Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

Automated summary

Public transportation is essential for communities to carry out daily activities, with buses being a commonly used mass transportation method. The smart transportation concept combines technology and strategy, with smart ideas being crucial for IoT applications. This study utilizes Artificial Neural Network and Support Vector Machine algorithms to predict travel time for university shuttle buses, outperforming SVM. Recommendations for suitable routes are provided, with future directions for the field discussed.