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Forecasting travel behaviour from crowdsourced data with machine learning based model

Book Contribution - Book Chapter Conference Contribution

Information and communication technologies have become integral part of our everyday lives. It seems as logical consequence that smart city concept is trying to explore the role of integrated information and communication approach in managing cityU+2019s assets and in providing better quality of life to its citizens. Provision of better quality of life relies on improved management of cityU+2019s systems (e.g., transport system) but also on provision of timely and relevant information to its citizens in order to support them in making more informed decisions. To ensure this, use of forecasting models is needed. In this paper, we develop support vector machine based model with aim to predict future mobility behavior from crowdsourced data. The crowdsourced data are collected based on dedicated smartphone app that tracks mobility behavior. Use of such forecasting model can facilitate management of smart cityU+2019s mobility system but also ensures timely provision of relevant pre-travel information to its citizens.
Book: Fifth International Conference on Data Analytics
Volume: 5
Pages: 93 - 99
Publication year:2016