Nowadays traffic management is a complex task. Policy choices have to be made and operational decisions have to be taken on how to deal with car, trucks, busses, bicycles and pedestrians in the network and how to keep them safe and flowing, especially where they interact. Without a clear and transparent framework to base these decisions upon, problems and arbitrariness could occur. Sustainable regional and urban mobility requires a widely supported vision on the desired use of the different networks. A multimodal network management framework offers such a vision. It is a translation from mobility policy into and clear and unambiguous description of the desired situation for the different networks. Which mode has priority on route X? What is the average travel time mode Y has to meet? Which operational speed is the target for the bus on relation A-B? To compare this ideal situation with the actual one, it becomes clear where challenges are and bottlenecks between modes become visible. For the bottlenecks, policy makers and traffic managers can formulate solution directions in line with the desired situation for the networks.

The translation from a mobility policy into the desired situation is done in six steps
1. Start the project and involve all relevant stakeholders. In this step the goals of the project should be clearly defined.
2. Determine the basic principles and list the policy objectives are listed. What are the current agreements and ambitions for traffic and transport in the city or region?
3. Determine accessibility profiles. Accessibility profiles are the basic principles, but more specific and further detailed per area.
4. Determine the functional arrangement and describe and map the intended use of the network.
5. Decide on priorities to facilitate and manage traffic on those links. Choices have to be made: between traffic of different modes and functions, but also between the same modes and different functions.
6. Formulate the frame of reference, combining all results from the previous steps. Also, the practical consequences of policy choices are covered.

In the paper the steps are described in more detail and some applications of the multimodal network management framework are given.@en

Welke eisen moet je aan verkeersdata stellen, wil je weggebruikers effectief kunnen informeren en sturen? Die vraag blijkt niet een-twee-drie te beantwoorden, zeker niet in regionaal verband. Reden voor het samenwerkingsverband Regionaal Verkeersmanagement Midden-Nederland om er in triple helix-verband grondig onderzoek naar te doen. In deze bijdrage vertellen de auteurs over de stand van zaken.@en

The Amsterdam Practical Trial (APT) is a multiyear program initiated by the Dutch Ministry of Transport and the Environment and is carried out in close cooperation by Rijkswaterstaat, the City of Amsterdam, the Province of North Holland, and the Amsterdam Metropolitan Region. The APT aims to integrate innovative roadside and in-car developments networkwide. After the proof of concept was finished in 2009, two parallel tracks started in the Amsterdam region: the roadside track (aiming at an innovative, automated working system with ramp metering and traffic lights) and the in-car track (aiming at individualized travel and traffic information in the car for commuter traffic and for large-event traffic). This paper aims specifically at the innovative APT roadside system. Development of this system began in 2012, with the system becoming operational in April 2014. After all technical and functional tests were completed, the system parameters were tuned and finalized the implementation phase. Starting April 14, 11 weeks were taken to collect the data for the ex post assessment. In doing so, the authors have used an alternating assessment design, implemented by switching the system on and off every week so that a fair comparison could be made. From the collected data, it could be concluded that while the system caused additional delays on the urban network and ramps, the throughput on the freeway arterial was improved. Detailed analysis of the data showed the system performed as expected and which improvements can be made to further increase its effectiveness. These optimizations are discussed, as are the expected effects of implementing them in the system. How they are operationalized in Phase 2 of this innovative project will also be discussed.@en