Classificação e Análise de Modelos de Predição de Trajetórias e de Destinos - Um Mapeamento Sistemático da Literatura
Resumo
Realizar predição de trajetórias e destinos tem relevância considerável no contexto de mobilidade urbana, sendo útil para sugerir desvios, evitar congestionamentos e otimizar deslocamentos de pessoas. Por isso, esta pesquisa realiza uma classificação e análise de modelos de predição de trajetórias e de destinos em artigos publicados no período de 2017 a 2022. Esses modelos foram mapeados considerando: autores; se houve mais de um cenário geográfico; tipo de predição; uso de dados semânticos e contextuais; e a descrição dos algoritmos. O resultado consiste nas discussões dos trabalhos representativos, a partir da classificação, com o agrupamento de técnicas.
Referências
Araújo, F. R. De; Rosário, D. L.; Machado, K.; Cerqueira, E. C.; Villas, L. (2019). “TEMMUS: A Mobility Predictor based on Temporal Markov Model with User Similarity.”. In: Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC). Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2019). Porto Alegre: Sociedade Brasileira de Computação. p. 594-607. ISSN 2177-9384. DOI: 10.5753/sbrc.2019.7389.
Arias, E. F.; Guinot, B. (2004). “Coordinated Universal Time UTC: historical background and perspectives”. SESSION II: MODELS FOR EARTH ROTATION: FROM POINCARÉ TO IAU 2000 43 Dehant V., de Viron O., Van Hoolst T.: Poincaré flow in the Earth’s core....... 45 Capitaine N., Wallace PT: Improvements in the precession-nutation models..... 49 Hilton JL: Progress report of the International Astronomical Union Division I Work, p. 254.
Barth, F.; Funke, S.; Jepsen, T. S.; Proissl (2020). “Scalable unsupervised multi-criteria trajectory segmentation and driving preference mining”. In: Proceedings Of The 9th Acm Sigspatial International Workshop On Analytics For Big Geospatial Data. Seattle Washington: ACM, 2020. p. 1-10. DOI: 10.1145/3423336.3429348.
Besse, P. C.; Guillouet, B.; Loube, J.-M.; Royer, F. (2018) “Destination prediction by trajectory distribution-based model”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 19, n. 8, p. 2470–2481, 2018. DOI: 10.1109/TITS.2017.2749413.
Bhuvaneswari, M.; Jyothirbindhu, V.; Paramasivan, B. (2017). “Statistical adaptive heuristic model supported driver intended destination and path prediction algorithm for vehicular ad hoc network” In: 2017 International Conference On Algorithms, Methodology, Models And Applications In Emerging Technologies (ICAMMAET). (Anais). IEEE, 2017. p. 1-6. DOI: 10.1109/ICAMMAET.2017.8186749.
Chen, Z.; Liu, H.; Wang, Y.; Duan, Z.; Chen, C. “E-dam: Encoder-decoder with attention mechanism for city-scale taxi trajectory prediction”. In: Proceedings Of The 2019 3rd International Conference On Computer Science And Artificial Intelligence. (Anais...). New York, NY, USA: ACM, 2019. DOI: 10.1145/3374587.3374610.
Choi, S.; Kim, J.; Yu, H.; Yeo, H. (2019). “Real-time prediction of arterial vehicle trajectories: An application to predictive route guidance for an emergency vehicle.”. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). (Anais...). IEEE, 2019. p. 4030-4036. DOI: 10.1109/ITSC.2019.8917122.
Dai, S.; Li, L.; Li, Z. (2019). “Modeling vehicle interactions via modified LSTM models for trajectory prediction”. IEEE Access, v. 7, p. 38287-38296, 2019. DOI: 10.1109/ACCESS.2019.2907000.
Druck, S.; Carvalho, M. S.; Câmara, G.; Monteiro, A. V. M. (2004). “Análise espacial de dados geográficos”. Brasília: Embrapa.
Ebel, P.; Gol, I. E.; Lingenfelder, C.; Vogelsang, A. (2020). “Destination prediction based on partial trajectory data”. In: 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE. p. 1149-1155.
Fan, H.; Yao, W. (2017). “A trajectory prediction method with sparsity data”. In: 2017 IEEE International Symposium On Parallel And Distributed Processing With Applications And 2017 IEEE International Conference On Ubiquitous Computing And Communications (ISPA/IUCC) (Anais...). IEEE. p. 1261-1265. DOI: 10.1109/ISPA/IUCC.2017.00191.
Fu, T.-Y.; Lee, W.-C. (2020). “Trembr: Exploring Road Networks for Trajectory Representation Learning”. ACM Transactions on Intelligent Systems and Technology, v. 11, n. 1, p. 1–25. DOI: 10.1145/3361741.
Imai, R.; Tsubouchi, K.; Konishi, T.; Shimosaka, M. (2018). “Early destination prediction with spatio-temporal user behavior patterns”. In: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies. v. 1, n. 4, p. 1-19. DOI: 10.1145/3161197.
Jiang, M.; Li, C.; Li, K.; Liu, H. (2022). “Destination prediction based on virtual POI docks in dockless bike-sharing system”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 23, n. 3, p. 2457–2470. DOI: 10.1109/TITS.2021.3099571.
Kitchenham, B.; Charters, S. (2007). “Guidelines for performing systematic literature reviews in software engineering”.
Lassoued, Y.; Monteil, J.; Gu, Y.; Russo G.; Shorten R.; Mevissen M. (2017). “A hidden Markov model for route and destination prediction.”. In: 2017 IEEE 20th International Conference On Intelligent Transportation Systems (ITSC). IEEE. p. 1-6. DOI: 10.1109/ITSC.2017.8317888.
Liang, Y.; Zhao, Z. (2022). “NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of the IEEE Intelligent Transportation Systems Council. v. 23, n. 9, p. 14470–14481, 2022. DOI: 10.1109/TITS.2021.3129588.
Liu, Lingbo; Qiu, Zhilin; Li, Guanbin; Wang, Qing; Ouyang, Wanli; Lin, Liang. (2019). “Contextualized spatial–temporal network for taxi origin-destination demand prediction”. IEEE Transactions on Intelligent Transportation Systems. v. 20, n. 10, p. 3875-3887. DOI: 10.1109/TITS.2019.2915525.
Ma, W.; Xie, H. (2021). “Research on prediction algorithm of vehicle trajectory in front based on driving intention classification.”. In: 2021 4TH International Conference on Data Science and Information Technology. (Anais...). New York, NY, USA: ACM, 2021. DOI: 10.1145/3478905.3478977.
Ning, F. (2021). “Prediction and detection of urban trajectory using data mining and deep neural network”. In: 2021 International Conference on Big Data Analysis and Computer Science (BDACS). (Anais...). IEEE, 2021. DOI: 10.1109/BDACS53596.2021.00017.
Qiao, S.; Han, N.; Wang J.; Li, R. -H; Gutierrez, L. A.; Wu, X. (2018). “Predicting long-term trajectories of connected vehicles via the prefix-projection technique”. IEEE transactions on intelligent transportation systems. v. 19, n. 7, p. 2305–2315, 2018. DOI: 10.1109/TITS.2017.2750075.
Rainbow, B. A.; Men, Q.; Shum, H. P. H. (2021). “Semantics-STGCNN: A semantics-guided spatial-temporal graph convolutional network for multi-class trajectory prediction”. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). (Anais...). IEEE. p. 2959-2966. DOI: 10.1109/SMC52423.2021.9658781.
Ren, C. et al. (2022). “Mapping grade-separated junctions in detail using crowdsourced trajectory data”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 23, n. 6, p. 5552–5561. DOI: 10.1109/TITS.2021.3054910.
Sadri, A. et al. (2018). “What will you do for the rest of the day?: An approach to continuous trajectory prediction”. In: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies. v. 2, n. 4, p. 1–26, 2018. DOI: 10.1145/3287064.
Santana, A. V.; Campos, J. (2017). “Travel history: Reconstructing semantic trajectories based on heterogeneous social tracks sources.”. In: Proceedings Of The 22nd Brazilian Symposium On Multimedia And The Web. (Anais...). New York, NY, USA: ACM. DOI: 10.1145/2976796.2976854.
Selvaraj, D. C. et al. (2021). “Edge learning of vehicular trajectories at regulated intersections.”. In: 2021 IEEE 94TH Vehicular Technology Conference (VTC2021-FALL). (Anais…). Norman, OK, USA: IEEE. p. 1-7. DOI: 10.1109/VTC2021-Fall52928.2021.9625570.
Tang, L.; Duan, Z.; Zhu, Y.; Ma, J.; Liu, Z. (2021). “Recommendation for ridesharing groups through destination prediction on trajectory data”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 22, n. 2, p. 1320–1333. DOI: 10.1109/TITS.2019.2961170.
Tong, P.; Du, W.; Li, M.; et al. (2021). “Last-mile school shuttle planning with crowdsensed student trajectories”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 22, n. 1, p. 293–306. DOI: 10.1109/TITS.2019.2956786.
Vahedian, A.; Zhou, X.; Tong, L.; Li, Y.; Luo, J. (2017). “Forecasting gathering events through continuous destination prediction on big trajectory data”. In: Proceedings Of The 25th Acm Sigspatial International Conference On Advances In Geographic Information Systems. (Anais....). New York, NY, USA: ACM, 2017. DOI: 10.1145/3139958.3140008.
Wang, L.; Yu, Z.; Guo, B.; Ku, T.; YI, F. (2017). “Moving destination prediction using sparse dataset: A mobility gradient descent approach”. ACM transactions on knowledge discovery from data. v. 11, n. 3, p. 1–33. DOI: 10.1145/3051128.
Weintrit, A. (2017). “The concept of time in navigation. TransNav the International Journal on Marine Navigation and Safety of Sea Transportation”. v. 11, n. 2, p. 23–33. DOI: 10.12716/1001.11.02.01.
Wu, H.; Wang, L.; Zheng, S.; Xu, Q.; Wang, J. (2020). “Crossing-road pedestrian trajectory prediction based on intention and behavior identification.” In: 2020 IEEE 23rd International Conference On Intelligent Transportation Systems (ITSC). (Anais…). Rhodes, Greece: IEEE, 2020. p.1–6. DOI: 10.1109/ITSC45102.2020.9294491.
Yuan, H.; Li, G. (2019). “). Distributed in-memory trajectory similarity search and join on road network.”. In: 2019 Ieee 35th International Conference On Data Engineering (ICDE). (Anais...). IEEE, 2019. p.1262–1273. DOI: 10.1109/ICDE.2019.00115.
Zhang, W.; Liu, Y.; Liu, T.; Yang, C. (2018). “Trajectory prediction with recurrent neural networks for predictive resource allocation”. In: 2018 14TH IEEE International Conference On Signal Processing (ICSP). (Anais...). IEEE. DOI: 10.1109/ICSP.2018.8652460.
Arias, E. F.; Guinot, B. (2004). “Coordinated Universal Time UTC: historical background and perspectives”. SESSION II: MODELS FOR EARTH ROTATION: FROM POINCARÉ TO IAU 2000 43 Dehant V., de Viron O., Van Hoolst T.: Poincaré flow in the Earth’s core....... 45 Capitaine N., Wallace PT: Improvements in the precession-nutation models..... 49 Hilton JL: Progress report of the International Astronomical Union Division I Work, p. 254.
Barth, F.; Funke, S.; Jepsen, T. S.; Proissl (2020). “Scalable unsupervised multi-criteria trajectory segmentation and driving preference mining”. In: Proceedings Of The 9th Acm Sigspatial International Workshop On Analytics For Big Geospatial Data. Seattle Washington: ACM, 2020. p. 1-10. DOI: 10.1145/3423336.3429348.
Besse, P. C.; Guillouet, B.; Loube, J.-M.; Royer, F. (2018) “Destination prediction by trajectory distribution-based model”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 19, n. 8, p. 2470–2481, 2018. DOI: 10.1109/TITS.2017.2749413.
Bhuvaneswari, M.; Jyothirbindhu, V.; Paramasivan, B. (2017). “Statistical adaptive heuristic model supported driver intended destination and path prediction algorithm for vehicular ad hoc network” In: 2017 International Conference On Algorithms, Methodology, Models And Applications In Emerging Technologies (ICAMMAET). (Anais). IEEE, 2017. p. 1-6. DOI: 10.1109/ICAMMAET.2017.8186749.
Chen, Z.; Liu, H.; Wang, Y.; Duan, Z.; Chen, C. “E-dam: Encoder-decoder with attention mechanism for city-scale taxi trajectory prediction”. In: Proceedings Of The 2019 3rd International Conference On Computer Science And Artificial Intelligence. (Anais...). New York, NY, USA: ACM, 2019. DOI: 10.1145/3374587.3374610.
Choi, S.; Kim, J.; Yu, H.; Yeo, H. (2019). “Real-time prediction of arterial vehicle trajectories: An application to predictive route guidance for an emergency vehicle.”. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). (Anais...). IEEE, 2019. p. 4030-4036. DOI: 10.1109/ITSC.2019.8917122.
Dai, S.; Li, L.; Li, Z. (2019). “Modeling vehicle interactions via modified LSTM models for trajectory prediction”. IEEE Access, v. 7, p. 38287-38296, 2019. DOI: 10.1109/ACCESS.2019.2907000.
Druck, S.; Carvalho, M. S.; Câmara, G.; Monteiro, A. V. M. (2004). “Análise espacial de dados geográficos”. Brasília: Embrapa.
Ebel, P.; Gol, I. E.; Lingenfelder, C.; Vogelsang, A. (2020). “Destination prediction based on partial trajectory data”. In: 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE. p. 1149-1155.
Fan, H.; Yao, W. (2017). “A trajectory prediction method with sparsity data”. In: 2017 IEEE International Symposium On Parallel And Distributed Processing With Applications And 2017 IEEE International Conference On Ubiquitous Computing And Communications (ISPA/IUCC) (Anais...). IEEE. p. 1261-1265. DOI: 10.1109/ISPA/IUCC.2017.00191.
Fu, T.-Y.; Lee, W.-C. (2020). “Trembr: Exploring Road Networks for Trajectory Representation Learning”. ACM Transactions on Intelligent Systems and Technology, v. 11, n. 1, p. 1–25. DOI: 10.1145/3361741.
Imai, R.; Tsubouchi, K.; Konishi, T.; Shimosaka, M. (2018). “Early destination prediction with spatio-temporal user behavior patterns”. In: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies. v. 1, n. 4, p. 1-19. DOI: 10.1145/3161197.
Jiang, M.; Li, C.; Li, K.; Liu, H. (2022). “Destination prediction based on virtual POI docks in dockless bike-sharing system”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 23, n. 3, p. 2457–2470. DOI: 10.1109/TITS.2021.3099571.
Kitchenham, B.; Charters, S. (2007). “Guidelines for performing systematic literature reviews in software engineering”.
Lassoued, Y.; Monteil, J.; Gu, Y.; Russo G.; Shorten R.; Mevissen M. (2017). “A hidden Markov model for route and destination prediction.”. In: 2017 IEEE 20th International Conference On Intelligent Transportation Systems (ITSC). IEEE. p. 1-6. DOI: 10.1109/ITSC.2017.8317888.
Liang, Y.; Zhao, Z. (2022). “NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of the IEEE Intelligent Transportation Systems Council. v. 23, n. 9, p. 14470–14481, 2022. DOI: 10.1109/TITS.2021.3129588.
Liu, Lingbo; Qiu, Zhilin; Li, Guanbin; Wang, Qing; Ouyang, Wanli; Lin, Liang. (2019). “Contextualized spatial–temporal network for taxi origin-destination demand prediction”. IEEE Transactions on Intelligent Transportation Systems. v. 20, n. 10, p. 3875-3887. DOI: 10.1109/TITS.2019.2915525.
Ma, W.; Xie, H. (2021). “Research on prediction algorithm of vehicle trajectory in front based on driving intention classification.”. In: 2021 4TH International Conference on Data Science and Information Technology. (Anais...). New York, NY, USA: ACM, 2021. DOI: 10.1145/3478905.3478977.
Ning, F. (2021). “Prediction and detection of urban trajectory using data mining and deep neural network”. In: 2021 International Conference on Big Data Analysis and Computer Science (BDACS). (Anais...). IEEE, 2021. DOI: 10.1109/BDACS53596.2021.00017.
Qiao, S.; Han, N.; Wang J.; Li, R. -H; Gutierrez, L. A.; Wu, X. (2018). “Predicting long-term trajectories of connected vehicles via the prefix-projection technique”. IEEE transactions on intelligent transportation systems. v. 19, n. 7, p. 2305–2315, 2018. DOI: 10.1109/TITS.2017.2750075.
Rainbow, B. A.; Men, Q.; Shum, H. P. H. (2021). “Semantics-STGCNN: A semantics-guided spatial-temporal graph convolutional network for multi-class trajectory prediction”. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). (Anais...). IEEE. p. 2959-2966. DOI: 10.1109/SMC52423.2021.9658781.
Ren, C. et al. (2022). “Mapping grade-separated junctions in detail using crowdsourced trajectory data”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 23, n. 6, p. 5552–5561. DOI: 10.1109/TITS.2021.3054910.
Sadri, A. et al. (2018). “What will you do for the rest of the day?: An approach to continuous trajectory prediction”. In: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies. v. 2, n. 4, p. 1–26, 2018. DOI: 10.1145/3287064.
Santana, A. V.; Campos, J. (2017). “Travel history: Reconstructing semantic trajectories based on heterogeneous social tracks sources.”. In: Proceedings Of The 22nd Brazilian Symposium On Multimedia And The Web. (Anais...). New York, NY, USA: ACM. DOI: 10.1145/2976796.2976854.
Selvaraj, D. C. et al. (2021). “Edge learning of vehicular trajectories at regulated intersections.”. In: 2021 IEEE 94TH Vehicular Technology Conference (VTC2021-FALL). (Anais…). Norman, OK, USA: IEEE. p. 1-7. DOI: 10.1109/VTC2021-Fall52928.2021.9625570.
Tang, L.; Duan, Z.; Zhu, Y.; Ma, J.; Liu, Z. (2021). “Recommendation for ridesharing groups through destination prediction on trajectory data”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 22, n. 2, p. 1320–1333. DOI: 10.1109/TITS.2019.2961170.
Tong, P.; Du, W.; Li, M.; et al. (2021). “Last-mile school shuttle planning with crowdsensed student trajectories”. IEEE Transactions On Intelligent Transportation Systems: A Publication Of The IEEE Intelligent Transportation Systems Council. v. 22, n. 1, p. 293–306. DOI: 10.1109/TITS.2019.2956786.
Vahedian, A.; Zhou, X.; Tong, L.; Li, Y.; Luo, J. (2017). “Forecasting gathering events through continuous destination prediction on big trajectory data”. In: Proceedings Of The 25th Acm Sigspatial International Conference On Advances In Geographic Information Systems. (Anais....). New York, NY, USA: ACM, 2017. DOI: 10.1145/3139958.3140008.
Wang, L.; Yu, Z.; Guo, B.; Ku, T.; YI, F. (2017). “Moving destination prediction using sparse dataset: A mobility gradient descent approach”. ACM transactions on knowledge discovery from data. v. 11, n. 3, p. 1–33. DOI: 10.1145/3051128.
Weintrit, A. (2017). “The concept of time in navigation. TransNav the International Journal on Marine Navigation and Safety of Sea Transportation”. v. 11, n. 2, p. 23–33. DOI: 10.12716/1001.11.02.01.
Wu, H.; Wang, L.; Zheng, S.; Xu, Q.; Wang, J. (2020). “Crossing-road pedestrian trajectory prediction based on intention and behavior identification.” In: 2020 IEEE 23rd International Conference On Intelligent Transportation Systems (ITSC). (Anais…). Rhodes, Greece: IEEE, 2020. p.1–6. DOI: 10.1109/ITSC45102.2020.9294491.
Yuan, H.; Li, G. (2019). “). Distributed in-memory trajectory similarity search and join on road network.”. In: 2019 Ieee 35th International Conference On Data Engineering (ICDE). (Anais...). IEEE, 2019. p.1262–1273. DOI: 10.1109/ICDE.2019.00115.
Zhang, W.; Liu, Y.; Liu, T.; Yang, C. (2018). “Trajectory prediction with recurrent neural networks for predictive resource allocation”. In: 2018 14TH IEEE International Conference On Signal Processing (ICSP). (Anais...). IEEE. DOI: 10.1109/ICSP.2018.8652460.
Publicado
06/08/2023
Como Citar
FIRMINO JÚNIOR, João Batista; DUTRA, Janderson Ferreira; NOBRE, Francisco Dantas.
Classificação e Análise de Modelos de Predição de Trajetórias e de Destinos - Um Mapeamento Sistemático da Literatura. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 15. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 41-50.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2023.229831.
