一种基于GPS和RFID的智能公交站台定位方法

目前,将智能网联技术、自动驾驶技术应用到城市公共交通领域已经成为一个重要的研究方向,然而,车辆实时位置监测,作为这两项技术最重要的基础之一,仍旧主要依赖GPS定位系统,该系统受天气及环境影响较大,无法独立提供完善及精确的位置信息。文章提出了一种基于GPS和RFID的智能公交站台定位方法,该方法采用GPS与RFID两者协同、矫正的方式,克服了单一GPS定位的不足,对于提高公交车辆站台定位的精确性、及时性,实现公交车辆站台提前播报及自动停靠,具有重要的研究意义。     

单片机实验系统中GSM、GPRS实验模块的设计与应用

以单片机实验系统中GSM、GPRS模块的应用为例,介绍单片机应用于移动通信教学实验的一个解决方案．     

“当前”模型自适应卡尔曼滤波在有轨电车定位中的应用

有轨电车常与地面其他交通工具混行,因此其准确定位是列车调度指挥和运输安全的重要保障。为节约成本,有轨电车系统一般采用GPS结合速度传感器等方式进行定位,而非大量铺设应答器,因此,GPS信号的处理对于列车定位精度的提高具有重要作用。从简化系统、节省费用、计算难度和精度等方面综合考虑,采用卡尔曼滤波方法对GPS定位信息进行修正。同时,为更好地描述列车运行状态,使其更贴近列车真实运动情况,采用机动加速度"当前"统计模型结合自适应卡尔曼滤波方法对有轨电车位置进行最优估计处理。通过Matlab仿真对在试验场地采集的GPS定位信息进行最优估计处理。仿真结果表明:采用"当前"模型下的自适应卡尔曼滤波算法后,列车定位结果能够更好地跟随其运动的真实轨迹,更接近其真实值,很好地提高了定位精度。     

如何避免GSM-R无线通信系统对机场电磁干扰

以拟建合湛高速铁路经过遂溪机场的线路方案为例,对Longley-Rice不规则地形信道模型进行了简要介绍,仿真分析了该模型在不同传输距离和置信区间下信号的衰落情况。现场实测数据验证了Longley-Rice信道模型的准确性和合理性。结合仿真和实测结果,提出了GSM-R无线覆盖的解决方案,并且为铁路选线方案的优化提供了技术支撑。     

铁路槽罐车智能监测系统的设计

针对铁路槽罐车运输过程中仍需企业员工随车押运,存在无法准确掌握货物状态、出现紧急情况不能发现、随车押运条件艰苦等诸多问题,提出了铁路槽罐车智能监测系统,实现了列车押运的自动化、无人化、远程化,使传统押运模式得到极大改善.     

个人出行轨迹中轨道交通段GPS信号缺失修补算法

基于GPS的个人出行轨迹在城市路网环境中信号丢失较为严重。针对城市轨道交通段的GPS信号缺失问题提出了相应解决办法。研究分析了出行者在换乘节点的一般时空行为以及GPS轨迹的形态特征,给出在特定情况下模型中最优路径的界定,以此制定出交通路段选取规则;在逻辑设计上结合地理信息数据库的语义信息和轨道交通网的拓扑结构,开发了基于广度优先原则的轨道交通段GPS信号的修补算法。此外,研究结合使用Baidu Map API开发了修补效果的在线可视化平台,以上海市内20组GPS个人出行数据进行了实例分析。结果表明,算法的修补效果良好,设计思路合理,且计算和修补时耗线性单增,具有一定的实用价值。     

关于GPS静态测量同步环闭合差限差的探讨

GPS静态测量技术已在交通、水利等领域得到广泛应用,但测量数据的同步环闭合差核验往往很难达到现行测量规范的限差要求。通过系统分析GPS静态测量中同步环闭合差的产生原因,认为现行测量规范中同步环闭合差的限差值可能存在一定问题,进一步尝试推求同步环闭合差的限差参考值,并结合具体的工程实例进行验证和说明。     

Repeatability & reproducibility: Implications of using GPS data for freight activity chains

As transport modellers we are interested in capturing the behaviour of freight vehicles that includes the locations at which vehicles perform their activities, the duration of activities, how often these locations are visited, and the sequence in which they are visited. With disaggregated freight behaviour data being scarce, transport modellers have identified vehicle tracking and fleet management companies as ideal third party sources for GPS travel data. GPS data does not provide us with behavioural information, but allows us to infer and extract behavioural knowledge using a variety of processing techniques. Many researchers remain sceptical as specific human intervention, referred to as ‘expert knowledge’, is often required during the processing phase: each GPS data set has unique characteristics and requires unique processing techniques and validation to extract the necessary behavioural information. Although much of the GPS data processing is automated through algorithms, human scrutiny is required to decide what algorithmic parameters as considered ‘best’, or at least ‘good’. In this paper we investigate the repeatability and reproducibility (R&R) of a method that entails variable human intervention in processing GPS data. More specifically, the judgement made by an observer with domain expertise on what clustering parameters applied to GPS data best identify the facilities where commercial vehicles perform their activities. By studying repeatability we want to answer the question ‘if the same expert analyses the GPS data more than once, how similar are the outcomes?’, and with reproducibility we want to answer the question ‘if different experts analyse the same GPS data, how similar are their outcomes?’ We follow two approaches to quantify the R&R and conclude in both cases that the measurement system is accurate. The use of GPS data and the associated expert judgements can hence be applied with confidence in freight transport models.     

Classifying the purpose of stopped truck events: An application of entropy to GPS data

This paper applies the concept of entropy to mine large volumes of global positioning system (GPS) data in order to determine the purpose of stopped truck events. Typical GPS data does not provide detailed activity information for a given stop or vehicle movement. We categorize stop events into two types: (1) primary stops where goods are transferred and (2) secondary stops where vehicle and driver needs are met, such as rest stations. The proposed entropy technique measures the diversity of truck carriers with trucks that dwell for 15 min or longer at a given location. Larger entropy arises from a greater variety of carriers and an even distribution of stop events among these carriers. An analysis confirms our initial hypothesis that the stop locations used for secondary purposes such as fuel refills and rest breaks tend to have higher entropy, reflecting the diversity of trucks and carriers that use these facilities. Conversely, primary shipping depots and other locations where goods are transferred tend to have lower entropy due to the lower variety of carriers that utilize such locations.     

Speed,travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data

Very little is known about cyclist speeds and delays at the disaggregate level of each road segment and intersection in an entire city network. Speeds and delays serve as vital information for planning, navigation and routing purposes including how they differ for different times of the day and across road and bicycle facility types, after controlling for other factors. In this work, we explore the use of recent GPS cyclist trip data, from the Mon RésoVélo Smartphone application, to identify different performance measures such as travel time, speed and delay at the level of the entire network of roads and intersections on the island of Montreal. Also, a linear regression model is formulated to identify the geometric design and built environment characteristics affecting cyclist speeds on road segments. Among other results, on average, segment speeds are greater along arterials than on local streets and greater along segments with bicycle infrastructure than those without. Incorporating different measures of cyclist personality in the models revealed that the following characteristics all affect cyclist speeds along segments, each cyclist’s average speed on uphill, downhill and level segments as well as geometric design and built environment characteristics. The model results also identify that the factors that increase cyclist speeds along segments include, segments which have cyclists biking for work or school related purposes, segments used during morning peak and segments which do not have signalized intersections at either end.