Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Abstract

Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.