Smart City Gnosys

Smart city article details

Title	Internet Taxi Trip Prediction Based On Multi-Source Data Fusion
ID_Doc	33112
Authors	Du C.; Samonte M.J.C.
Year	2024
Published	Advances in Transdisciplinary Engineering, 61
DOI	http://dx.doi.org/10.3233/ATDE240767
Abstract	In the context of smart city and intelligent transportation system, net taxi demand prediction becomes a key research point. In this paper, we propose a CNN-BiLSTM-Attention model based on deep learning for net taxi demand prediction. By converting net taxi GPS data and weather data, etc. into rasterized representations to adapt to the model input requirements, and using CNN to capture spatial features, combined with BiLSTM and attention mechanism to mine temporal features, the model is able to accurately predict the demand for net taxis in a specific area of the city. The experiment adopts the GPS data of network taxi in Chengdu city, and the results show that the model has higher prediction accuracy compared with the traditional models (e.g., ARIMA, CNN, BiLSTM), which provides an important support for the intelligent transportation system. © 2024 The Authors.
Author Keywords	City traffic; data fusion; deep learning; trajectory data; trip prediction

Similar Articles

Id	Similarity	Authors	Title	Published
38444	0.893	Wu M.; Zhu C.; Chen L.	Multi-Task Spatial-Temporal Graph Attention Network For Taxi Demand Prediction	ACM International Conference Proceeding Series (2020)
52523	0.891	Shu P.; Sun Y.; Zhao Y.; Xu G.	Spatial-Temporal Taxi Demand Prediction Using Lstm-Cnn	IEEE International Conference on Automation Science and Engineering, 2020-August (2020)
60161	0.89	Wu Y.; Zhang H.; Li C.; Tao S.; Yang F.	Urban Ride-Hailing Demand Prediction With Multi-View Information Fusion Deep Learning Framework	Applied Intelligence, 53, 8 (2023)
21159	0.884	Yang T.; Tang X.; Liu R.	Dual Temporal Gated Multi-Graph Convolution Network For Taxi Demand Prediction	Neural Computing and Applications, 35, 18 (2023)
5524	0.884	Xue X.; Zhou C.; Zhang X.; Guo J.	A Taxi Demand Prediction Model Based On Spectral Domain Graph Convolution	Proceedings of SPIE - The International Society for Optical Engineering, 12613 (2023)
2199	0.881	Liu X.-H.; Guo L.-M.; Yang B.-W.	A Hybrid Network Model Based On The Construction Of Virtual Service Areas For Taxi Demand Prediction	4th International Conference on Intelligent Robotics and Control Engineering, IRCE 2021 (2021)
54463	0.88	Askari B.; Le Quy T.; Ntoutsi E.	Taxi Demand Prediction Using An Lstm-Based Deep Sequence Model And Points Of Interest	Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 (2020)
52828	0.875	Bhanu M.; Priya S.; Moreira J.M.; Chandra J.	St-Agp: Spatio-Temporal Aggregator Predictor Model For Multi-Step Taxi-Demand Prediction In Cities	Applied Intelligence, 53, 2 (2023)
12932	0.853	Kumar R.; Bhanu M.; Roy S.; Mendes-Moreira J.; Chandra J.	Bts-Z: A Bootstrap Zero-Shot Learning Approach For City Traffic Forecasting	International Symposium on Advanced Networks and Telecommunication Systems, ANTS (2024)
34857	0.852	Elmi S.; Kian-Lee T.	Learned Taxi Fare For Real-Life Trip Trajectories Via Temporal Resnet Exploration	ACM International Conference Proceeding Series (2020)