Masterclass Certificate in Smart City Traffic Data Modeling Techniques
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⢠Introduction to Smart Cities ← defines the concept of smart cities, their benefits, and the role of traffic data modeling techniques. ⢠Traffic Data Collection Methods ← covers various data collection methods, including manual counting, sensors, video detection, GPS, and Bluetooth. ⢠Data Preprocessing Techniques ← discusses data cleaning, normalization, transformation, and aggregation techniques. ⢠Traffic Flow Theory ← explains fundamental traffic flow concepts, such as speed-flow relationships, capacity, and queueing. ⢠Traffic Simulation Tools ← introduces microscopic and macroscopic traffic simulation tools, such as VISSIM, SimTraffic, and Paramics. ⢠Machine Learning Techniques for Traffic Data Modeling ← covers regression, time series, and neural network models for traffic prediction. ⢠Real-time Traffic Monitoring ← discusses real-time traffic monitoring techniques, including floating car data, probe vehicles, and loop detectors. ⢠Intelligent Transportation Systems ← introduces ITS technologies, such as adaptive signal control, ramp metering, and traffic responsive systems. ⢠Traffic Data Visualization ← explains how to present traffic data through maps, charts, and graphs, using tools such as Tableau, Power BI, and QlikView. ⢠Evaluation Metrics for Traffic Data Modeling ← covers various metrics, such as root mean square error, mean absolute percentage error, and coefficient of determination, for evaluating the performance of traffic data models.
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