Personalized Route Selection Methods in an Urban Computing Scenario

• Matheus Brito UFPA
• Eduardo Cerqueira UFPA
• Denis Rosário UFPA

DOI: https://doi.org/10.5753/sbrc_estendido.2024.1622

Resumo

With population growth in urban areas, the more extensive city infrastructure faces several problems affecting the population’s health and quality of life. In this context, smart urban mobility solutions perform a ubiquitous way of sensing the population mobility and the local mobility context, such as criminality, accidents, and air quality near the road infrastructure, complementing the city mobility. Likewise, Location-based Social Networks (LBSN) dispose of users’ geolocated data, allowing the identification of mobility patterns and alternative modal transport recommendations. This work develops two pollutionaware route selection approaches, a multi-modal hybrid routes method and a multi-criteria personalized route selection method, for urban citizens’ mobility flow improvement, attending to the urban mobility overload and deficiency. The hybrid multi-modal solution surpasses the single-modal, offering less expensive and less polluted trip options. Considering all calculated route possibilities, the multi-criteria personalized profile solution outperforms the single-criterion choice in the same context.

Referências

Brito, M. (2023). Personalized route selection methods in a urban computing scenario. Master’s thesis, Federal University of Pará.

Brito, M., Martins, B., Santos, C., Medeiros, I., Araujo, F., Seruffo, M., Oliveira, H., Cerqueira, E., and Rosário, D. (2023). Personalized experience-aware multi-criteria route selection for smart mobility. In Proceedings of the 41st Brazilian Symposium on Computer Networks and Distributed Systems. SBC.

Brito, M., Santos, C., Oliveira, H., Cerqueira, E., and Rosário, D. (2022). Air pollution calculation for location based social networks multimodal routing service. In Proceedings of the 6th Urban Computing Workshop (CoUrb), pages 280–293. SBC.

Brito, M. M. d., Santos, C. H. M. d., Martins, B. S., Medeiros, I. L. d., Seruffo, M. C. d. R., Cerqueira, E. C., and do Rosário, D. L. (2024). Context-aware multi-modal route selection for urban computing scenarios. Available at SSRN 4673005.

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.

Kaivonen, S. and Ngai, E. C.-H. (2020). Real-time air pollution monitoring with sensors on city bus. Digital Communications and Networks, 6(1):23–30.

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 context-aware multimodal routing. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2250–2255. IEEE.

Sarraf, R. and McGuire, M. P. (2020). Integration and comparison of multi-criteria decision making methods in safe route planner. Expert Systems with Applications, 154:113399.

Solé, L., Sammarco, M., Detyniecki, M., and Campista, M. E. M. (2022). Towards drivers’ safety with multi-criteria car navigation systems. Future Generation Computer Systems, 135:1–9.

Wu, M.-Y., Ke, C.-K., and Lai, S.-C. (2022). Optimizing the routing of urban logistics by context-based social network and multi-criteria decision analysis. Symmetry, 14(9):1811.

Zhang, D.-G., Wang, J.-X., Zhang, J., Zhang, T., Yang, C., and Jiang, K.-W. (2022). A new method of fuzzy multicriteria routing in vehicle ad hoc network. IEEE Transactions on Computational Social Systems, pages 1–13.

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.