城市公交客车发展趋势

本文从未来十年城市社会经济和文化发展的角度分析了公交客车发展的趋势:即动力设计环保化、形体设计大型化、技术配置多样化、性能选择人本化、信息沟通智能化、外观设计艺术化、操作系统自动化、应急设置可靠化、总成寿命同步缩短化、车身结构轻型化。     

中国汽车工业可持续发展的必由之路--新型能源清洁汽车

随着汽车的不断普及,来自能源及环保的压力不断增大。分析了电动汽车及代用燃料汽车的前景,并阐述了我国应当加快清洁能源汽车研制的必要性及在该领域已经取得的成果,最后展望了我国未来新型能源汽车的前景。     

人工智能在车辆自动驾驶中的应用

提出一种新的基于人工智能的感知 计划 动作agent结构实现智能车辆自动驾驶的方案。首先通过描述该结构的原理说明该结构可以解决自动驾驶中存在的一些问题,接着通过建立自动驾驶知识库阐述如何具体实现自动驾驶,最后通过仿真实验验证该方法能够为智能车辆实现自动或辅助驾驶提供一种非常有效的机制。     

公路路面施工沥青拌和站建设方案探讨

通过对公路沥青拌和站的选型、选址、节能、加热方式等一系列问题进行探讨,研究如何在建设合理、高效、一流的拌和站的同时,怎样提高拌和质量,减少能源消耗,降低生产成本,以达到技术先进,经济效益和社会效益最佳配置的理想目标。     

无横撑下承式系杆拱侧倾稳定实用计算方法

以无横撑下承式系杆拱桥侧倾稳定问题为背景 ,用能量变分方法导出了其侧倾稳定的近似解析解 ,并通过具体算例 ,比较了由于吊杆的非保向力作用对其侧倾稳定性的影响 ,并将本文方法与工程设计中常用方法作了比较     

聚丙烯纤维混凝土直接拉伸性能的试验研究

重点研究聚丙烯纤维增强混凝土在单轴直接拉伸荷载下的力学性能和纤维混凝土的单轴拉伸应力变形全曲线。提出单轴拉伸相对应力裂缝宽度曲线的理论方程式。由单轴拉伸全曲线得到了纤维混凝土的应力裂缝宽度曲线、断裂能及特征长度等。试验发现:当纤维体积掺量为0 14%时,纤维混凝土的轴心抗拉强度比基准混凝土提高20%,极限拉伸应变提高49%,断裂能提高68%,临界断裂时的最大裂缝宽度增加55%。聚丙烯纤维具有良好的阻裂性能,增强了硬化混凝土的能量吸收能力。     

电动汽车充电桩安全管理研究

随着国家对环境保护和能源节约的重视以及新能源政策的实施,电动汽车行业迎来了快速发展。充电基础设施的建设,是加快电动汽车推广的重要需要。电动汽车充电基础设施建设和运营业务发展的机会也极大,随之而来的安全管理工作要求也越来越高。本文依据充电桩建设和运营的特点,剖析其中面临的安全风险,并提出加强安全管理工作的措施,为充电桩企业安全管理运营提供参考和借鉴。     

Ontologies for transportation research: A survey

Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.     

Integrated approach to determine highway flooding and critical points of drainage

Among the natural hazards that threaten transportation infrastructure, flooding represents a major hazard to highways as it challenges their design, operation, efficiency and safety. In extreme cases, it may lead to massive obstruction of traffic and direct damages to the road structures themselves and indirect damages to the economic activity and development of the region. To enable the prevention of such consequences, and the proposition of adaptive measures for existing infrastructure, this paper presents an integrated framework to identify the most vulnerable points to flooding along a highway. This is done through the combination of remote sensing information (e.g. LiDAR based Digital Elevation Model, satellite imagery), a high-quality dataset, and a quasi-2D hydrodynamic model. The forcing condition is defined using a hyetograph associated to a storm with duration of 1 day and return period of 100 years. The selected highway is located in the Mexican state of Tabasco, where extreme precipitation events and floods are frequent. Results demonstrate the ability of the methodology to identify critical water levels along the road (h > 1.50 m) at those locations where flooding has been experienced, as well as points of inspection for the highway drainage. These locations were visited in the field and maintenance problems were detected that do increase its level of exposure. We show that this framework is useful for the generation of a flood management strategy to the analyzed highway, which includes an optimum location of adaptive measures to an anticipated more intense future climate.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.