Smart City Gnosys

Smart city article details

Title	In-Memory Deep Learning Computations On Gpus For Prediction Of Road Traffic Incidents Using Big Data Fusion
ID_Doc	31037
Authors	Aqib M.; Mehmood R.; Alzahrani A.; Katib I.
Year	2020
Published	EAI/Springer Innovations in Communication and Computing
DOI	http://dx.doi.org/10.1007/978-3-030-13705-2_4
Abstract	A staggering 1.25 million people die and up to 50 million people suffer injuries annually due to road traffic crashes around the world, causing great socio-economic and environmental damages. Road collisions are a major cause of road congestion. The cost of congestion to the US economy, alone, exceeded 305 billion USD in 2017. Smart infrastructure developments have accelerated the pace of technological advancements and the penetration of these technologies to all spheres of everyday life including transportation. The use of GPS devices to collect data, image processing and artificial intelligence (AI) for traffic analysis, and autonomous driving are but a few examples. This paper brings together transport big data, deep learning, in-memory computing, and GPU computing to predict traffic incidents on the road. Three different kinds of datasets—road traffic, vehicle detector station (VDS), and incident data—are combined together to predict road traffic incidents. The data is acquired from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). We have analyzed over 10 years of road traffic data. This work-in-progress paper reports incident prediction results using 3 months’ data, September to November 2017. The data fusion methodology is explained in detail along with the algorithms. The results for various configurations of deep convolution neural networks are given. Conclusions are drawn from the current status of the results and ideas for future improvements are given. © 2020, Springer Nature Switzerland AG.
Author Keywords	Big data; Data fusion; Deep learning; GPUs; In-memory computing; Incident prediction; Smart cities; Smart transportation

Similar Articles

Id	Similarity	Authors	Title	Published
26125	0.897	Perafan-Villota J.C.; Mondragon O.H.; Mayor-Toro W.M.	Fast And Precise: Parallel Processing Of Vehicle Traffic Videos Using Big Data Analytics	IEEE Transactions on Intelligent Transportation Systems, 23, 8 (2022)
1247	0.892	Wang M.; Lee W.-C.; Liu N.; Fu Q.; Wan F.; Yu G.	A Data-Driven Deep Learning Framework For Prediction Of Traffic Crashes At Road Intersections	Applied Sciences (Switzerland), 15, 2 (2025)
4738	0.89	Aqib M.; Mehmood R.; Alzahrani A.; Katib I.	A Smart Disaster Management System For Future Cities Using Deep Learning, Gpus, And In-Memory Computing	EAI/Springer Innovations in Communication and Computing (2020)
26856	0.889	Angadi V.S.; Halyal S.	Forecasting Road Accidents Using Deep Learning Approach: Policies To Improve Road Safety	Journal of Soft Computing in Civil Engineering, 8, 4 (2024)
3392	0.886	Joseph L.M.I.L.; Goel P.; Jain A.; Rajyalakshmi K.; Gulati K.; Singh P.	A Novel Hybrid Deep Learning Algorithm For Smart City Traffic Congestion Predictions	Proceedings of IEEE International Conference on Signal Processing,Computing and Control, 2021-October (2021)
51592	0.886	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
42732	0.88	Lin K.-Y.; Liu P.-Y.; Wang P.-K.; Hu C.-L.; Cai Y.	Predicting Road Traffic Risks With Cnn-And-Lstm Learning Over Spatio-Temporal And Multi-Feature Traffic Data	Proceedings - 2023 IEEE International Conference on Software Services Engineering, SSE 2023 (2023)
17943	0.877	Attioui M.; Lahby M.	Deep Learning-Based Congestion Forecasting: A Literature Review And Future	Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023 (2023)
58657	0.877	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)
40920	0.876	Abdullah S.M.; Periyasamy M.; Kamaludeen N.A.; Towfek S.K.; Marappan R.; Kidambi Raju S.; Alharbi A.H.; Khafaga D.S.	Optimizing Traffic Flow In Smart Cities: Soft Gru-Based Recurrent Neural Networks For Enhanced Congestion Prediction Using Deep Learning	Sustainability (Switzerland), 15, 7 (2023)