Congestion estimation and turning ratio prediction based on machine learning with applications in urban traffic light control
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...
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| Format: | Thesis-Master by Research |
| Language: | English |
| Published: |
Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/143517 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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