"卫星定位地理信息"服务系统在公路交通现代化中的重要应用

介绍了交通GPS(/GIS)服务系统发展概况,特别剖析了省(局)级公路运输GPS(/GIS)信息服务系统技术方案,展望了交通GPS(/GIS)服务系统的发展.     

多元动态交通流信息融合与交通动态信息实时诱导发布系统

文章结合多元数据融合(MDF)、地理信息系统(GIS)和全球定位系统(GPS)等现代信息技术,设计城市多元动态交通流信息融合与交通动态信息实时诱导发布系统,实现面向政府、企业和出行者的交通决策支持、综合管理、信息服务功能,改进城市交通现状,并为居民提供良好的出行环境。     

基于3S技术的管道巡检系统

针对传统的管道线路巡检方法越来越不能满足现代化的管理要求,根据管道巡检系统的现状和发展趋势,阐述了现有巡检系统的不足之处,分析了影响巡检质量的各种因素,提出了一种方便高效的巡检方式,即集成GPS技术、GIS技术、GPRS技术和嵌入式技术来实现的管道线路巡检管理系统.保证了巡检的到位率,确保巡检质量,严格规范工作流程,实现缺陷信息的实时消缺处理,有效减少故障发生,保障线路安全运行.     

基于OBD、GPS与3G技术的车辆性能远程监控系统

为提高车队管理和车辆维修技术水平,提出并设计一套车辆性能远程监控系统,该系统整合OBD、GPS、3G无线通信模块和GIS平台,可将采集的车辆运行状态信息和定位信息等透过3G网络回传至企业监控中心,便于使用者实时掌握车辆的运行状况,包括车辆OBD故障信息、车速、引擎转速、电瓶电压、冷却水温、行车日期与时间、行车地点与路径信息等。该系统可使车辆保修人员与车队管理人员远程了解车辆的实时状况与所在位置,从而做出相应的维修和管理对策,对保持车辆正常的技术状况、实现节能、环保、安全运营具有较高的应用价值。     

无锡驾培智能物联管理与服务系统

根据无锡市驾培行业发展的需求,无锡市运输管理处深入贯彻绿色低碳的发展理念,综合运用GPS、GIS和无线通信等技术,构建了"驾培智能物联管理与服务系统"。该系统对驾培全过程进行实时有效监控,实现了对教练车位置信息、行驶路径、训练数据等信息的综合管理与统计分析,为驾校经营和行业监管提供了科学的手段,为学员提供了便捷、高效的信息与驾培服务。     

GPS应用综述(一)

1 GPS概述 GPS是全球定位系统(Global Positionsing System)的英文缩写.全球定位系统是美国从20世纪70年代开始研制、耗资约200亿美元、于1994年全面建成的一套能够实时、全天候、全球范围内的,为陆地、海上、空中的各类用户目标提供连续、实时的三维定位、三维速度及精确时问的信息系统.GPS系统具有三大特点:(1)全球、全天候工作;(2)定位精度高;(3)功能多,应用广.     

GPS在高速公路测量中的应用

全球定位系统GPS技术应用于高速公路测量是公路外业勘测的一项重大技术革新,其应用及开发的前景十分宽广。尤其是实时动态(RTK)定位技术在高速公路测量中蕴含着巨大的技术潜力,介绍GPS技术的发展由来和组成,并重点介绍RTK技术在高速公路测量中的特点。     

武汉市建设智能公交系统的建设思路

城市交通问题是世界大城市发展过程中普遍存在的问题。公共交通具有载客量大、运送效率高、能源消耗低、相对污染小和运输成本低、人均占用道路面积少等优点,是解决大城市交通拥挤问题的最佳方式。但是,传统的公交车辆运营管理不仅工作量大、容易出错,而且无法实时监控公交车辆的情况,导致公交车辆运营效率低、服务质量差等问题。解决这一问题的最好办法就是建立智能公交系统。所谓的智能公共交通系统就是在公交网络规划、公交调度等基础理论研究的前提下,通过集成现代通信、信息、电子控制、计算机、网络、GPS、GIS等高新科技,建立智能…     

交通流量监控系统的设计与实现

随着我国城市化进程的加快,城市规模不断扩大,城市人口也越来越多,这给城市交通管理提出了更高的要求。利用交通流量监控系统,可以对交通流量进行监控与管理,从而有效缓解城市交通拥堵问题。交通流量监控系统是基于GPS(全球定位系统)和GIS(地理信息系统)来设计和实现的,通过这些系统的配合,可以将车辆信息无线实时地传递到车辆监控中心,车辆监控中心从而对车辆进行调度。本文探讨了交通流量监控系统的工作原理及系统构成,分析了交通流量监控系统实现其功能的方式,并对交通流量监控系统的软硬件进行了设计。     

高压组合电器(GIS)的智能化研究

利用信息技术、通信技术和控制技术,完成对输变电系统控制的信息化、远程化是智能电网的基本要求,同时也是电网安全的有力保障。文中简述了智能化GIS的定义,从5个方面介绍了GIS在线监测,最后介绍了智能高压开关设备运行维护技术。将在线监测技术运用到智能化GIS中,可以对GIS的多个指标做到实时监控,使智能电网的安全运行获得保障。     

Identifying travel mode with GPS data

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.     

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.     

Sight distance analysis of highways using GIS tools

Analyzing the distance visible to a driver on the highway is important for traffic safety, especially in maneuvers such as emergency stops, when passing another vehicle or when vehicles cross at intersections. This analysis is necessary not only in the design phase of highways, but also when they are in service. For its use in this last phase, a procedure supported by a Geographic Information System (GIS) has been implemented that determines the highway distances visible to the driver. The use of a GIS allows the sight distance analysis to be integrated with other analyses related to traffic safety, such as crash and design consistency analyses. In this way, more complete analyses could be made and costs shared. Additionally, with the procedure proposed it is possible to use data regarding the trajectory of a vehicle obtained on a highway with a Global Positioning System (GPS) device. This application is very useful when highway design data are not available. The procedure developed and its application in a case study are presented in this article.     

Vehicle classification from low-frequency GPS data with recurrent neural networks

The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results.     

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.     

Integrity of map-matching algorithms

Map-matching algorithms are used to integrate positioning data with digital road network data so that vehicles can be placed on a road map. However, due to error associated with both positioning and map data, there can be a high degree of uncertainty associated with the map-matched locations. A quality indicator representing the level of confidence (integrity) in map-matched locations is essential for some Intelligent Transport System applications and could provide a warning to the user and provide a means of fast recovery from a failure. The objective of this paper is to determine an empirical method to derive the integrity of a map-matched location for three previously developed algorithms. This is achieved by formulating a metric based on various error sources associated with the positioning data and the map data. The metric ranges from 0 to 100 where 0 indicates a very high level of uncertainty in the map-matched location and 100 indicates a very low level of uncertainty. The integrity method is then tested for the three map-matching algorithms in the cases when the positioning data is from either a stand-alone global positioning system (GPS) or GPS integrated with deduced reckoning (DR) and for map data from three different scales (1:1250, 1:2500, and 1:50 000). The results suggest that the performance of the integrity method depends on the type of map-matching algorithm and the quality of the digital map data. A valid integrity warning is achieved 98.2% of the time in the case of the fuzzy logic map-matching algorithm with positioning data come from integrated GPS/DR and a digital map data with a scale of 1:2500.     

The return on investment for taxi companies transitioning to electric vehicles

We study whether taxi companies can simultaneously save petroleum and money by transitioning to electric vehicles. We propose a process to compute the return on investment of transitioning a taxi corporation’s fleet to electric vehicles. We use Bayesian data analysis to infer the revenue changes associated with the transition. We do not make any assumptions about the vehicles’ mobility patterns; instead, we use a time-series of GPS coordinates of the company’s existing petroleum-based vehicles to derive our conclusions. As a case study, we apply our process to a major taxi corporation, Yellow Cab San Francisco (YCSF). Using current prices, we find that transitioning their fleet to battery electric vehicles and plug-in hybrid electric vehicles is profitable for the company. Furthermore, given that gasoline prices in San Francisco are only 5.4 % higher than the rest of the United States, but electricity prices are 75 % higher; taxi companies with similar practices and mobility patterns in other cities are likely to profit more than YCSF by transitioning to electric vehicles.     

Predicting market potential and environmental benefits of deploying electric taxis in Nanjing,China

This paper investigates the market potential and environmental benefits of replacing internal combustion engine (ICE) vehicles with battery electric vehicles (BEVs) in the taxi fleet in Nanjing, China. Vehicle trajectory data collected by onboard global positioning system (GPS) units are used to study the travel patterns of taxis. The impacts of charger power, charging infrastructure coverage, and taxi apps on the feasibility of electric taxis are quantified, considering taxi drivers’ recharging behavior and operating activities. It is found that (1) depending on the charger power and coverage, 19% (with AC Level 2 chargers and 20% charger network coverage) to 56% (with DC chargers and 100% charger network coverage) of the ICE vehicles can be replaced by electric taxis without driving pattern changes; (2) by using taxi apps to find nearby passengers and charging stations, drivers could utilize the empty cruising time to charge the battery, which may increase the acceptance of BEVs by up to 82.6% compared to the scenario without taxi apps; and (3) tailpipe emissions in urban areas could be significantly reduced with taxi electrification: a mixed taxi fleet with 46% compressed-natural-gas-powered (CNG) and 54% electricity-powered vehicles can reduce the tailpipe emissions by 48% in comparison with the fleet of 100% CNG taxis.     

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.     

Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System

Due to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the inherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpose numerical method, FOS algorithm has the ability to figure out the system nonlinearity efficiently with a tolerance of arbitrary stochastic system noise. Even using online short-term training data, this method is still able to build an accurate nonlinear model that predicts the system dynamics. Motivated by the above merits, an augmented KF/FOS module is proposed by cascading FOS algorithm to a traditional KF structure. By estimating and reducing both linear and nonlinear RISS errors, the proposed method is supposed to offer substantial enhancement on the positioning accuracy of MEMS-based RISS during GPS outages. In order to examine the effectiveness of the proposed technique, the KF/FOS module is applied on the low cost RISS together with GPS in a land vehicle for several road test trajectories. The performance of the proposed method is compared to KF-only solution, both assessed with respect to a reference offered by a high-end solution. The experimental results confirm that KF/FOS module outperforms KF-only method. The results also show the applicability of the proposed method for real-time vehicle applications.