**3.1 Overview of the proposed control framework**

The proposed framework is composed by two sub-models: the first one aims at the traffic lights decision variables optimisation whilst the second one aims at the vehicle control trough speed optimisation.

Furthermore, in terms of traffic management an on-line procedure based on the combination of a centralised method and a link metering approach is adopted.

Regarding the driver guidance this paper focuses on the implementation of GLOSA algorithm aiming to improve the traffic efficiency. The algorithm firstly calculates the distance and travel time to the front traffic signal, then calculate the target speed constrained to the traffic signal decision variables and then to the estimated travel times.

Two sub-models operate simultaneously, and an overview of the framework is displayed in **Figure 1**.

In particular, the vehicle control is actuated depending on the vehicles distance from the infrastructure, whilst the traffic control procedure operates every control interval as it will be further discussed in the following Section 3 focusing on the implementation settings.

As already anticipated, the whole framework is composed by two sub- models:


In **Figure 2** a further overview of the whole framework including the vehicle control and in particular the traffic management, is shown then in the following a detailed description of each sub-model is provided.

Regarding the traffic control framework, this operates as a predictive control in which the network traffic control is the optimisation procedure, the proposed

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junction.

*Centralised Traffic Control and Green Light Optimal Speed Advisory Procedure in Mixed…*

traffic flow models are the plant models, the Kalman Filter acts as prediction model and the ELS algorithm [12] for unequipped vehicles location and speed estimation.

2.The traffic flow prediction and estimation model providing input flows for the

As already anticipated, in order to guarantee the consistency between the traffic signals decision variables and the traffic flow two prediction terms are applied: the first one is related to the traffic flow model which is predicted with reference to the prediction horizon (e.g. fifteen min) the second one is the rolling horizon of the control. Concerning the rolling horizon, it must be clarified that the optimisation procedure works every control interval and the traffic information are updated every roll period (e.g. five minutes). Finally, the traffic information is collected in

The second sub - model is represented by the on-board vehicle control procedure and operates depending on the vehicle distance as it will be discussed in more detail in Section 5 about the algorithm explication. As already anticipated in the introduction in this paper the S-GLOSA algorithm has been implemented. Therefore, the considered traffic control method is able to consider the interaction among junctions, whilst the vehicles control is applied only to the vehicles approaching each

implementation of a traffic signal centralised approach;

c.The unequipped vehicles status estimation;

*DOI: http://dx.doi.org/10.5772/intechopen.95247*

Then this framework is composed by:

*Further description of the framework overview.*

1.The microscopic traffic flow model;

a.The rolling horizon approach;

general every sub-interval (e.g. five seconds).

3.The traffic control procedures.

b.The KF;

**Figure 2.**

**Figure 1.** *Overview of the proposed control framework.*

*Centralised Traffic Control and Green Light Optimal Speed Advisory Procedure in Mixed… DOI: http://dx.doi.org/10.5772/intechopen.95247*

**Figure 2.** *Further description of the framework overview.*

traffic flow models are the plant models, the Kalman Filter acts as prediction model and the ELS algorithm [12] for unequipped vehicles location and speed estimation.

Then this framework is composed by:

	- a.The rolling horizon approach;

b.The KF;


As already anticipated, in order to guarantee the consistency between the traffic signals decision variables and the traffic flow two prediction terms are applied: the first one is related to the traffic flow model which is predicted with reference to the prediction horizon (e.g. fifteen min) the second one is the rolling horizon of the control. Concerning the rolling horizon, it must be clarified that the optimisation procedure works every control interval and the traffic information are updated every roll period (e.g. five minutes). Finally, the traffic information is collected in general every sub-interval (e.g. five seconds).

The second sub - model is represented by the on-board vehicle control procedure and operates depending on the vehicle distance as it will be discussed in more detail in Section 5 about the algorithm explication. As already anticipated in the introduction in this paper the S-GLOSA algorithm has been implemented. Therefore, the considered traffic control method is able to consider the interaction among junctions, whilst the vehicles control is applied only to the vehicles approaching each junction.
