毕业设计外文资料及翻译智能交通信号灯模拟控制系统设计

外文资料

Agent controlled traffic lights

Introduction

The quality of (urban) traffic control systems is determined 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 becomes 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 self-evaluating and self-adjusting traffic control systems. There is already a market for an urban traffic control system that is able to react if the environment changes;the so called adaptive systems. "Real" adaptive systems will need pro-active calculated traffic information and cycle plans- based on these calculated traffic conditions- to be updated frequently.

Our research of the usability of agent technology within traffic control can be split into two parts. First there is a theoretical part integrating agent technology and traffic control. The final stage of this research focuses on practical issues like implementation and performance. Here we present the concepts of agent technology applied to dynamic traffic control. Currently we are designing a layered model of an agent based urban traffic control system. We will elaborate on that in the last chapters.

Adaptive urban traffic control

Adaptive signal control systems must have a capability to optimise the traffic flow by adjusting the traffic signals based on current traffic. All used traffic signal control methods are based on feed-back algorithms using traffic demand data -varying from years to a couple of minutes - in the past. Current adaptive systems often operate on the basis of adaptive green phases and flexible co-ordination in (sub)networks based on measured traffic conditions (e.g., UTOPIA-spot,SCOOT). These methods are still not optimal where traffic demand changes rapidly within a short time interval. The basic premise is that existing signal plan generation tools make rational decisions about signal plans under varying conditions; but almost none of the current available tools behave pro-actively or have meta-rules that may change behaviour of the controller incorporated into the system. The next logical step for traffic control is the inclusion of these meta-rules and pro active and goal-oriented behaviour. The key aspects of improved control, for which contributions from artificial intelligence and artificial intelligent agents can be expected, include the capability of dealing with conflicting objectives; the capability of making pro-active decisions on the basis of temporal analysis; the ability of managing, learning, self adjusting and responding to non-recurrent and unexpected events (Ambrosino et al.., 1994).

What are intelligent agents ?

Agent technology is a new concept within the artificial intelligence (AI). The agent paradigm in AI is based upon the notion of reactive, autonomous, internally-motivated 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 pre-designated objectives while responding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Systems can be characterised by the interaction of many agents trying to solve a variety of problems in a co-operative fashion. Besides AI, intelligent agents should have some additional attributes to solve problems by itself in real-time; understand information; have goals and intentions; draw distinctions between situations; generalise; synthesise new concepts and / or ideas; model the world they operate in and plan and predict consequences of actions and evaluate alternatives. The problem solving component of an intelligent agent can be a rule-based system but can also be a neural network or a fuzzy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local optima in decentralised systems, are not the global optimum. This problem is not easily solved. The solution has to be found by tailoring the interaction mechanism or to have a supervising agent co-ordinating the optimisation process of the other agents. Intelligent agents in UTC,a helpful paradigm

Agent technology is applicable in different fields within UTC. The ones most important mentioning are: information agents, agents for traffic simulation and traffic control. Currently, most applications of intelligent agents are information agents. They collect information via a network. With special designed agents user specific information can be provided. In urban traffic these intelligent agents are useable in delivering information about weather, traffic jams, public transport, route closures, best routes, etc. to the user via a Personal Travel Assistant. Agent technology can also be used for aggregating data for further distribution. Agents and multi agent systems are capable of simulating complex systems for traffic simulation. These systems often use one agent for every traffic participant (in a similar way as object oriented programs often use objects). The application of agents in (Urban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for pro-active traffic light control with on-line optimisation. Signal plans then will be determined based on predicted and measured detector data and will be tuned with adjoining agents. The most promising aspects of agent technology, the flexibility and pro-active behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not that flexible, it is unable to adjust itself if situations change and can't handle un-programmed situations. Agent technology can also be implemented on several different control layers. This gives the advantage of being close to current UTC while leaving considerable freedom at the lower (intersection) level. Designing agent based urban traffic control systems

The ideal system that we strive for is a traffic control system that is based on actuated traffic controllers and is able to pro actively handle traffic situations and

handling the different, sometimes conflicting, aims of traffic controllers. The proposed use of the concept of agents in this research is experimental. Assumptions and considerations on agent based urban traffic control There are three aspects where agent based traffic control and -management can improve current state of the art UTC systems:

- Adaptability. Intelligent agents are able to adapt its behaviour and can learn from earlier situations.

- Communication. Communication makes it possible for agents to co-operate and tune signal plans.

- Pro-active behaviour. Due to the pro active behaviour traffic control systems are able to plan ahead.

To be acceptable as replacement unit for current traffic control units, the system should perform the same or better than current systems. The agent based UTC will require on-line and pro-active reaction on changing traffic patterns. An agent based UTC should be demand responsive as well as adaptive during all stages and times. New methods for traffic control and traffic prediction should be developed as current ones do not suffice and cannot be used in agent technology. The adaptability can also be divided in several different time scales where the system may need to handle in a different way (Rogier, 1999):

- gradual changes due to changing traffic volumes over a longer period of time,

- abrupt changes due to changing traffic volumes over a longer period of time,

- abrupt, temporal, changes due to changing traffic volumes over a short period of time,

- abrupt, temporal, changes due to prioritised traffic over a short period of time One way of handling the balance between performance and complexity is the use of a hierarchical system layout. We propose a hierarchy of agents where every agent is responsible for its own optimal solution, but may not only be influenced by adjoining agents but also via higher level agents. These agents have the task of solving conflicts between lower level agents that they can't solve. This represents current traffic control implementations and idea's. One final aspect to be mentioned is the robustness of agent based systems (if all communication fails the agent runs on, if the agent fails a fixed program can be executed.

To be able to keep our first urban traffic control model as simple as possible we have made the following assumptions: we limit ourselves to inner city traffic control (road segments, intersections, corridors), we handle only controlled intersections with detectors (intensity and speed) at all road segments, we only handle cars and we use simple rule bases for knowledge representation.

Types of agents in urban intersection control

As we divide the system in several, recognisable, parts we define the following 4 types of agents:

- Roads are represented by special road segment agents (RSA),

- Controlled intersections are represented by intersection agents (ITSA),

- For specific, defined, areas there is an area agent (higher level),

- For specific routes there can be route agents, that spans several adjoining road segments (higher level).

We have not chosen for one agent per signal. This may result in a more simple solution but available traffic control programs do not fit in that kind of agent. We deliberately choose a more complex 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 communication takes place between neighbouring agents and upper and lower level ones.

Design of our agent based system

The essence of a, demand responsive and pro-active agent based UTC consists of several ITSA'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 ITSA'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 RSA's. The ITSA is the agent that controls and operates one specific intersection of which it is completely informed. All ITSA's have direct communication with neighbouring ITSA's, RSA's and all its traffic lights. Here we use the agent technology to implement a distributed planning algorithm. The route agents’ tasks are controlling, co-ordinating 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 on-coming 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

suggest the introduction of an extra, slow, loop where rules and parameters of a prediction- model can be changed by a higher order meta-model.

ITSA model

The internals of an ITSA consists of several agents. For a better overview of the internal ITSA model-agents and agent based functions see figure 2. Data collection is partly placed at the RSA's and partly placed in the ITSA'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 ITSA-controller agent. The central part of the ITSA, acts as a control strategy agent. That agent can operate several control strategies, such as anti-blocking 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 ITSA-prediction 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. On-line 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 one-time occurrences (Rogier, 1999).

Prediction model

We hope to include pro-activeness 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 ITSA-prediction model agent estimates the states of the traffic in the near future via its own prediction model. The prediction meta-model compares 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 traffi c condition, communication with ITSA’s of nearby intersections and higher level agents. The agent itself has a rule-base, 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, up-stream and current link traffic give the best results (Hobeika & Kim, 1994).

Control strategy model

The prediction of the prediction model is used in the control strategy planning phase. We have also included a performance indicating agent, necessary to update the control parameters in the slower loop. The control strategy agent uses the estimates of the prediction model agent to calculate the most optimal control strategy to pro-act on

the forecasts of the prediction model agent, checks with other adjoining agents its proposed traffic control schema and then plans the signal control strategy The communication schema is based on direct agent to agent communication via a network link. The needed negotiation finds place via a direct link and should take the global perspective into consideration. Specific negotiation rules still have to be developed. Some traffic regulation rules and data has to be fed into the system initially. Data on average flow on the links is gained by the system during run-time. In the near future computer based programs will be able to do, parts of, these kind of calculus automatically. For real-time control the same basic computer programs, with some artificial knowledge, will be used. Detectors are needed to give information about queues and number of vehicles. The arrival times can also be given by the RSA so that green on demand is automatically covered.

Conclusions and future work

Adaptive signal control systems that are able to optimise and adjust the signal settings are able to improve the vehicular throughput and minimise delay through appropriate response to changes in the measured demand patterns. With the introduction of two un-coupled feed back loops, whether agent technology is used or not, a pro-active theory of traffic control can be met. There are several aspects still unresearched. The first thing we are going to do is to build a prototype system of a single intersection to see if the given claims of adaptability and pro activeness can be realised. A working prototype of such system should give appropriate evidence on the usability of agent based control systems. There are three other major subjects to be researched in depth; namely self adjustable control schema's, on-line optimisation of complex systems and getting good prediction models. For urban traffic control we need to develop self adjustable control schemes that can deal with dynamic and actuated data. For the optimisation we need mathematical programming methodologies capable of real-time on-line operation. In arterial and agent based systems this subject becomes complex due to several different, continuously changing, weights and different goals of the different ITSA's and due to the need for co-ordination and synchronisation. The research towards realising real-time on-line prediction models needs to be developed in compliance with agent based technology. The pro-active and re-active 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 agent-based control systems.

外文资料翻译

代理控制交通灯

作者：Danko A. Roozemond，Jan L.H. Rogier

单位：Delft University of Technology

前言

（城市）交通控制系统的好坏决定于系统控制模式和实际交通流量模式是否相符。如果交通方式改变，他们通常所采取的有效措施是使系统适应这些改变。当这种改变能力成为交通控制系统单元的一部分，它将能更好地改变目前的交通状况。如果考虑人工调整，那么调整交通控制单元是一件花费昂贵和耗时大的事情。假如使用一种能够自我分析和自适应调整的交通控制系统，它将会带来额外的收益。对于这种可以适应环境改变的城市交通控制系统已经有销售。这种系统叫做自适应系统。“实时”自适应系统将需要主动计算交通信息和循环地计划，这种计划是根据不断计算更新的交通信息而采取的。

我们研究代理可用性技术在交通控制时可以分成两部分。首先，理论部分结合代理技术和交通控制。这项研究的最后阶段关注的是实际问题，如实施和收到的成效。在这里，我们目前的代理技术应用于动态交通控制概念。目前，我们正在设计一个代理分层模型为基础的城市交通控制系统。我们将在最后一章详细阐述。

自适应城市交通控制

自适应信号控制系统必须有能力根据目前的交通状况，通过调整优化交通流量。所有使用的交通信号控制方法是基于反馈算法，而反馈算法的数据来源于过去的几分钟到几年时间里的交通需求。当前的交通控制系统运作网络是根据测到的交通状况来协调适应性和灵活性。这些还不是根据交通需求在短时间内改变的最佳方法。其基本前提是，现有的信号计划工具能够根据不同的情况作出合适的信号计划。但是，目前可用的工具都是积极主动型，或者元规则型。这就使得改变控制器的方式放在系统中进行。交通控制的下一个逻辑步骤是将元规则、主动的和目标导向行为都列入。为此，从人工智能和人工智能代理的贡献可以预测，加强管制的主要方面包括：与冲突的目标处理能力；基于时间分析的决策能力；管理、学习、自我调整和应对非经常性和突发事件的能力（Ambrosino等..，1994）。

什么是智能代理？

代理技术是人工智能的一个新概念。人工智能代理模式是基于无功，自主，内部机动的实体，动态的居住概念，并不一定是完全可预见的环境（Weiss，1999年）。自主是指在一段长时间内，能够作为一个独立单元，执行必要的行动以实现多种预先指定的目标，同时对同一体载传感器产生的刺激产生反应（Ziegler, 1990）。综合代理系统的特点是可以通过很多的代理系统互相合作去解决各种各样的问题。此外，人工智能、智能代理应该有一些额外的属性使得它自己可以解决一些实时问题、理解信息、有目标和方向、对情况进行分清区别、揉合新概念或想法。其解决问题的智能代理组件可以是一个以规则为基础的系统，但也可以是一个神经网络或模糊专家系统。显然，对代理技术而言，找到一个可行的解决方案是必要的。通常在分散系统中最优的是局部而不是全部。这个问题不容易解决。解决的方案可以参照裁缝业的相互作用机制，或者有监督代理人协调其他代理人进行优化的过程。

智能代理在UTC的使用是一种有益的范例

代理技术适合不同领域的UTC。那些值得最重要一提的是：信息代理、代理交通仿真和交通控制。目前，大多数运用智能代理来进行信息代理。它们通过网络收集信息。对于采用特殊设计的代理商用户可以提供具体的资料。城市交通智能代理可以为用户提供有关的天气信息，交通拥堵，公共交通，道路封锁，最佳路线等，成为个人的旅行助理。代理技术也可以用于数据的分配。代理和多代理系统，有模拟仿真复杂交通系统的能力。这些系统通常为每一个交通参与者使用一个代理（类似于面向对象程序使用对象）。我们感兴趣的是代理技术在（城市）交通控制中的应用。为此，我们最终使用实时代理技术进行有效的交通灯控制。信号计划将根据预测和探测器实测数据，以及相邻的代理控制的基础上而进行调整。代理技术最具有优势的是它的灵活性和积极性，可以给UTC带来更好的交通预测。当前的UTC不具有灵活性的，它无法自行调节，如果情况变化，不能处理非程序化的情况。代理技术可以在不同的控制层进行控制。这对于接近目前还处于低水平的UTC具有一定的优势。

设计基于城市交通控制系统的代理控制技术

我们争取的理想系统是一个基于启动的交通控制，并能亲积极处理交通情况和处理不同的，以及有时相互矛盾的交通控制系统。本研究提出代理的概念是一

种实验性。

基于城市交通控制的代理假设和考虑

基于城市交通控制和管理的代理，可以从三方面改善UTC系统的现状。●适应性。智能代理能够适应它的行为，可以学习从早期的情况。

●通信。通信是的代理之间进行合作，从而对计划信号进行调整。

●积极主动。由于积极主动，交通控制控制系统可以提前计划。

要对目前的交通控制单元进行替换，可以接受的是该系统应执行得和现在系统一样或者比它更好。基于UTC的代理将要求改变交通模式的在线反应和积极反应。基于UTC的代理应根据各个阶段的要求做出适应性的回应。当前的交通控制和预测方法是不够的，不能使用在代理技术上，应该发展新的交通控制和交通预测方法。适应性也可以分为几个不同的时间尺度，系统可能需要以不同的方式处理（Rogier，1999年）：

●由于渐进式的改革改变了一个较长时间的交通量；

●由于突然的变化改变了一个较长时间的交通量；

●突然，由于改用短的时间内的交通量，时空改变；

●突然，时空改变，由于交通优先较短时间。

一种处理方法之间的性能和复杂性是一个等级制度布局使用的平衡。我们提出了一个在每一个层次的代理对自己负责的最佳解决方案，可能不仅影响邻近的代理，而且也可能影响更高一级代理。这些代理的下级代理之间解决他们不能解决的冲突任务。最后要提的一个方面是稳健的代理系统（如果所有运行的代理通讯失败，如果没有一个固定的代理程序被执行）。

为了能够保持我们的第一个城市交通控制模型尽可能简单，我们作了以下的假设：我们限制自己的市内交通控制（路段，路口，走廊），我们只处理控制与探测器（强度和速度）在所有路段的十字路口，只处理车和我们使用简单的规则为基础的知识表示。

代理类型城市交叉路口控制

正如我们在划分识别几个零部件一样，我们定义以下4剂的类型系统：

●道路是代表特殊路段的代理商（RSA）；

●控制路口是十字路口代理人的代表(ITSA)；

●对于具体的、明确的领域，有一个地区代理（上级）

●对于跨越几个相邻路段的具体线路可以由路由代理（上级）

我们没有选择每一个代理信号。这可能导致一个更简单的解决方法，但现在的交通管制方案不适合在该代理类。我们特意选择更复杂的代理，可以使用标准的交通控制设计算法和方案。这个想法仍然是在地方一级的优化（十字路口），但与当地和整体控制。为此，我们使用面积代理的方式。所有相邻地方之间的通讯需要代理和上，下一级的地方。

我们设计的代理系统

一个基于UTC反应灵敏的代理系统的由智能路口，一些权威的代理（区域路线代理）和一些可选路线代理（RSA）组成。ITSA根据其目标，能力，知识，观念和数据决定如何去控制它的交叉路口。必要时，可以要求代理提供更多信息，或者接收其它目标或代理的信息。

ITSA明确规定，下列功能被列入城市交通控制范围(Roozemond, 1998):

●数据收集/分配（通过RSA的当前交通状态信息；从/到其他ITSA的- 其他

毗邻灯号控制路口）；

●分析（目前的趋势是要求具有精确的模型和了解周围的交通以及交通管制规

则定义；检测当前的交通状况）；

●预测（预测下一次最优的正确的数学周期）；

●决定（与其它代理决定如何使用下一个周期；处理当前的交通问题）；

●控制（根据信号计划周期操作）。

在图一是一个简化的，以代理为基础，UTC系统更加具体的实例。在这里，我们有几个控制路口的路线代理，在RSA的帮助下进行控制。ITSA对于代理和操作一个具体的控制路口是完全熟悉的，所有的ITSA和相邻的ITSA 通讯，RSA的和所有的交通灯。在这里，我们使用代理技术来实现分布式的规划算法。这条路线的代理的任务是控制、协调和引领ITSA迈向全球最优化的目标。ITSA利用所有可利用的信息预测下一个最优化的控制策略操作相应的交通控制信号。ITSA可以直接影响到它们交点的控制策略，并且能够洞察未来的交通状况。

ITSA模型内部结构

通常，引进快速环控制十字路口的交通。探测器的数据反馈到控制算法。基于预定的控制策略去选择相应的信号操作规则。在本研究中，我们要求介绍设立一个额外的，缓慢的循环规则和一个预测参数可以由一个高阶元改变的模型。ITSA模型

一个ITSA的内部由几个代理组成。对于ITSA的内部代理模型和代理的职能详见图二。采集的数据一部分放在RSA，一部分放在ITSA。需要收集的数据来源于不同的地方，但主要是通过探测器。数据存储在本地，并可以传输给其它的代理。交通信号的实际运作是由ITSA控制器代理。ITSA的中央部分是控制策略的代理。该代理可以操作一些控制策略，如抗粘连和公共交通优先。估计在不久的将来，控制策略的代理使用预测模型代理。ITSA预测代理模型预测将来不久的状态。该预测模型代理获得的数据作为代理的'知道'的预测方程，可以根据它的知识和数据库的相关数据推理出实际交和制约因素通条件，以及路口路段未来的交通状况。如果有预测，在线优化只能在交通，对于预测指标的评价，一个不错的选择是找到处理一次性事情的有效方法(Rogier，1999)。

预测模型

我们希望通过包括具体的预测模型代理去预测未来的交通状况。该预测对于主动交通控制的发展是非常重要的。ITSA-预测模型建议通过自己的预测模型去预测未来的交通状况。元预测模型比较了当前的交通预测精度，如果有不准确或不足的将会做出调整。该预测模型存储了几个输入：车辆检测系统、有关道路交通情况、控制策略、十字路口的重要数据和它的交通状况、和ITSA附近的交通路口以及更高级的代理通讯。代理有自己的基础规则，预测方程，知道规定有关十字路口的具体参数和获取目前（交通）状况。有了这些数据，内部交通预测模型可以预测其未来的交通情况。有效的预测预报是在有限的时间里。研究表明，模型的使用时间，上游和当前的链路流量相结合会得到最佳的预测结果(Hobeika & Kim, 1994)。

控制策略模型

该预测模型的预测是用于控制战略规划阶段。我们还包括了预测代理的说明，必要的更新速度去控制较慢的回路参数。该控制策略代理使用代理的预测模

型的估计数来计算最优化控制策略，有利于对预测模型的代理，其建议与其他交通管制架构毗邻检查行为的预测，然后计划信号控制策略的通信模式的基础是直接代理，代理通信是通过一个网络连接。考虑应从全球的角度来看，通过谈判找到所需要的直接的联系地方。具体的谈判规则还有待开发。一些交通管理规则和数据必须最先反馈到系统。系统在运行时可以得到流量的平均数据。在不久的将来，以电脑为基础的方案将能够做到部分自动预测。实时控制的计算机程序基本相同，一些人为的知识将被使用。探测器需要提供有关队列和车辆数目的资料。到达的时间可以由RSA提供，是的绿色自动覆盖。

结论和未来的工作

自适应信号控制系统，能够优化和调整信号设置,可以提高吞吐量，减少车辆, 通过适当的反应测得的需求模式的变化延迟。随着两个非耦合引进回路圈，无论是代理技术使用与否，一个制积极主动的交通控制理论可以得到满足。有几个方面仍需要查证。我们首先要做的是建立一个单一路口的原型系统，看看是否适应给定的要求和是否有利于灵活性的实现。这样一个系统的工作原型应该提供适当的数据证明基于控制系统代理的可用性。另外，有三个方面还可以深入研究：自我控制模式调制；在线优化复杂系统并取得了良好的预测模型。对于城市交通控制，我们需要发展自我调节的控制方案，可以处理和驱动动态数据。对于优化数学规划方法，我们需要能够实时在网上操作。由于几个不同的，不断变化的,重量和不同目标的不同的ITSA,，使得在动脉和代理系统这一问题变得复杂，需要适当的合作，协调。迈向实现实时在线预测模型的研究，需要遵守基于技术的代理。积极主动的代理人，重新和双回路控制模式，似乎是在智能交通管理和控制的典范。进一步的研究和模拟实验，研究控制策略，基于智能自主代理，有必要提供以代理为基础的控制系统的可用性的适当证明。