Sustainable Traffic Management
Organization responsible: Technolution
- Paul van Koningsbruggen (Technolution)
- Gijs Withagen (Technolution)
Technolution BV is developing a framework for road network wide, pro-active traffic management. This framework should support the decision making process on traffic management on combined interurban and urban networks. The framework should bridge the gap between the management strategy, defined by political goals, and low level optimization of the traffic flow. It should effectuate measures or provide advice on measures to be effectuated, to change the traffic flow towards the desired strategic goals. The effectuation of measures can be done automatically by the framework, but human operators can always intervene.
Traffic jams are a daily recurring problem on the road networks. Optimizations of traffic lights, ramp metering and other measures are not sufficient to prevent traffic jams. These traffic jams are caused by a structural saturation of the network and by incidents. More and more often we have to accept that there are problems in the network that cannot be solved. All that can be done with traffic management is to inform, warn and advice road users so that they make a safe and optimal usage of the whole road network, prevent blocking back effects as much as possible and to keep up quality of traffic flow as long as possible on those routes that refer most to the policy objectives. In order to come to a graceful degradation of the quality of traffic in the end traffic jams can be located on road segments where they hurt least.
The framework envisioned by Technolution is an instrument that observes traffic and identifies problems in the network in relation to the policy goals and visualizes these problems. The framework is based on multi-agent principles, using observation and service agents. An observation agent compares the quality of the traffic flow on road segments with the intended quality derived from the policy goals that are set for these segments. Service agents basically listen to the observation agents and decide upon the activation of traffic management measures. The service agents know about the capabilities of measures and if there are measures that can help an observation agent they will (advice to) effectuate that measure.
When there is more than one problem in the road network the service agents will weight the problems using the priorities as allocated to the trajectories and road segments in the road network and favour the most important trajectories and segments. In their collaboration the network of agents generate so-called traffic management scenario’s capturing the intended status of the traffic management measurements for the coming period. Human traffic operators are part of the decision making process and can change parameters of agents, can adjust the generated traffic management scenarios or even can overrule them completely.
There is now an object model for the representation of the traffic flow, network state and state of the traffic management measures along the road. This object model is fed with data coming from a traffic simulator, which is so far our representation of the real world. The object model itself will feed an autonomous controller (composed from observation and service agents) that will generate traffic management scenario’s given the traffic situation and observed bottlenecks. On top there will be a user interface to get an enhanced overview over the traffic situation and bottle necks as captured in the object model and to review, adjust, approve and activate the traffic management scenarios as generated by the autonomous controller.