Congestion estimation and turning ratio prediction based on machine learning with applications in urban traffic light control

• Main Author: Chen, Qixing
• Other Authors: Su Rong
• Format: Thesis-Master by Research
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/143517
• Institution: Nanyang Technological University

Increasing transportation efficiency is an interesting and important problem. In the world with convenient means of ICTs, the concept of “smart city” emerged. In the meantime, a lot of data-driven traffic network optimization algorithms have also been developed and applied widely. However, the performance of some optimization algorithms can be improved with some pre-works added. This thesis discusses two such pre-works. The first pre-work is urban traffic network congestion region identification and prediction with two case studies at NTU campus and Jurong area, which utilizes the vehicle data (average speed, GPS-based location, heading direction) via V2X to analyse the traffic condition of each link. Links with similar congestion levels will be clustered together into a region. Our simulation-based case studies show that about 75% of the total queue delay could be reduced with good knowledge of congestion regions in the network. The second pre-work is about traffic network turning ratio prediction, which may be useful in developing more accurate network dynamic models. By constructing a recurrent neural network to predict the vehicle turning ratios at the next time step with prior or online-learned knowledge of network supply functions, traffic light schedules and historical vehicle turning ratios as inputs. This prediction model can be integrated with a real-time traffic signal control algorithm to form an adaptive closed-loop traffic signal control strategy, which in our simulated case studies decreases 24% of the delay time compared to the case without turning ratio prediction.