Smart City Gnosys

Smart city article details

Title	Deep Intelligent Transportation System For Travel Time Estimation On Spatio-Temporal Data
ID_Doc	17793
Authors	Vankdoth S.R.; Arock M.
Year	2023
Published	Neural Computing and Applications, 35, 26
DOI	http://dx.doi.org/10.1007/s00521-023-08726-3
Abstract	Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model’s performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Author Keywords	Graph neural network; Intelligent transportation system; Spatio-temporal data; Traffic route finding; Travel time estimation

Similar Articles

Id	Similarity	Authors	Title	Published
58949	0.902	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)
29827	0.901	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)
44472	0.897	Nie X.; Peng J.; Wu Y.; Gupta B.B.; El-Latif A.A.A.	Real-Time Traffic Speed Estimation For Smart Cities With Spatial Temporal Data: A Gated Graph Attention Network Approach	Big Data Research, 28 (2022)
58657	0.897	Selvan C.; Senthil Kumar R.; Iwin Thanakumar Joseph S.; Malin Bruntha P.; Amanullah M.; Arulkumar V.	Traffic Prediction Using Gps Based Cloud Data Through Rnn-Lstm-Cnn Models: Addressing Road Congestion, Safety, And Sustainability In Smart Cities	SN Computer Science, 6, 2 (2025)
58950	0.896	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)
58592	0.895	Cenni D.; Han Q.	Traffic Flow Prediction Using Uber Movement Data	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 594 LNICST (2024)
1982	0.892	Sharma A.; Sharma A.; Nikashina P.; Gavrilenko V.; Tselykh A.; Bozhenyuk A.; Masud M.; Meshref H.	A Graph Neural Network (Gnn)-Based Approach For Real-Time Estimation Of Traffic Speed In Sustainable Smart Cities	Sustainability (Switzerland), 15, 15 (2023)
1395	0.89	Tripathi A.N.; Sharma B.	A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning	Lecture Notes in Mechanical Engineering (2023)
1927	0.888	Attoui S.-E.; Meddeb M.	A Generic Framework For Forecasting Short-Term Traffic Conditions On Urban Highways	2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021 (2021)
58653	0.887	Zafar N.; Haq I.U.; Sohail H.; Chughtai J.-U.-R.; Muneeb M.	Traffic Prediction In Smart Cities Based On Hybrid Feature Space	IEEE Access, 10 (2022)