GPS L1信号C/A码的高效并行软件相关器研究

为了能快速、准确地跟踪所捕获到的卫星,在分析传统GPS软件接收机跟踪算法的基础上,采用了一种用于处理CDMA扩频信号的软件相关器高效并行算法,该算法主要针对耗时较多相关器进行优化。实际运行结果和性能分析表明了文章提出的优化算法优于直接浮点计算,将软件相关器的处理速度提高了约3倍。     

集装箱车辆跟踪和管理

叙述对集装箱车辆跟踪和管理的重要性,介绍实现集装箱车辆跟踪和管理的方法,以及采用3G（GPS、GIS、GSM）高新技术开发而成的集装箱车辆跟踪和管理系统,最后展示应用实例。     

打车软件对出租汽车运营的影响——以深圳市为例

为验证"互联网+出租汽车"模式的有效性和网约车时代出租汽车运营规制的重要性,定量研究基于移动互联网技术的打车软件对出租汽车运营的影响。以深圳市为研究区域,选取打车软件广泛使用前后两个节点日的出租汽车GPS数据,对比两日出租汽车微观运营指标。各指标分析结果表明,打车软件改善了低人口密度和偏远地区的打车难状况,减少了出租汽车的无效运营,并且总体提升了驾驶员的接单效率。因此,鼓励出租汽车行业和互联网融合发展是科学合理的。同时,采用适当的策略规范网约车时代出租汽车的经营服务行为,减少打车软件的负面效应,是促进出租汽车行业健康发展的必要途径。     

浅谈GPS技术与高等级公路测量

全球定位系统为公路勘测设计工作者提供了一个可靠的提高外业测量精度及进度的工具,随着GPS越来越多地出现在人们的生产、生活中,这一先进的测量手段会被更广泛地应用到公路勘测工作中,从而可提高作业效率,缩短设计周期,推动交通事业的发展和公路的建设.     

基于掌上电脑的道路信息采集系统的设计与实现

道路信息采集包括道路位置信息采集及其相应的属性信息采集.掌上电脑的小巧灵活、GPS定位的优质高效和GPRS无线网络的实时便捷为野外数据采集带来了新的模式,可应用于道路施工测量、地图道路更新等多种场合.     

Mining factors affecting taxi drivers’ incomes using GPS trajectories

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Mining underlying factors affecting the income level will not only benefit the newcomers and low-income drivers but also assist in developing effective optimization algorithms for taxi operations. This paper intends to disclose the factors affecting incomes along with their quantitative influence by mining over 167 million GPS records from nearly 8000 taxis in Shanghai. We first identify a marked difference in drivers’ incomes and categorize drivers into three income levels accordingly. We next investigate the overall search-delivery process, thereby defining several factors that may affect the income level. We then develop a generalized multi-level ordered logit (GMOL) model to find the significant factors that influence incomes. Finally, we compute the elasticity for those significant factors and present their contributions, as well as challenge some preconceived ideas regarding how to earn high incomes.     

Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet

Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that (1) collective vehicle parking “hotspots” are good indicators for charging demand; (2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; (3) with current grid mix, emissions of CO₂, PM, SO₂, and NO_x will increase with taxi electrification; and (4) power demand for public taxi charging has peak load around noon, overlapping with Beijing’s summer peak power.     

Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

A driving cycle detection approach using map service API

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method’s ability to detect vehicle mode accurately.