毕业设计外文资料及翻译---代理控制交通灯-交通线路(编辑修改稿)

毕业设计外文资料及翻译---代理控制交通灯-交通线路(编辑修改稿)内容摘要：

e traffic control programs do not fit in that kind of agent. We deliberately choose a more plex agent to be able to use standard traffic control design algorithms and programs. The idea still is the optimisation on a local level (intersection), but with local and global control. Therefor we use area agents and route agents. All munication takes place between neighbouring agents and upper and lower level ones. Design of our agent based system The essence of a, demand responsive and proactive agent based UTC consists of several ITSA39。 s (InTerSection Agent).,some authority agents (area and route agents) and optional Road Segment Agents (RSA). The ITSA makes decisions on how to control its intersection based on its goals, capability, knowledge, perception and data. When necessary an agent can request for additional information or receive other goals or orders from its authority agent(s). For a specific ITSA, implemented to serve as an urban traffic control agent, the following actions are incorporated (Roozemond, 1998): data collection / distribution (via RSA information on the current state of traffic。 from / to other ITSA39。 s on other adjoining signalised intersections)。 analysis (with an accurate model of the surrounds and knowing the traffic and traffic control rules define current trend。 detect current traffic problems)。 calculation (calculate the next, optimal, cycle mathematically correct)。 decision making (with other agent deciding what to use for next cycle。 handle current traffic problems)。 control (operate the signals according to cycle plan). In figure 1 a more specific example of a simplified, agent based, UTC system is given. Here we have a route agent controlling several intersection agents, which in turn manage their intersection controls helped by RSA39。 s. The ITSA is the agent that controls and operates one specific intersection of which it is pletely informed. All ITSA39。 s have direct munication with neighbouring ITSA39。 s, RSA39。 s and all its traffic lights. Here we use the agent technology to implement a distributed planning algorithm. The route agents’ tasks are controlling, coordinating and leading the ITSA’s towards a more global optimum. Using all available information the ITSA (re)calculates the next, most optimal, states and control strategy and operates the traffic signals accordingly. The ITSA can directly influence the control strategy of their intersection(s) and is able to get insight into oning traffic The internals of the ITSA model Traffic dependent intersection control normally works in a fast loop. The detector data is fed into the control algorithm. Based upon predetermined rules a control strategy is chosen and the signals are operated accordingly. In this research we 5 suggest the introduction of an extra, slow, loop where rules and parameters of a prediction model can be changed by a higher order metamodel. ITSA model The internals of an ITSA consists of several agents. For a better overview of the internal ITSA modelagents and agent based functions see figure 2. Data collection is partly placed at the RSA39。 s and partly placed in the 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。