There are many ordinary planning apps inside the market. A few assist their customers plan their customers plan. A few assist their customer’s planned trips by means of using maps to discover the shortest path. Sometimes the usual user for maps that they know to be a faster route tends to be longer travel time. Other direction making plan apps tried to triumph over this issue by means of having their customers switch on GPS. Based at the amassed users of GPS in a specific region, the app can expect a high number of users. Similarly, some apps also comprise traffic reviews and tweets into their optimised version.
These strategies depend upon the consumer’s approval to show their GPS and tweeting about site visitors’ situation. Some customers may wish to use the app in an offline mode and do not turn on their GPS because of privacy worries, and we cannot rely on users to continually tweet approximate traffic situation. Categorisation of site visitor’s density are subjective. One person's definition of heavy traffic is sometimes just an ordinary day for everyday visitors.
By utilising the LTA traffic cameras to measure traffic density, we provide an addition method that is not dependent on users' GPS and reports. The intention is not always to update course planning app, but rather to offer an assistance that can be used to enhance the existing path making plans.
Highlights
In this overview session, you'll learn :
Workflow to develop a real time traffic density prediction
Techniques to pre-process your images and understand their importance
Applying transfer-learning method to classify traffic condition
Export MATLAB Deep Learning Model for ONNX (Open Neural Network Exchange) production environment
Explore how to build ensemble model to improve accuracy
System Implementation using ONNX runtime and flask (Web framework)