连续长大下坡路段交通事故特征分析

在对连续长大下坡路段交通事故数据和道路设计参数调查的基础上, 分析了连续长大下坡路段的交通安全状况, 讨论了急弯陡坡、连续陡坡和连续缓坡3种线形组合形式路段的交通事故特征和事故原因。结合典型长大下坡路段的交通事故数据, 运用数理统计与回归分析方法, 研究了交通事故与道路纵断面参数之间的关系。分析结果表明: 在连续长大下坡路段地点坡度为3%~4%时, 事故率最高; 事故率与事故发生地点前2 km以上路段平均坡度呈显著的指数关系, 所建立回归模型的判定系数高于0.84。     

基于CNS定位误差的侧向碰撞风险模型

为了有效地确定航路安全间隔与评估碰撞风险, 研究了基于通信、导航、监视(CNS) 定位误差的侧向碰撞风险问题。运用多维随机变量协方差矩阵, 给出了CNS性能环境下侧向定位误差分布函数, 建立了给定间隔下基于CNS定位误差的侧向碰撞风险模型, 并对侧向碰撞风险进行了评估计算。计算结果表明: 某航路侧向碰撞风险为4.8×10^-13, 在安全目标水平5.0×10^-9之内, 因此, 该航路在现有的CNS性能环境下满足安全目标水平要求。     

Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.     

Integrated vehicle and powertrain optimization for passenger vehicles with vehicle-infrastructure communication

This research proposes an optimal controller to improve fuel efficiency for a vehicle equipped with automatic transmission traveling on rolling terrain without the presence of a close preceding vehicle. Vehicle acceleration and transmission gear position are optimized simultaneously to achieve a better fuel efficiency. This research leverages the emerging Connected Vehicle technology and utilizes present and future information—such as real-time dynamic speed limit, vehicle speed, location and road topography—as optimization input. The optimal control is obtained using the Relaxed Pontryagin’s Minimum Principle. The benefit of the proposed optimal controller is significant compared to the regular cruise control and other eco-drive systems. It varies with the hill length, grade, and the number of available gear positions. It ranges from an increased fuel saving of 18–28% for vehicles with four-speed transmission and 25–45% for vehicles with six-speed transmission. The computational time for the optimization is 1.0–2.1 s for the four-speed vehicle and 1.8–3.9 s for the six-speed vehicle, given a 50 s optimization time horizon and 0.1 s time step. The proposed controller can potentially be used in real-time.     

Vehicle platoon formation using interpolating control: A laboratory experimental analysis

This work addresses the formation phase of automatic platooning. The objective is to optimally control the throttle of vehicles, with a given arbitrary initial condition, such that desired ground speed and inter-vehicular spacings are reached. The steering of the vehicles is also controlled, because the vehicles should track a desired path while forming the platoon. In order to address the platoon formation problem, a cooperative strategy is formed by constructing a discrete state space model which represents the dynamics of a set of n vehicles. Once this model is set, a control method known as Interpolating Control, which aims at regulating to the origin an uncertain and/or time-varying linear discrete-time system with state and control constraints, is utilized. The performance of this control method is evaluated and compared with other approaches such as Model Predictive Control (MPC).Simulations are conducted which suggest that the Interpolating Control approach can be seen as an alternative to optimization-based control schemes such as Model Predictive Control, especially for problems for which finding the optimal solution requires calculations, where the Interpolating Control approach can provide a straightforward sub-optimal solution.In the experimental part of this work, the control algorithms for the platoon formation and path tracking problems are combined, and tested in a laboratory environment, using three mobile robots equipped with wireless routers. Validation of the proposed models and control algorithms is achieved by successful experiments.     

公路工程费用控制浅谈

对费用控制的三个主要阶段进行分析,对每一阶段的控制方法与控制内容作出说明,对控制要求和控制目标进行探讨。     

自动变速器控制技术研究

自动变速器的控制技术影响到变速器使用性能,主要介绍了电子控制变速器的各种控制功能。     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

Dynamic traffic metering in urban street networks: Formulation and solution algorithm

Traffic metering offers great potential to reduce congestion and enhance network performance in oversaturated urban street networks. This paper presents an optimization program for dynamic traffic metering in urban street networks based on the Cell Transmission Model (CTM). We have formulated the problem as a Mixed-Integer Linear Program (MILP) capable of metering traffic at network gates with given signal timing parameters at signalized intersections. Due to the complexities of the MILP model, we have developed a novel and efficient solution approach that solves the problem by converting the MILP to a linear program and several CTM simulation runs. The solution algorithm is applied to two case studies under different conditions. The proposed solution technique finds solutions that have a maximum gap of 1% of the true optimal solution and guarantee the maximum throughput by keeping some vehicles at network gates and only allowing enough vehicles to enter the network to prevent gridlocks. This is confirmed by comparing the case studies with and without traffic metering. The results in an adapted real-world case study network show that traffic metering can increase network throughput by 4.9–38.9% and enhance network performance.