Evaluating Multi-Class Model Predictive Control

Evaluating Multi-Class Model Predictive Control

Van der Kleij, R.T.J.

Hoogendoorn, S.P. (mentor)
Schreiter, T. (mentor)
Van Lint, J.W.C. (mentor)
Berghout, E.A. (mentor)
Taale, H. (mentor)
Knoop, V.L. (mentor)
Wiggenraad, P.B.L. (mentor)

Civil Engineering and Geosciences

Transport & Planning

Transport & Planning

2013-02-25

Traffic Control is a part of Dynamic Traffic Management where traffic management measures are controlled to optimize the capacity of networks. Since September 2011 Traffic Management Scenarios are applied to the A15 highway in the Port of Rotterdam Area. Traffic Management Scenarios are the most advanced Traffic Control methods that are applied in practice. The current state of art in Traffic Control is Model Predictive Control, an adaptive method that calculates the optimal control signal and adjusts it to changing traffic states. In this study this method is compared with the current implemented Traffic Management Scenarios for the A15 highway eastbound. Since this highway has a high share of freight traffic from the port, traffic is divided into two user-classes and a multi-class variant of Model Predictive Control will also be compared. The goal of this study is: To make a quantitative comparison based on economic costs among Traffic management Scenarios, Single-class Model Predictive Control and Multi-class Predictive Control. To be able to make this comparison a literature review is done on traffic control, including the two control methodologies that will be compared in this thesis, and multi-class traffic management measures. A categorization of control methodologies will be made to illustrate how Traffic Management Scenarios and model Predictive Control relate. Here will be shown that Traffic Management Scenarios are adaptable methods but that Model Predictive Control is even more adaptable. The traffic management measures that can be controlled by both control methods, ramp metering and route guidance, will also be described. Only route guidance is applied by the current Traffic Management Scenario Since the used Traffic Management Scenario was created based on experience and Model Predictive Control does not exist in practice yet there is described how both methods should be compared. First some requirements have to be set. These requirements are that the both methods should use the same network, control the same signals and that these control signals will be determined based on the same input data. To analyze the results of both methods, they should produce the same sort of output data. The easiest way to do this is performing a simulation experiment where both Traffic Management Scenario and Model Predictive Control use the same traffic model with a control module in it. The control module then can be replaced by either the Traffic Management Scenario, the Model Predictive Control or remain empty. BOS-HbR is a framework that fulfills these requirements and is therefore used for this study. It uses the A15 highway as its network. BOS-HbR consists of a estimation and prediction component. In the estimation component the input data retrieved from loop detectors is converted to a traffic state which serves as input for the prediction component. The prediction component uses multi-class model Fastlane to predict the traffic state and predict the results of the control method which will be inserted here. The Traffic Management Scenario used for the current study is the ‘A15 Haven Uit’ scenario developed by Regiodesk. For the current study a Traffic Management Scenario is created within BOS-HbR with the same (de)activation triggers as ‘A15 Haven Uit’. The Model Predictive Controller used in BOS-HbR will use the Matlab function fmincon as its optimization algorithm. The simulation experiment will be executed for three cases: a heavy peak hour, a regular peak hour and a severe accident. For each case a validation will be done to check if the model predictions for Fastlane matched reality. Also for each of these cases the experiments will be done with 5 demand levels - 90%, 95%, 100%, 105% and 110% of the original expected demand - to measure the robustness of the control methods. For the Traffic Management Scenario the conditions for the rerouting signal at Spijkenisse to be turned on will be described and there will be explained that road users will only comply with this signal if the off-ramp to the alternative route is congestion-free. The variables to be adjusted for the Model Predictive Controller are control interval, control horizon and prediction horizon. The results of these experiments are discussed basis of the following performance indicators: Total cost, average travel time per user class and robustness. In the cases of the heavy peak hour applying single-class Model Predictive Control shows double the improvement Traffic Scenarios achieved. In the regular peak hour this improvement was less and in the accident case the relative differences were minimal. In all cases single-class Model Predictive Control performs better than Traffic Management Scenarios, which shows a good improvement over the situation where no traffic control is applied. Multi-class Model Predictive Control has small improvements over single-class Model Predictive Control especially when looked at user-class specific travel times. The multi-class controller reroutes exclusively passenger car traffic and keeps the trucks on the main road. All control cases show an equal sensitivity to demand fluctuations. Overall it can be concluded that Model Predictive Control shows approximately the same improvement over Traffic Management Scenarios as the latter does over a situation where no traffic control is applied. Since Traffic Management Scenarios performed well in this study it is recommended to apply Traffic Management Scenarios with route guidance to more locations in the Netherlands where this is possible. It can also clear the road for a future implantation of Model Predictive Control. The Traffic Management Scenarios currently used are designed based on experience, it is interesting to see how Traffic Management Scenarios that are designed and optimized with a traffic model will perform. Rerouting the traffic multi-class showed good results for the Model Predictive Controller, therefore researching rerouting multi-class with a Traffic Management Scenario could also be interesting for the Port Area. Some interesting topics for further research following from this study are applying other traffic management measures except rerouting in the Port area and a behavioral research on how traffic responds to the DRIP signals that guide it, because in this research assumptions on compliance to these signals were made.

Model Predicitive Control
Traffic Management Scenarios
A15

http://resolver.tudelft.nl/uuid:c642fd4f-b07b-4d69-99b2-f8e0b98ec8de

2013-03-01

Student theses

master thesis

(c) 2013 Van der Kleij, R.T.J.