performance.