Abstract
Transportation sector is the largest contributor to greenhouse gas emissions. Among all means of transportation (road, air, sea), road transportation has the greatest impact in terms of CO2 emissions in the atmosphere. In order to develop "smart" and sustainable cities and improve the health of the population, it is crucial to re-evaluate our use of various means of transportation for our daily travel to work or leisure and minimize the emissions of pollutants and greenhouse gases. Some smartphone applications currently offer routes to optimize greenhouse gas emissions, but these applications have limitations, particularly due to a lack of environmental data and a lack of multimodality regarding means of transportation (bicycles, walking, running, car, bus, metro, etc.). This paper aims to address these limitations by proposing an intelligent application for detecting the user travel mode based on smart phone sensors information and data from Geospatial Information System (GIS). Specifically, reliable transportation mode detection (TMD) algorithms using the real-time sensors data open new possibilities for travel optimization with minimum greenhouse gas emissions.
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Afghantoloee, A., Mostafavi, M.A., Gélinas, B. (2023). A Novel GIS-Based Machine Learning Approach for the Classification of Multi-motorized Transportation Modes. In: Mostafavi, M.A., Del Mondo, G. (eds) Web and Wireless Geographical Information Systems. W2GIS 2023. Lecture Notes in Computer Science, vol 13912. Springer, Cham. https://doi.org/10.1007/978-3-031-34612-5_8
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