沥青路面压实性能影响因素分析

将影响沥青路面压实的因素归纳为材料因素、结构和环境因素以及压实机具等,并对这些影响因素进行较为全面的分析,充分认识这些因素的作用机理对于在施工中提高沥青路面的压实质量和长期性能具有重大意义。     

水泥混凝土路面出现蜂窝和麻面的原因及预防措施

混凝土的蜂窝和麻面问题严重危害了混凝土的结构性能和外观。对混凝土的蜂窝和麻面现象的原因进行探究,并提出具体的预防和解决措施,可促进施工质量达到相关规范、设计要求。     

港珠澳大桥建设对珠三角地区物流业的影响

港珠澳大桥将改变珠江口东西两岸之间的空间运输联系方式,从而改变整个珠三角地区的物流格局.从珠三角地区城市空间布局结构、产业特点出发,总结珠三角地区物流业特点,对港珠澳大桥影响珠三角物流格局的途径进行介绍；并逐一分析港珠澳大桥建成后对香港、深圳、珠海等珠三角主要城市物流业的不同影响.     

采空区高速公路路基变形失稳的影响因素

矿体开采后会留下采空区.许多公路、铁路需要建在采空区之上或穿越这些采空区,面临因采空区塌陷所导致的路基失稳与破坏问题.了解采空区影响高速公路路基变形失稳的因素,对于有效避免此类病害发生具有重要指导意义.     

Hardware-in-the-loop testbed for evaluating connected vehicle applications

Connected vehicle environment provides the groundwork of future road transportation. Researches in this area are gaining a lot of attention to improve not only traffic mobility and safety, but also vehicles’ fuel consumption and emissions. Energy optimization methods that combine traffic information are proposed, but actual testing in the field proves to be rather challenging largely due to safety and technical issues. In light of this, a Hardware-in-the-Loop-System (HiLS) testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 ms and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

Field tests of a dynamic green driving strategy based on inter-vehicle communication

This paper presents the design and results for field tests regarding the environmental benefits in stop-and-go traffic of an algorithmic green driving strategy based on inter-vehicle communication (IVC), which was proposed in Yang and Jin (2014). The green driving strategy dynamically calculates advisory speed limits for vehicles equipped with IVC devices so as to smooth their speed profiles and reduce their emissions and fuel consumption. For the field tests, we develop a smartphone-based IVC system, in which vehicles’ speeds and locations are collected by GPS and accelerometer sensors embedded in smartphones, and communications among vehicles are enabled by specially designed smartphone applications, a central server, and 4G cellular networks. Six field tests are carried out on an uninterrupted ring road under slow or fast stop-and-go traffic conditions. We compare the performances of three alternatives: no green driving, heuristic green driving, and the IVC-based algorithmic green driving. Results show that heuristic green driving has better smoothing and environmental effects than no green driving, but the IVC-based algorithmic green driving outperforms both. In the future, we are interested in field tests under more realistic traffic conditions.     

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.     

基于基准有限元模型的箱梁TMD振动控制研究

针对高架桥梁结构引起的振动噪声问题,研究TMD控制箱梁结构振动的特性。为了获得精准的箱梁有限元模型,首先以铁路32 m简支箱梁桥为原型,按10:1的几何相似比设计制作简支箱梁缩尺试验模型,应用ANSYS软件建立初始动力有限元模型;对有限元模型模态分析与试验模型模态测试得到的自由模态信息进行误差分析,并采用基于灵敏度分析的模型修正技术对初始动力有限元模型弹性模量和密度进行修正,得到基准有限元模型,误差确认结果显示修正后的有限元模型更精准地反应箱梁的振动特性;进一步利用基准有限元模型,开展TMD控制简支箱梁桥振动的研究,研究结果表明TMD对于抑制桥梁竖向共振有很好的效果。