Smart City Gnosys

Smart city article details

Title	Travel Time Prediction In Missing Data Areas: Feature-Based Transfer Learning Approach
ID_Doc	58950
Authors	Elmi S.; Tan K.-L.
Year	2020
Published	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
DOI	http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00196
Abstract	Travel time prediction based on deep learning techniques has received much attention in recent years. It is of great importance to advanced traffic management systems but remains a challenging problem as historical data are available in some but not all areas. Our preliminary experiments on New York City road network demonstrated that GPS pings recorded by in-vehicle GPS devices providing speed, position and time, do not cover the whole road network, thus, travel time in some regions could not be predicted accurately. However, predicting accurate travel time in each and every region in a city is fundamental to intelligent transportation systems. To predict travel time of target areas where little historical traffic data are available, we propose a transfer learning framework that exploits historical data of some source areas which are data-Abundant regions. To accurately predict travel time in source areas, we propose a hybrid architecture that models the spatial and temporal closeness as well as the road network typology. Experimental results on New York City road network show that our proposed model can achieve competitive performance comparing to baseline methods and classic regression models. © 2020 IEEE.
Author Keywords	Missing Data; Spatio-Temporal Analysis; Transfer Learning.; Travel Time Prediction

Similar Articles

Id	Similarity	Authors	Title	Published
17793	0.896	Vankdoth S.R.; Arock M.	Deep Intelligent Transportation System For Travel Time Estimation On Spatio-Temporal Data	Neural Computing and Applications, 35, 26 (2023)
18013	0.892	De Araujo A.C.; Etemad A.	Deep Neural Networks For Predicting Vehicle Travel Times	Proceedings of IEEE Sensors, 2019-October (2019)
7669	0.884	Jawad-Ur-Rehman C.; Ul Haq I.; Muneeb M.	An Attention-Based Recurrent Learning Model For Short-Term Travel Time Prediction	PLoS ONE, 17, 12 December (2022)
39060	0.879	Xu W.; Rong W.	Neural Network Model Based On Travel Planning For Travel Time Prediction	Journal of Physics: Conference Series, 1883, 1 (2021)
58952	0.878	Goudarzi F.	Travel Time Prediction: Comparison Of Machine Learning Algorithms In A Case Study	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (2019)
29827	0.876	Chen B.; Hu K.; Li Y.; Miao L.	Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2022-October (2022)
58949	0.876	Lan W.; Xu Y.; Zhao B.	Travel Time Estimation Without Road Networks: An Urban Morphological Layout Representation Approach	IJCAI International Joint Conference on Artificial Intelligence, 2019-August (2019)
52587	0.876	Priya K.; Omkar K.; Naresh R.; Venkatesh K.	Spatio-Temporal Traffic Prediction In Urban Area Using Convolutional Temporal Network System	6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings (2025)
2026	0.875	Chughtai J.-U.-R.; Haq I.U.; Islam S.U.; Gani A.	A Heterogeneous Ensemble Approach For Travel Time Prediction Using Hybridized Feature Spaces And Support Vector Regression	Sensors, 22, 24 (2022)
38582	0.873	Fan S.; Li J.; Lv Z.; Zhao A.	Multimodal Traffic Travel Time Prediction	Proceedings of the International Joint Conference on Neural Networks, 2021-July (2021)