Smart City Gnosys

Smart city article details

Title	Context-Aware Destination And Time-To-Destination Prediction Using Machine Learning
ID_Doc	15923
Authors	Tsiligkaridis A.; Zhang J.; Paschalidis I.C.; Taguchi H.; Sakajo S.; Nikovski D.
Year	2022
Published	ISC2 2022 - 8th IEEE International Smart Cities Conference
DOI	http://dx.doi.org/10.1109/ISC255366.2022.9922593
Abstract	The rapid adoption of Internet-connected devices (i.e., smart phones, smart cars, etc.) in today's society has given rise to a massive amount of data that can be harnessed by intelligent systems to learn and model the behavior of people. One useful set of such data is movement data, which can readily be obtained via GPS or motion-detection sensors, and which can be used to create models of user movement. One relevant application task based on this type of data is destination prediction, where movement data are used to form highly customized models that can forecast intended user destinations based on partially observed trajectories. In this work, we present a two-stage predictive model for destination prediction and Time-To-Destination (TTD) estimation using movement trajectories and contextual information. Our two-stage approach uses a Transformer-based architecture to predict an intended destination and a regression model to estimate how many steps must be traversed before a destination is reached. We showcase experimental results on various trajectory datasets and show that our proposed approach is able to yield significant destination prediction improvements over previous state-of-the-art methods and can also produce accurate TTD estimates. © 2022 IEEE.
Author Keywords	deep learning; Destination prediction; machine learning; smart city; transformer

Similar Articles

Id	Similarity	Authors	Title	Published
40085	0.878	Fan Z.; Yang X.; Yuan W.; Jiang R.; Chen Q.; Song X.; Shibasaki R.	Online Trajectory Prediction For Metropolitan Scale Mobility Digital Twin	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (2022)
39208	0.874	Li P.; Wang Z.; Zhang X.; Wang P.; Liu K.	Next Arrival And Destination Prediction Via Spatiotemporal Embedding With Urban Geography And Human Mobility Data	Mathematics, 13, 5 (2025)
2005	0.872	Boukhedouma H.; Meziane A.; Hammoudi S.; Benna A.	A Grid-Based And A Context-Oriented Trajectory Modeling For Mobility Prediction In Smart Cities	Lecture Notes in Networks and Systems, 906 LNNS (2024)
29612	0.871	Miao Q.; Li M.; Lin W.; Wang Z.; Shao H.; Xie J.; Shu N.; Qiao Y.	Human Mobility Prediction With Calibration For Noisy Trajectories	Electronics (Switzerland), 11, 20 (2022)
57812	0.87	Hocine B.; Abdelkrim M.; Slimane H.; Amel B.; Allel H.	Towards A Context-Based Mobility Prediction In Smart Cities: First Experimentations	Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023 (2023)
58950	0.864	Elmi S.; Tan K.-L.	Travel Time Prediction In Missing Data Areas: Feature-Based Transfer Learning Approach	Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020)
13471	0.863	Cheng J.; Huang J.; Zhang X.	Castle: A Context-Aware Spatial-Temporal Location Embedding Pre-Training Model For Next Location Prediction	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, 4/W2-2022 (2023)
23054	0.863	Chen C.; Zhang D.; Wang Y.; Huang H.	Enabling Smart Urban Services With Gps Trajectory Data	Enabling Smart Urban Services with GPS Trajectory Data (2021)
38052	0.858	Xu S.; Zhang R.; Cheng W.; Xu J.	Mtlm: A Multi-Task Learning Model For Travel Time Estimation	GeoInformatica, 26, 2 (2022)
17870	0.854	Pang Y.; Sekimoto Y.	Deep Learning For Destination Choice Modeling: A Fundamental Approach For National Level People Flow Reconstruction	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (2022)