DGPS自主车辆定位及多功能处理软件研制

本文论述了车载GPS自动车辆定位（AVL）的有关研究与试验，着重分析了车辆定位的方法与定位的准确度问题，并利用作者自行开发的处理软件进行处理，证明了基于DGPS的AVL的可行性，取得了满意的结果。     

CNG发动机工作过程的一维数值模拟

内燃机模拟计算是一种现代化的计算方法,可以减少试验次数及时间,从而节约内燃机开发和研究工作的人力、物力和财力.采用AVL BOO ST软件建立了某CNG发动机的整机模型,宏观上描述整个发动机的热力学过程,通过全负荷下的速度特性试验验证该模型的可靠性.并在此基础上,对影响CNG发动机性能指标的主要参数进行对比分析,提出改进措施.     

基于车辆自动定位数据的有轨电车运行效率分析与应用

结合有轨电车线路的现有车辆自动定位数据,分析有轨电车运行效率及其相关影响因素.其中,影响因素从站台、路段、交叉口3个方面进行考虑.定性分析了不同站台型式、站台位置、交叉口类型的属性特征,并量化不同路段的路段长度、所包含的交叉口个数,同时考虑了交叉口控制策略对有轨电车运行时间的影响.以某已运营的有轨电车线路为例,通过建立...     

基于dSPACE和AVL的纯电动汽车电驱动系统效率测试

针对纯电动汽车电驱动系统搭建基于dSPACE和AVL的测试平台,模拟整车模式采用CAN通讯方式传输数据。详细论述系统软硬件架构、平台搭建、效率测试等,并分析电驱动系统高效区和特定工况下的效率。     

Passenger waiting time and information acquisition using automatic vehicle location for verification

Bus riders utilize a variety of information media to learn how to travel to their destinations and to learn when they should arrive at bus stops. As part of the OCTA (Orange County Transit Authority) Transit Probe evaluation, 1199 passengers were surveyed to measure relationships between information acquisition and waiting time. A unique aspect of the survey was that some of the data could be correlated with automatic‐vehicle‐location (AVL) measurements of bus lateness at stops. Insights are provided as to the types of information riders acquire based on the nature of the trip and demographic characteristics. Insights are also provided as to factors affecting perceived waiting time. We found age group, whether a person needs to arrive at a destination by a specific time, primary language, and whether the person is a first‐time user of the bus line to be significant causal factors.     

Using non-real-time Automatic Vehicle Location data to improve bus services

Although real-time Automatic Vehicle Location (AVL) data is being utilised successfully in the UK, little notice has been given to the benefits of historical (non-real-time) AVL data. This paper illustrates how historical AVL data can be used to identify segments of a bus route which would benefit most from bus priority measures and to improve scheduling by highlighting locations at which the greatest deviation from schedule occurs. A new methodology which uses historical AVL data and on-bus passenger counts to calculate the passenger arrival rate at stops along a bus route has been used to estimate annual patronage and the speed of buses as they move between stops. Estimating the patronage at stops using AVL data is more cost-effective than conventional methods (such as surveys at stops which require much more manpower) but retains the benefits of accuracy and stop-specific estimates of annual patronage. The passenger arrival rate can then be used to calculate how long buses spend at stops. If the time buses spend at stops is removed from the total time it takes the bus to traverse a link, the remaining amount of time can be assumed to be the time the bus spends moving and hence the moving speed of the bus can be obtained. It was found that estimation of patronage and the speed of buses as they move between stops using AVL data produced results which were comparable with those obtained by other methods. However the main point to note is that this new method of estimating patronage has the potential to provide a larger and superior data set than is otherwise available, at very low cost.     

Bus running time prediction using a statistical pattern recognition technique

Abstract

Given that real-time bus arrival information is viewed positively by passengers of public transit, it is useful to enhance the methodological basis for improving predictions. Specifically, data captured and communicated by intelligent systems are to be supplemented by reliable predictive travel time. This paper reports a model for real-time prediction of urban bus running time that is based on statistical pattern recognition technique, namely locally weighted scatter smoothing. Given a pattern that characterizes the conditions for which bus running time is being predicted, the trained model automatically searches through the historical patterns which are the most similar to the current pattern and on that basis, the prediction is made. For training and testing of the methodology, data retrieved from the automatic vehicle location and automatic passenger counter systems of OC Transpo (Ottawa, Canada) were used. A comparison with other methodologies shows enhanced predictive capability.     

基于AVL EXCITE TD的凸轮型线仿真优化

在现代发动机开发过程中,CAE软件的应用已经成为降低开发成本和缩短开发周期的主要手段.针对某微小型汽车发动机性能提升的改进工作,利用AVL Excite Timing Drive软件对发动机凸轮轴型线进行优化设计.建立了该发动机的单阀系统模型,在给定的配气机构零件基础上利用多段函数的数学方式优化了凸轮轴的缓冲段和工作段型线.优化后的结果表明,采用梯形函数形式的缓冲段,以及分段函数形式的工作段的凸轮轴型线,既能满足配气机构的动力学和运动学要求,同时达到了提高发动机中低速扭矩的性能目标,有效降低了整机的开发成本和周期.     

基于AVL CRUISE的客车经济性仿真分析及验证

利用AVL CRUISE搭建某款旅游客车符合国标要求的燃油经济性仿真模型,并进行C-WTVC工况仿真分析,将仿真结果与转鼓试验值进行比较,分析仿真结果精度影响因素,并进行修正.     

Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.