Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

北京市道路智能交通管理系统应用与展望

在分析北京市道路交通管理特点以及总结智能交通管理系统应用情况的基础上,对首都智能交通管理系统的发展进行了展望,提出了智能交通管理系统是解决城市交通问题的有效手段和实现科学发展的必然选择。     

Web环境下公路工程造价审查系统研究

针对目前公路工程造价审查体系存在的问题，在分析群体决策支持系统、多Agent和数据库技术发展趋势的基础上，对Web环境下的公路工程造价审查系统的体系框架进行了优化设计。并提出了造价资料的分步综合审查法。     

飞轮储能系统建模与仿真研究

为研究独立电力系统采用飞轮储能系统对大功率脉冲负载的供电性能,本文在分析了飞轮储能系统工作原理基础上,对飞轮储能系统建模与仿真开展了研究。采用了PSCAD/EMTDC软件搭建按照其实际组成搭建了SPWM控制的变流器及触发控制信号仿真模型,利用具有大转动惯量、小摩擦系数和小阻力转矩的负载模拟飞轮转子,建立了飞轮储能系统的全系统模型。通过对建立的模型进行仿真,给出了飞轮储能系统在储能状态、放能状态及两者之间的转换过渡时刻的转速、转矩、电流及母线直流电压响应曲线,深入分析表明,该仿真结果与理论完全一致,证明了飞轮储能负载的仿真模型是正确有效的。研究的相关结论可用于相关电力系统供电稳定性的仿真设计。     

Multiagent architectures for intelligent traffic management systems

This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.     

三阶非线性系统的两点边值问题

研究一类三阶非线性系统的两点边值问题,利用微分不等式理论和积分算子给出了解的存在性,结果表明所用技巧可以被应用到其它相应的边值问题.     

城市轻轨车辆微机控制系统的方案设计

本文根据城市轻轨车辆的运营特点和技术要求,论述了城市轻轨车辆微机控制系统的方案选择,并就系统的组成和软硬件的设计提出了建议.     

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在目前的出租车调度系统中,调度的出租车可能需要经过城市堵车区域,不能及时为乘客提供服务,本文对城市道路堵车对出租车调度的影响进行研究.根据出租车自动上传的城市道路堵车信息,建立出租车通过堵车区域的损耗方程,结合城市各道路交叉路口之间的距离,采用Dijkstra算法对最佳调度路径进行求解.实验仿真结果给出了发生堵车时,不同单位额外损耗(β)情况下对应的调度路程、路径和规避堵车区域发生的时间.最后与未考虑城市道路堵车的调度系统进行比较.比较结果表明,通过设置合理的β值,系统可根据堵车严重程度,合理规划出租车路径,规避堵车较严重区域.     

‘Tool port’ to ‘landlord port’: a game theory approach to analyse gains from governance model transformation

This study investigates potential port users’ surplus and terminal operators’ profits due to transforming port governance from the ‘tool port’ model to the ‘landlord port’ model. Although the landlord port model is one of the most adopted and successful port governance models, many ports still operate under other models. Chittagong Port, the largest port in Bangladesh, currently operates under the tool port model, while the country’s second-largest port, Mongla Port, operates under the service port model. Chittagong Port is currently facing many challenges, including capacity expansion and renovation of port facilities. Therefore, we form four hypothetical situations for port governance model transformation, assuming that Chittagong Port will transform to the landlord port model but that Mongla Port will run under the existing governance model. Estimating a Bertrand game model, we present a cost-benefit analysis for port users and terminal operators (or port authorities) in hypothetical game situations. The results reveal that privatising one of the container terminals under a landlord setting is the most profitable scenario for Chittagong Port Authority, but privatising all terminals of Chittagong Port yields the highest users’ surplus. However, privatising two of the terminals while they cooperate yields the lowest users’ surplus.     

数据驱动的共享单车停放区规划方法研究

从宏观和微观两个角度研究共享单车停放区规划方法,宏观层面解决停放区选址定容问 题,微观层面进一步优化每个停放区内部的停车位布局问题。宏观层面利用DBSCAN对共享单 车停/取点进行聚类分析得到停/取需求点;然后考虑共享单车使用者路径选择、停/取点选择和停 放区服务能力约束,建立混合整数线性规划模型优化停放区选址及容量。微观层面充分考虑行 人、自行车与汽车之间的影响,其中行人和自行车采用改进社会力模型进行仿真,并考虑共享单 车使用者最优停放区选择;汽车采用智能驾驶模型进行仿真。停车位布局通过仿真通行效率及 混合交通流风险进行评价,并基于代理模型进行优化。该方法应用于北京市崇文门地铁站周边 共享单车停放区规划,验证了方法的有效性,该方法可提高混合交通流通行效率,降低交通风险。