Nowadays, the high demand for road transportation often reaches a point where it exceeds the capacity of freeway traffic networks, resulting in congestion. Freeway traffic congestion is a major social problem, as it is the reason for increased time delays, higher accident risk and environmental pollution. There is, therefore, the need for a sustainable solution that can be implemented on the existing road infrastructure. Freeway traffic control is considered as such a solution. It uses different control measures, in order to improve the performance of the freeway traffic network, by influencing the drivers’ behaviour.

The Variable Speed Limits (VSLs) are a traffic control measure that aims to increase traffic safety, improve traffic flow and reduce the environmental pollution. Towards the improvement of the freeway traffic flow, easy-to-implement VSL control algorithms are used as mainline metering control approach.

Two easy-to-implement VSL algorithms are reported, namely the Mainstream Traffic Flow Control (MTFC) and the Logic-Based control algorithm for Variable Speed Limits (LB-VSL). The algorithms are usually implemented in an non-adaptive framework. The main contribution of this thesis is that it proposes an adaptive framework for both algorithms, in which the critical density of the freeway traffic network at bottleneck’s location is estimated on-line. This estimated critical density is used to adjust the controllers’ parameters in real-time.

Three different estimation methods for the bottleneck’s critical density are studied, namely the Parameter Estimation (Parameterschatter) method, the Simple Derivative Estimation (SDE) and the Kalman-Filter-based derivative Estimation (KFE) methods. All three methods focus on the real-time estimation of the derivative of the Fundamental Diagram (FD), in order to produce estimations of the critical density.

A case study is performed to evaluate the performance of the three algorithms. A hypothetical 12 km long freeway stretch is used, which contains two VSLs installations and a lane-drop bottleneck. In the first part of the case study an accident scenario is studied, which decreases the critical density at the bottleneck’s location. The second part of the case study evaluates the three adaptive easy-to-implement VSL algorithms under the assumption of a decrease of the critical density across the whole network, due to bad weather conditions.