Description of the Cooperative Traffic TSoS Use Case

The Local4Global methodology was implemented and evaluated to theCooperative Traffic Use Case as one application for the final product. Cooperative systems are considered as state of the art in the field of traffic management as they enable the communication of vehicles with each other or between vehicles and the traffic control infrastructure. Besides requiring specific technical conditions for communicating with cooperative vehicles, the control infrastructure of such a use case must be able to cope with both kinds of constituent systems on a strategic level, i.e. the system is called to optimize both the traffic flow in intersection areas and the movements of cooperative vehicles aiming to reach an optimized traffic flow throughout the network.

munich1A test bed in the north of Munich, Germany was defined in order to introduce the Local4Global Traffic Use Case into practice. The road section of the arterial road B 13 between the highway junction Unterschleissheim in the north and the junction of the federal roads B 13 and B 471 in the south has two lanes per driving direction and consists of seven signalized intersections. The Local4Global Traffic Use Case integrates two types of constituent systems to be optimized on a local level aiming to increase the global performance of traffic flow throughout the test bed. The first type of constituent systems are traffic controllers at seven signalized intersections. The second type of constituent systems are cooperative vehicles approaching the signalized intersections. Their movements can be influenced by providing them with traffic light information. Optimal progression speeds can be calculated on the basis of this information and displayed to drivers by nomadic devices mounted locally on the cooperative vehicles’ dashboards. Local information is collected at each junction (occupancy of detectors and process data of signalization) and in each cooperative vehicle (position and time). The data was collected and aggregated by the Local4Global System, interpreted (e.g. by determining section-related travel times or traffic flows from detector data) and further refined. The data refinement results in global information on the traffic conditions in the test bed, e.g. the average network mean speed and the current signal plan configuration.

munich3The Local4Global algorithm analyses traffic conditions in combination with the corresponding signal plan configurations. The optimization engine determines the most suitable signal plan configuration for the specific traffic condition. A suitable signal plan is selected for each junction controller on the intersection level. Furthermore, the set of signal plans to be activated is sent to cooperative vehicles in the test bed. By matching the traffic light information of the relevant signal group at the corresponding intersection with the current position of the vehicle, the Local4Global System calculates a progression speed recommendation to be displayed on the nomadic devices in the vehicles. The chart below illustrates the information flow in the context of the Local4Global Traffic Use Case.

The optimization of the traffic flow in intersection areas on the one hand (constituent system I) and the movements of cooperative vehicles on the other hand (constituent system II) lead to an optimized traffic flow throughout the network on a global level. High network performance resulted in the minimization of travel times, reduction of stops at signalized intersections and increase of the average network mean speed.


Despite the fact that the problem of real-time signal control at the junctions of urban traffic networks has been studied for many decades, and many different urban traffic control strategies have been developed, tested and are operational in a number of cities around the world, it is today a well-accepted fact that urban traffic control systems are not able to cope effectively with the constantly increasing problem of congestion. According to the American FHWA (Federal Highway Administration) “No current generally available tool is adequate for optimizing [signal] timing in congested conditions” (2008). Moreover, the problem of designing effective urban traffic control strategies will become significantly more complicated with the introduction of cooperative traffic/transport systems, whereby vehicles will be enabled to communicate directly with each other and with the infrastructure. Thus, apart from controlling the traffic signals in the junctions, the control system is also called to optimize the routes and speeds of the cooperative vehicles.

An urban traffic network may seem, at first view, to have a steady and constant structure with respect to its subsystems (i.e. the junctions). However, the functional interdependencies between these subsystems are, in reality, subject to change. When traffic conditions are non-saturated (i.e. when vehicle queues forming during the red phase are cleared during the next green phase), then upstream flows (released from upstream traffic signals) influence the downstream junctions and their traffic signals. On the other hand, when the links start building increasing vehicle queues (e.g. during the daily peak period congestion), then the outflow from upstream junctions is hindered due to existing long queues in the downstream links, hence an existing queue may extend towards upstream links; in other words, congested traffic conditions mark a radical change of the subsystem interactions, which are now directed from downstream to upstream and are strengthened substantially (see the figure below). Thus, the tail of a queue forming at a critical link, propagates upstream, may form multiple congested branches of upstream links, and may even lead to gridlocks and accordingly strongly degraded traffic flow operation.


Apparently, an ideal traffic control system would be one that could control the traffic signals (as well as the cooperative vehicles routes/speeds) on a second-by-second basis by using real-time information stemming from throughout the whole traffic network. However, such an ideal control system is practically impossible to be developed, as the problem becomes extremely complex (NP-complete) [PDDKW]. Additionally, as the traffic network characteristics and dynamics as well as the number and locations of cooperative vehicles are constantly changing, such an ideal control system would have to deal with a problem where the dynamics and structure of the controlled system constantly change. Within Local4Global we take the view that by treating the overall system of junction controllers/cooperative vehicles as a TSoS comprising constituent systems which are based only on local information, learn, evolve and self-organize so as to optimize the global TSoS performance, we were able to provide a control strategy that approaches the performance of the above-mentioned ideal system and outperforms the existing centralized or utterly decentralised local traffic control strategies.

Use Case Site: Cooperative Traffic Network of B13 Munich, Germany

The Bavarian Road Administration has defined a test site to test and evaluate the outcomes of Local4Global, namely the Federal Road B13 in the north of Munich (Germany) as briefly described here. This road section with a length of more than 7 km allows for the test of new technologies in a rural environment with comparably long distances between intersections and high-speed sections. The Bavarian Road Administration has identified this road, because of the high traffic loads and good potential for improvement. To acquire reference data on the traffic flow, the Bavarian Centre for Traffic Management installed wireless traffic sensors which will be available for the project. Additionally, floating car data of equipped vehicles may be used to add further information.

The road stretch is equipped with traffic signal components by Siemens. Traffic light configuration and harmonization of signalling times was achieved in cooperation with the local road maintenance office and the companies that have delivered the hard- and software. Moreover, control of cooperative vehicles was realized by setting up a bi­directional communication between vehicles and a control centre or roadside infrastructure: on-board devices or smartphones will be used to deliver the route and speed recommendations as calculated by the Local4Global system to the drivers. On the other hand, information from the vehicles is used within the control centre or roadside controllers in order to optimize traffic signal control considering the prevailing traffic conditions. The traffic signal systems and the vehicles had to be combined in an integrative communications- and control structure. The network of the traffic signal system is connected wireless (e.g. UMTS) to the control entity, where traffic data and information on signal stages were analysed and processed.

traffic2Constituent Systems:

  • Junction controllers that control the green times of the stages at a second-by-second basis. The sensor information they receive is: (a) the flow (veh/h) and occupancy (%) at the links (road segments) approaching or leaving the junction; and (b) the location and speed of cooperative vehicles that are in the vicinity of the junction. In addition, each junction controller receives appropriate information from all adjacent controllers so as to coordinate the decentralised actions and enable efficient operation at the network level.
  • Cooperative vehicles that decide their routes and speed so as to optimize their travel time. The sensor information they receive is (a) their location and speed and (b) the flow and occupancy of link (road segment) they are currently present.

Specific TSoS Challenges:

  • Use of local information to globally optimize TSoS performance (average traffic network mean speed).
  • Constantly changing interactions (between junctions) and topology (cooperative vehicles are constantly moving) and constantly changing dynamics (traffic demand and traffic patterns of vehicles).
  • Combination of continuous (vehicle flow), discrete (green lights, cooperative vehicles) and highly stochastic (the human factor and the traffic demand/patterns) characteristics. Extremely complex and discontinuous dynamics.
    Emergent behaviour (particularly during congestion).
  • Heterogeneity (two completely different types of technical constituent systems: junction controllers and cooperative vehicles).
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