铁路车务系统既有线施工安全风险管理探讨

针对铁路既有线改造施工、日常养护对运输效率和运输能力的影响,分析既有线施工对车务系统安全风险管理的要求,探讨施工现场车务系统的安全卡控措施,即强化车务系统管理人员的安全责任制和施工安全风险关键点的卡控措施,探索既有线施工安全风险管理的新方法、新途径,逐步形成安全风险管理的长效机制。     

混合动力--未来城市公交的明星动力

本文阐述了目前国内外混合动力总成常用的几种方案,介绍了一种适合康明斯发动机的混合动力方案,展望了该动力总成的开发前景。     

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。     

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。     

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.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

基于分级节点的高速铁路货运动车组开行研究

“八纵八横”高速铁路网货运节点可以利用聚类分析法,依据货运总量、GDP、人口、社会消费品零售总额、快递业务量、线路连接数、城市类别和节点类别等指标划分为4个等级。基于灰色系统预测模型,预测2025年高速铁路货运量,构建数学模型计算保本货运量。依据节点划分、货运量预测、保本货运量结果,计算各等级节点中盈利OD所占的比例,结合日均OD量,分析各等级节点间开办高速铁路货运的可行性。研究表明,在一级节点之间可以开行高速铁路货运动车组专列,在二级节点之间可以开行客货联挂动车组,三、四级节点可以采用捎带运输的方式。