Air Pollution Calculation for Location Based Social Networks Multimodal Routing Service
With the growth of the urban population, the urban mobility infrastructure suffers several types of problems, such as the more significant occurrence of traffic jams, which directly affects the quality of life of the population and the inhabitants who need to use different types of transport, also generating a more extraordinary occurrence of air pollution emitted by vehicles. This work addresses the need to integrate the generation of hybrid multimodal routes through the analysis of geographic data collected from location-based social networks, adding the calculation of greenhouse gas emissions by used vehicles. Further, performs a user experience analysis for the main identified flows of the analyzed urban environment, for users and urban planners analysis. The proposed algorithm proves its efficiency by offering less expensive, healthier trips for the population.
Bolin, B. and Sundararaman, N. (1996). Revised 1996 IPCC guidelines for National Greenhouse Gas Inventories. OECD.
D'Ulizia, A., Grifoni, P., and Ferri, F. (2021). Query processing of geosocial data in location-based social networks. ISPRS International Journal of Geo-Information, 11(1):19.
Ferreira, A. P., Silva, T. H., and Loureiro, A. A. (2020). Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing. Computer Communications, 160:240-252.
Kalajdjieski, J., Zdravevski, E., Corizzo, R., Lameski, P., Kalajdziski, S., Pires, I. M., Garcia, N. M., and Trajkovik, V. (2020). Air pollution prediction with multi-modal data and deep neural networks. Remote Sensing, 12(24):4142.
Rodrigues, D., Santos, F., Rocha Filho, G., Akabane, A., Cabral, R., Immich, R., L. Junior, W., Cunha, F., Guidoni, D., Silva, T., Rosário, D., Cerqueira, E., Loureiro, A., and Villas, L. (2019). Computação urbana da teoria à prática: Fundamentos, aplicações e desafios. In Minicursos do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 51-91. SBC.
Rodrigues, D. O., Boukerche, A., Silva, T. H., Loureiro, A. A., and Villas, L. A. (2017). Smaframework: Urban data integration framework for mobility analysis in smart cities. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, pages 227-236.
Rodrigues, D. O., Boukerche, A., Silva, T. H., Loureiro, A. A., and Villas, L. A. (2018a). Combining taxi and social media data to explore urban mobility issues. Computer Communications, 132:111-125.
Rodrigues, D. O., Fernandes, J. T., Curado, M., and Villas, L. A. (2018b). Hybrid contextaware multimodal routing. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2250-2255. IEEE.
Silva, T. H., Viana, A. C., Benevenuto, F., Villas, L., Salles, J., Loureiro, A., and Quercia, D. (2019). Urban computing leveraging location-based social network data: a survey. ACM Computing Surveys (CSUR), 52(1):1-39.
Sobral, T., Galvão, T., and Borges, J. (2019). Visualization of urban mobility data from intelligent transportation systems. Sensors, 19(2):332.
Zawieska, J. and Pieriegud, J. (2018). Smart city as a tool for sustainable mobility and transport decarbonisation. Transport Policy, 63:39-50.
Zou, B., Li, S., Zheng, Z., Zhan, B. F., Yang, Z., and Wan, N. (2020). Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk. Computers, Environment and Urban Systems, 80:101456.