Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

Current signal systems for managing road traffic in many urban areas around the world lack a coordinated approach to detecting the spatial and temporal evolution of congestion across control regions within city networks. This severely inhibits these systems’ ability to detect reliably, on a strategic level, the onset of congestion and implement effective preventative action. As traffic is a time-dependent and non-linear system, Chaos Theory is a prime candidate for application to Urban Traffic Control (UTC) to improve congestion and pollution management. Previous applications have been restricted to relatively uncomplicated motorway and inter-urban networks, arguably where the associated problems of congestion and vehicle emissions are less severe, due to a general unavailability of high-resolution temporal and spatial data that preserve the variability in short-term traffic patterns required for Chaos Theory to work to its full potential. This paper argues that this restriction can now be overcome due to the emergence of new sources of high-resolution data and large data storage capabilities. Consequently, this opens up the real possibility for a new generation of UTC systems that are better able to detect the dynamic states of traffic and therefore more effectively prevent the onset of traffic congestion in urban areas worldwide.     

Design and optimisation of wheel–rail profiles for adhesion improvement

This paper describes a study for the optimisation of the wheel profile in the wheel–rail system to increase the overall level of adhesion available at the contact interface, in particular to investigate how the wheel and rail profile combination may be designed to ensure the improved delivery of tractive/braking forces even in poor contact conditions. The research focuses on the geometric combination of both wheel and rail profiles to establish how the contact interface may be optimised to increase the adhesion level, but also to investigate how the change in the property of the contact mechanics at the wheel–rail interface may also lead to changes in the vehicle dynamic behaviour.     

高速公路单点入口匝道RLRM控制方法

为缓解交通堵塞, 基于人工智能的强化学习理论, 提出了不完全信息下的强化学习单点入口匝道控制方法(RLRM)。基于6个仿真实例, 分别计算了平均速度、平均密度、流出交通量与旅行时间, 比较了无控制、定时控制与RLRM控制的控制效果。仿真结果表明: 在交通量较小的实例1中, 以旅行时间为评价指标, 定时控制与RLRM控制的交通阻塞缓解率分别为-6.25%、-9.38%, 几乎没有控制效果; 在交通量变大的实例3中, 以旅行时间为评价指标, 定时控制与RLRM控制的交通阻塞缓解率分别为-8.19%、3.51%, 匝道控制有一定效果, RLRM控制略优于定时控制; 在交通量最大的实例6中, 以平均速度、平均密度、流出交通量与旅行时间为评价指标, 定时控制的交通阻塞缓解率分别为8.20%、0.39%、18.97%与23.99%, RLRM控制的交通阻塞缓解率分别为18.18%、3.42%、30.65%与44.41%, RLRM控制明显优于定时控制。可见, 交通量越大, RLRM控制效果越明显。     

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模糊控制器的应用于1977年首次在文献中提出,其中指出对于有简单绿灯延时控制的单车道交叉口,模糊控制器比车辆感应式控制器更具优势。此后,模糊控制器有了进一步发展,关于交通信号控制的模糊逻辑方法的研究也进一步深入,该方法被陆续应用于无转向车流的双车道交叉口、无限制的单车道交叉口、多交叉口、相位顺序和相位时长控制、拥挤交叉口和路网等等,尤其在高负荷和不均衡交通流条件下表现出优于传统交通信号控制方法的特性。模糊逻辑方法可以改善自适应交通信号控制,改变自适应控制器和TMS的整个决策过程,很大程度上改善未来的交通需求管理方法,然而这类交通信号控制和交通需求管理方法的实际应用并不多见。在诸如沙特阿拉伯这样的发展中国家,学者应根据本国的特有情况研究基于模糊逻辑的交通信号控制与TMS的发展潜力,从而减少拥挤导致的各种损失。