Multi Criteria Decision Support (MDS)

In order to balance the available services in the given traffic scenarios between the prioritized targets, PSIroads/MDS uses the multi-criteria decision technology Qualicision. In Qualicision, the dependencies between services and targets are not coded into arithmetic systems of equations as is usual for optimization problems.
Often these are not isolated events, but complex traffic situations.

Choosing the right measures to influence traffic is not an easy task and requires extensive experience and local traffic management know-how.

Types of measures

Bypass

Reduce inflow

 

Drain heights

Partially conflicting optimization objectives

Minimize travel time

Reduce emissions

Preferably use your own infrastructure to solve problem

PSIroads/MDS supports the traffic manager by proposing groups of services in the form of bypasses or changes in inflow and outflow on road sections that optimize the traffic situation. The proposed services are selected because under the given boundary conditions they are best suited to mediate between sometimes conflicting traffic management objectives.

Road operators usually try to solve traffic problems with services on their own roads. However, in special or particularly critical situations it may be useful and necessary to be supported by other operators in the region. PSIroads/MDS supports management of cooperation agreements for the using third party services and their situation-specific application. Road users benefit from an improvement of the overall traffic situation regardless of the operator; at the same time, the interaction processes between the control rooms are significantly simplified.

Background

Qualicision stands for qualified decision support in the optimization of business processes. With Qualicision-optimized business processes, the interactions are recorded in the form of matrices (effect matrices) on the basis of the process data. Mathematical conflict and compatibility analysis (CT analysis) are applied to the effect matrices to determine the decision alternatives which must be selected in order to archieve the process objectives as closely as possible. From a technical point of view, the CT analysis makes the so-called combinatorial variety of control options controllable with regard to the optimization of the KPIs. In the course of supporting the PSI convergence strategy, PSI FLS is currently developing a release based on PSI GUI technology. All elements of the Qualicision data modelling can thus be represented. This includes KPI target functions, effect matrices, KPI relationship matrices, the corresponding editors, the data tables and other visualization functionalities. A demo version is available.
Our colleagues at PSI FLS Fuzzy Logik & Neuro Systeme GmbH will be happy to explain details to you - info@fuzzy.de

Strategy Cockpit for Further Influencing Factors

Traffic management measures are not only determined by the volume of traffic itself.

A strategy cockpit was introduced in PSIroads/MDS to take into account other influencing factors such as different day types, times of day or weather conditions.

This approach also includes the reduction of traffic pollutant emissions based on strategic, legal or internal requirements in the decisions regarding the applicable measures.

All factors influence the optimization of the regional and supra-regional traffic flow according to the defined priority.

Monitored self-learning

Decision making in PSIroads/MDS is supported by a self-learning module in order to archieve continous process improvement.


The differences between the measures proposed by PSIroads/MDS and those applied by the operators are recorded.

The self-learning module then changes the priorities of the different influencing factors so that the measures applied by the operators for comparable future decision proposals are reproduced as much as possible.

Traffic engineers check the changed priorities before they are applied by the system (monitored self-learning).

The self-learning module is not only useful for the optimization of the system itself. Gaps in the parameterized traffic model, training backlogs for traffic managers or even systematic errors in traffic management can be detected within the quality assurance process.