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Title	Towards Intelligent And Dynamic Road Speed Adaptation Model In Smart Cities
ID_Doc	58188
Authors	Abi-Char P.E.; Ashtaiwi A.
Year	2020
Published	International Conference on Wireless and Mobile Computing, Networking and Communications, 2020-October
DOI	http://dx.doi.org/10.1109/WiMob50308.2020.9253378
Abstract	Smart cities integrate data collection and communication technologies to operate more efficiently in order to provide better services to citizens, improved road traffic situation, better economic development, etc. However, safety still a main concern of road traffic as road crashes pose serious threats to road users causing death and injury all over the world. Road traffic safety is the process of implementing measures to reduce the number of crashes, death and severe injuries. In this paper, using Machine Learning (ML) algorithms, Off-line, we train and test the Intelligent and Dynamic Adaptation Model (IDAM) on the road traffic data collected for 14 years of car crashes in United Kingdom (UK). IDAM then on-line determines the optimal speed limit for each road segment based on many predictors such as weather, road type, light condition, and many others, stated later. We train and test IDAM using three different machine learning algorithms: Artificial Neural Network (ANN), Decision Tree (DT), and Linear Regression (LR) with Stochastic Gradient Descent (SGD). Using the testing dataset, IDAM achieves prediction accuracy of around 96%. Implemented in an operation algorithm, IDAM can continuously adapt the road speed limit to an optimal value as the predictors change on-line.
Author Keywords	Intelligent Speed Adaptation; Road Safety; Road Speed Management; Smart Cities; Speed Limits

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