Enabling GLOSA for on-street operating traffic light controllers

Enabling GLOSA for on-street operating traffic light controllers

Glastra, Thom (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Intelligent Vehicles)

Gavrila, Dariu (graduation committee)
Kooij, Julian (mentor)
Wang, Meng (graduation committee)

Delft University of Technology

Mechanical Engineering

2020-04-15

The bottleneck of the maximum road volume in urban areas is the maximum capacity of the traffic flow on the intersection, which is coordinated with Traffic Light Controllers (TLCs). A promising method to decrease the number of stops are Green Light Optimal Speed Advice (GLOSA) systems. These systems will give a speed advice to arriving vehicles based on the schedule of TLCs, which needs to be known and fixed. However, most on-street controllers change their schedule until the last moment to maximize the performance. In this thesis a predictive controller is developed that is suitable for real-world application based on DIRECTOR; a state-of-the-art predictive controller. A prediction model is used to predict future arrivals based on available measurements to optimize and fix the schedule in advance. The proposed controller can enable GLOSA systems to improve performance. Appropriate pre-processing steps are implemented and the optimal input features are selected to improve the performance of a Long Short-Term Memory (LSTM) network to predict future arrivals. All detection data is stationary over time by using the differenced series. The time data is divided into workdays and weekend days to create a binary input and undesirable jumps during midnight are removed. The combination of stop line detectors, queue detectors, arrival detectors and signal states of the controlled and preceding intersections as input maximized the performance. The prediction horizon of the proposed prediction model could be extended. The Normalized Root Mean Square Error (NRMSE) decreased with 17% compared to DIRECTOR. The proposed controller extends the control horizon and uses multiple prediction models to predict the arrivals for the entire control horizon. The proposed controller outperforms DIRECTOR with 14 - 38% reduction in terms of vehicle delay and 5 - 32% reduction in terms of numbers of stops based on the scheduling mode. The GLOSA system is an add-on of the controller and is able to operate without the GLOSA system. The control horizon of the proposed controller always has a fixed length which is needed to determine the time until green. The implemented GLOSA system will determine the optimal speed based on the time until green and the expected delay due to the surrounding vehicles. The proposed controller is a cloud controlled application. Therefore, it is possible to adjust the setup (i.e. scheduling modes) during the day. Enabling GLOSA all day except during rush hours will lead to 3 - 4% reduction in terms of vehicle delay and 29 - 32% reduction in terms of numbers of stops based on the scheduling mode. This setup of the proposed controller is also competitive with the hand-crafted non-predictive on-street controller. Compared with this controller, the proposed controller will reduce the number of stops with 26% at the cost of 16% increase in vehicle delay. The proposed controller is designed conform the safety standards used on-street. Permission is received from the provincial government to do on-street pilots with the proposed controller. These results could give new insights into the performance of the proposed controller.

Traffic flow
Traffic light controller
TLC
Predictive controller
Prediction model
Decentralized control
Model predictive control
green light optimal speed advise
GLOSA

http://resolver.tudelft.nl/uuid:63a21739-a200-4375-8f19-641352e504b2

Student theses

master thesis

© 2020 Thom Glastra