The environmental and economic importance of traffic control systems combined with the distributed nature of traffic nodes and their constantly changing traffic demands make traffic light control an ideal test case for organic computing approaches.

—Holger Prothman

The authors are members of the Organic Traffic Control Collaborative (OTC²), a joint project between the two universities. A prior project developed an architecture for an adaptive learning node controller. This architecture was extended to enable collaboration among the node controllers—a prerequisite for a network-wide optimization.

Organic Computing is based on the presence of large collections of autonomous systems, which are equipped with sensors and actuators, are aware of their environment, can communicate freely, and can organize themselves in order to perform the actions and services that seem to be required.

The presence of networks of intelligent systems in our environment opens fascinating application areas but, at the same time, bears the problem of their controllability. Hence, we have to construct such systems—which we increasingly depend on—as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. In order to achieve these goals, our technical systems will have to act more independently, flexibly, and autonomously, i.e. they will have to exhibit life-like properties. We call those systems “organic”. Hence, an “Organic Computing System” is a technical system, which adapts dynamically to the current conditions of its environment. It will be self-organizing, self-configuring, self-optimizing, self-healing, self-protecting, self-explaining, and context-aware.

—OTC²

In the case of an urban traffic system, the sensors would be closed-circuit TV cameras mounted on road gantries and other places while the controllers, or actuators, would be traffic lights, which can effectively start and stop the flow of traffic.

Currently, traffic lights either have fixed timer controls or a centralized, control system. The widely used Split, Cycle and Offset Optimization Technique (SCOOT) is popular with those responsible for traffic control. It computes a single cycle time for all intersections, splits this cycle time into green times for each intersection and then adjusts offset times in order to minimize waiting times. SCOOT’s primary aim is keep traffic flowing smoothly and pedestrians safe. Modern traffic-responsive Urban Control (TUC) additionally takes public transport into account.

However, although these systems have been developed over many years, they do have several technical shortcomings and traffic jams do occur more frequently than drivers would like because problems with flow control. Fixed timers are obviously flawed as they do not respond to traffic itself and even centralized systems cannot respond optimally to the changes in traffic movements out on the roads. This leads to jams and waste drivers’ time, vehicle fuel, and to higher levels of localized pollution in towns and cities than might otherwise be present.

Prothmann and his colleagues used the organic computing approach to develop a decentralized traffic control system and compared its impact on traffic flow with a conventional system. The organic approach is based on industry-standard traffic light controllers. These have been adapted to have an observer/controller architecture that allows the traffic light to respond to traffic flow and to pass on information to the other traffic lights on neighboring roads.

Tests at busy junctions in Hamburg demonstrated that the average number of vehicle stops can be cut significantly, delays avoided, and journey times reduced, all of which has benefits for drivers, pedestrians and city dwellers, and, in terms of fuel use and pollution, the environment.

Resources

• Holger Prothmann, Jürgen Branke, Hartmut Schmeck, Sven Tomforde, Fabian Rochner, Jörg Hähner, Christian Müller-Schloer (2009) Organic traffic light control for urban road networks. Int. J. Autonomous and Adaptive Communications Systems, 2, 203-225 doi: 10.1504/IJAACS.2009.026783

• Organic Traffic Control Collaborative

• Organic Traffic Control research overview