Route Selection and Trajectory Filling in Mobility Traces
Resumo
The feasibility of vehicular networks is directly related to the understanding of mobility patterns, which is a necessary knowledge for the elaboration and application of novel algorithms and technologies for such networks. However, there are few traces that represent urban mobility with details and precision. The main objective of this dissertation is to characterize the mobility of vehicles in urban environments and to use this information to propose an algorithm to generate and enrich data in this environment.
Referências
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Çatay, B. and Keskin, M. (2017). The impact of quick charging stations on the route planning of electric vehicles. In IEEE Symposium on Computers and Communications, 2017, pages 152-157. IEEE.
Cotta, L., de Melo, P. O. V., and Loureiro, A. A. (2017). Understanding the role of mobility in real mobile ad-hoc networks connectivity. In IEEE Symposium on Computers and Communications, 2017, pages 1098-1103. IEEE.
Diniz, G. R., Cunha, F. D., and Loureiro, A. A. (2017). On the characterization of vehicular mobility. In Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2017, pages 23-29. ACM.
Domingues, A. C. (2018). Route selection and trajectory filling in mobility traces. Master's thesis, Federal University of Minas Gerais.
Garcia, J. C., Avendaño, A., and Vaca, C. (2018). Where to go in brooklyn: Nyc mobility patterns from taxi rides. In World Conference on Information Systems and Technologies, pages 203-212. Springer.
Hou, X., Li, Y., Jin, D., Wu, D. O., and Chen, S. (2016). Modeling the impact of mobility on the connectivity of vehicular networks in large-scale urban environments. IEEE Transactions on Vehicular Technology, 65(4):2753-2758.
Lu, M., Lai, C., Ye, T., Liang, J., and Yuan, X. (2017). Visual analysis of multiple route choices based on general gps trajectories. IEEE Transactions on Big Data, 3(2):234-247.
Lu, Z., Feng, Y., Zhou, W., Li, X., and Cao, Q. (2018). Inferring correlation between user mobility and app usage in massive coarse-grained data traces. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4):153.
Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009). Crawdad dataset epfl-mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224.
Qiao, L., Shi, Y., and Chen, S. (2017a). An empirical study on the temporal structural characteristics of vanets on a taxi gps dataset. IEEE Access, 5:722-731.
Qiao, Y., Si, Z., Zhang, Y., Abdesslem, F. B., Zhang, X., and Yang, J. (2017b). A hybrid markov-based model for human mobility prediction. Neurocomputing.
Sadri, A., Salim, F. D., and Ren, Y. (2017). Full trajectory prediction: what will you do the rest of the day? In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pages 189-192. ACM.
Silva, F. A., Celes, C., Boukerche, A., Ruiz, L. B., and Loureiro, A. A. (2015). Filling the gaps of vehicular mobility traces. In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 47-54. ACM.
Wang, D., Liu, Q., Xiao, Z., Chen, J., Huang, Y., and Chen, W. (2017). Understanding travel behavior of private cars via trajectory big data analysis in urban environments. In Proceedings of the 15th International Conference on Dependable, Autonomic and Secure Computing, pages 917-924. IEEE.
Wang, P., Liu, G., Fu, Y., Zhou, Y., and Li, J. (2018a). Spotting trip purposes from taxi trajectories: A general probabilistic model. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3):29.
Wang, Y., Qin, K., Chen, Y., and Zhao, P. (2018b). Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi gps data. ISPRS International Journal of Geo-Information, 7(1):25.
Xia, F., Wang, J., Kong, X., Wang, Z., Li, J., and Liu, C. (2017). Exploring human mobility patterns in urban scenarios: A trajectory data perspective. IEEE Communications Magazine.
Yao, Z. (2018). Exploiting human mobility patterns for point-of-interest recommendation. In WSDM, 2018, pages 757-758. ACM.
Çatay, B. and Keskin, M. (2017). The impact of quick charging stations on the route planning of electric vehicles. In IEEE Symposium on Computers and Communications, 2017, pages 152-157. IEEE.
Cotta, L., de Melo, P. O. V., and Loureiro, A. A. (2017). Understanding the role of mobility in real mobile ad-hoc networks connectivity. In IEEE Symposium on Computers and Communications, 2017, pages 1098-1103. IEEE.
Diniz, G. R., Cunha, F. D., and Loureiro, A. A. (2017). On the characterization of vehicular mobility. In Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2017, pages 23-29. ACM.
Domingues, A. C. (2018). Route selection and trajectory filling in mobility traces. Master's thesis, Federal University of Minas Gerais.
Garcia, J. C., Avendaño, A., and Vaca, C. (2018). Where to go in brooklyn: Nyc mobility patterns from taxi rides. In World Conference on Information Systems and Technologies, pages 203-212. Springer.
Hou, X., Li, Y., Jin, D., Wu, D. O., and Chen, S. (2016). Modeling the impact of mobility on the connectivity of vehicular networks in large-scale urban environments. IEEE Transactions on Vehicular Technology, 65(4):2753-2758.
Lu, M., Lai, C., Ye, T., Liang, J., and Yuan, X. (2017). Visual analysis of multiple route choices based on general gps trajectories. IEEE Transactions on Big Data, 3(2):234-247.
Lu, Z., Feng, Y., Zhou, W., Li, X., and Cao, Q. (2018). Inferring correlation between user mobility and app usage in massive coarse-grained data traces. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4):153.
Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009). Crawdad dataset epfl-mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224.
Qiao, L., Shi, Y., and Chen, S. (2017a). An empirical study on the temporal structural characteristics of vanets on a taxi gps dataset. IEEE Access, 5:722-731.
Qiao, Y., Si, Z., Zhang, Y., Abdesslem, F. B., Zhang, X., and Yang, J. (2017b). A hybrid markov-based model for human mobility prediction. Neurocomputing.
Sadri, A., Salim, F. D., and Ren, Y. (2017). Full trajectory prediction: what will you do the rest of the day? In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pages 189-192. ACM.
Silva, F. A., Celes, C., Boukerche, A., Ruiz, L. B., and Loureiro, A. A. (2015). Filling the gaps of vehicular mobility traces. In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 47-54. ACM.
Wang, D., Liu, Q., Xiao, Z., Chen, J., Huang, Y., and Chen, W. (2017). Understanding travel behavior of private cars via trajectory big data analysis in urban environments. In Proceedings of the 15th International Conference on Dependable, Autonomic and Secure Computing, pages 917-924. IEEE.
Wang, P., Liu, G., Fu, Y., Zhou, Y., and Li, J. (2018a). Spotting trip purposes from taxi trajectories: A general probabilistic model. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3):29.
Wang, Y., Qin, K., Chen, Y., and Zhao, P. (2018b). Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi gps data. ISPRS International Journal of Geo-Information, 7(1):25.
Xia, F., Wang, J., Kong, X., Wang, Z., Li, J., and Liu, C. (2017). Exploring human mobility patterns in urban scenarios: A trajectory data perspective. IEEE Communications Magazine.
Yao, Z. (2018). Exploiting human mobility patterns for point-of-interest recommendation. In WSDM, 2018, pages 757-758. ACM.
Publicado
06/05/2019
Como Citar
DOMINGUES, Augusto C. S. A.; LOUREIRO, Antonio A. F.; SILVA, Fabrício A..
Route Selection and Trajectory Filling in Mobility Traces. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 2. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 97-104.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2019.7775.
