【正文】 ined by the match between the control schema and the actual traffic patterns. If traffic patterns change, what they usually do, the effectiveness is determined by the way in which the system adapts to these changes. When this ability to adapt bees an integral part of the traffic control unit it can react better to changes in traffic conditions. Adjusting a traffic control unit is a costly and timely affair if it involves human attention. The hypothesis is that it might offer additional benefit using selfevaluating and selfadjusting traffic control systems. There is already a market for an urban traffic control system that is able to react if the environment changes。 the ability of managing, learning, self adjusting and responding to nonrecurrent and unexpected events (Ambrosino et al.., 1994). What are intelligent agents ? 2 Agent technology is a new concept within the artificial intelligence (AI). The agent paradigm in AI is based upon the notion of reactive, autonomous, internallymotivated entities that inhabit dynamic, not necessarily fully predictable environments (Weiss, 1999). Autonomy is the ability to function as an independent unit over an extended period of time, performing a variety of actions necessary to achieve predesignated objectives while responding to stimuli produced by integrally contained sensors (Ziegler, 1990). MultiAgent Systems can be characterised by the interaction of many agents trying to solve a variety of problems in a cooperative fashion. Besides AI, intelligent agents should have some additional attributes to solve problems by itself in realtime。 generalise。t solve. This represents current traffic control implementations and idea39。s on other adjoining signalised intersections)。 decision making (with other agent deciding what to use for next cycle。s have direct munication with neighbouring ITSA39。s. The needed data is collected from different sources, but mainly via detectors. The data is stored locally and may be transmitted to other agents. The actual operation of the traffic signals is left to an ITSAcontroller agent. The central part of the ITSA, acts as a control strategy agent. That agent can operate several control strategies, such as antiblocking and public transport priority strategies. The control strategy agent uses the estimates of the prediction model agent which estimates the states in the near future. The ITSAprediction model agent estimates the states in the near future. The prediction model agent gets its data related to intersection and road segments as an agent that ‘knows’ the forecasting equations, actual traffic conditions and constraints and future traffic situations can be calculated by way of an inference engine and it’s knowledge and data base. Online optimisation only works if there is sufficient quality in traffic predictions, a good choice is made regarding the performance indicators and an effective way is found to handle onetime occurrences (Rogier, 1999). Prediction model We hope to include proactiveness via specific prediction model agents with a task of predicting future traffic conditions. The prediction models are extremely important for the development of pro active traffic control. The proposed ITSAprediction model agent estimates the states of the traffic in the near future via its own prediction model. The prediction metamodel pares the accuracy of the predictions with current traffic and will adjust the prediction parameters if the predictions were insufficient or not accurate. The prediction model agent is fed by several inputs: vehicle detection system, relevant road conditions, control strategies, important data on this intersection and its traffic condition, munication with ITSA’s of nearby intersections and higher level agents. The agent itself has a rulebase, forecasting equations, knows constraints regarding specific intersections and gets insight into current (traffic) conditions. With these data future traffic situations should be calculated by its internal traffic forecasting model. The predicted forecast is valid for a limited time. Research has shown that models using historic, upstream and current link traffic give the best results (Hobeika amp。s and due to the need for coordination and synchronisation. The research towards realising realtime online prediction models needs to be developed in pliance with agent based technology. The proactive and reactive nature of agents and the double loop control schema seems to be a helpful paradigm in intelligent traffic management and control. Further research and simulated tests on a control strategy, based on intelligent autonomous agents, is necessary to provide appropriate evidence on the usability of agentbased control systems. 7