Improving the Quality of Public Transportation by Dynamically Adjusting the Bus Departure Time

Nowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit time is a critical issue. To this end, we propose a bus operation prediction model based on deep learning techniques, and use this model to dynamically adjust the bus departure time to improve the bus service quality. Specifically, we first combine bus fare card data and open data, such as weather conditions and traffic accidents, to build models for predicting the number of passengers who board/alight the bus at a stop, the boarding and alighting time, and the bus running time between stops. Then we combine these models to predict the operation of the bus for deciding the best bus departure time within the bus departure interval. Experimental results on real-world data of Taichung City bus route #300 show that our approach to deciding the bus departure time is effective for improving its service quality.