Automatic classification of citizen requests for transportation using deep learning: Case study from Boston city

Abstract

Responding to requests from citizens is an essential administrative service that affects the daily life of people. The drastic increase in the volume of citizen requests in recent years has necessitated on-going studies on the automatic classification of citizen requests due to the time, effort, and misclassification errors involved in manual classification. Even though there have been prior studies that have analyzed citizen requests according to topic and frequency, they ignore the complicated and dynamic nature of such a dataset. Using a deep learning algorithm, this study proposes an automatic classification model for unstructured data by using transportation-related citizen requests from January 15th, 2016 until November 7th, 2018 of the City of Boston, USA, as an example. A combination of unsupervised and supervised learning was applied to the data. To address the issue of imbalance in data, this study also considered an equalization method. Five stepwise models were applied to increase the classification accuracy for the unstructured data. The final model uses achieved a classification accuracy of 90%. The model proposed in this study is expected to be generalized for classification of other citizen requests or unstructured text data on specific topics in the future. Moreover, this study has substantial academic importance given that it has proven diverse machine learning-related theories through their application to unstructured data.