Classificação e Análise de Modelos de Predição de Trajetórias e de Destinos - Um Mapeamento Sistemático da Literatura
Abstract
Predicting routes and destinations has considerable relevance in the context of urban mobility, being useful to suggest detours, avoid traffic jams and optimize people's movements. Therefore, this research performs a classification and analysis of trajectory and destination prediction models in articles published from 2017 to 2022. These models were mapped considering: authors; if there was more than one geographic setting; type of prediction; use of semantic and contextual data; and the description of the algorithms. The result consists of discussions of representative works, based on the classification, with the grouping of techniques.
References
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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.
Published
2023-08-06
How to Cite
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: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (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.
