Big data analytics for smart transportation - Neoh, Rachael Li Yii (Nanyang Technological University, 2021)
Today, large cities in China are experiencing severe traffic congestion and, on the road, situations are bound to arise. Traffic situations worsen with such heavy congestion demanding a dire need for improvements to better help commuters in China to optimize their transport by making well-informed decisions. By drawing insights from traffic data, the Ministry of Transport of the People’s Republic of China (MOT) will be able to better plan the position of traffic lights and road cameras to improve the traffic condition. Better planning can help to avoid traffic congestion, traffic accidents and even improve the economy with a more efficient traffic network to supplement the road infrastructure. This project is to develop a user-friendly tagging tool for a road network to assist the user in analysing the traffic data to produce insights to authorities such as MOT to optimize their commute in China. This report will go in-depth into how this project will achieve this through the use of Python programming language, JavaScript programming language, Flask framework and AMap web mapping services.