Evaluation of Vehicle Positioning Accuracy Using GPS-enabled Smartphones in Traffic Data Capturing PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Evaluation of Vehicle Positioning Accuracy Using GPS-enabled Smartphones in Traffic Data Capturing PDF full book. Access full book title Evaluation of Vehicle Positioning Accuracy Using GPS-enabled Smartphones in Traffic Data Capturing by Na Yin. Download full books in PDF and EPUB format.
Author: Na Yin
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 116
Get Book Here
Book Description
Connected Vehicle (CV) technology aims to improve transportation management and system performance by incorporating advanced detection and communication system such as Global Positioning System (GPS), and smart devices to make roads and vehicles better equipped to exchange important information regarding road and travel conditions. GPS have emerged as the leading technology to provide location information to various location based services. With an increasing smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic management and data monitoring is promising. This study presents a field experiment conducted along Whitemud Drive (a section of Connected Vehicle Test Bed in Edmonton, Alberta, Canada), Queen Elizabeth Highway, and various urban arterial roadways using a GPS-enabled smartphone, cellular positioning technique, professional GPS handset and combination of smartphone and Geofence. The relative positioning errors and the data collection performances using the aforementioned technologies were evaluated and compared. The characteristics and the relationships between the positioning errors and traffic related factors are investigated using regression analysis. The results indicate that GPS-enabled smartphones are capable of correctly positioning 92% of the roadway segments to Google Earth, while achieving accuracy of less than 10 meters for 95% of the data. Using a cellular positioning technique, cell-IDs were correctly identified in repeatable trials with accuracy levels much lower than the smartphone-GPS positioning. Using combination of smartphone positioning and Geofence are promising in finding accurate positions and timestamps. In all scenarios, the use of four data source for obtaining location and traffic condition is feasible; and particularly, using GPS-enabled smartphones and/or its combination with Geofences can provide good accuracy level for location and traffic state parameter estimates.
Author: Na Yin
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 116
Get Book Here
Book Description
Connected Vehicle (CV) technology aims to improve transportation management and system performance by incorporating advanced detection and communication system such as Global Positioning System (GPS), and smart devices to make roads and vehicles better equipped to exchange important information regarding road and travel conditions. GPS have emerged as the leading technology to provide location information to various location based services. With an increasing smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic management and data monitoring is promising. This study presents a field experiment conducted along Whitemud Drive (a section of Connected Vehicle Test Bed in Edmonton, Alberta, Canada), Queen Elizabeth Highway, and various urban arterial roadways using a GPS-enabled smartphone, cellular positioning technique, professional GPS handset and combination of smartphone and Geofence. The relative positioning errors and the data collection performances using the aforementioned technologies were evaluated and compared. The characteristics and the relationships between the positioning errors and traffic related factors are investigated using regression analysis. The results indicate that GPS-enabled smartphones are capable of correctly positioning 92% of the roadway segments to Google Earth, while achieving accuracy of less than 10 meters for 95% of the data. Using a cellular positioning technique, cell-IDs were correctly identified in repeatable trials with accuracy levels much lower than the smartphone-GPS positioning. Using combination of smartphone positioning and Geofence are promising in finding accurate positions and timestamps. In all scenarios, the use of four data source for obtaining location and traffic condition is feasible; and particularly, using GPS-enabled smartphones and/or its combination with Geofences can provide good accuracy level for location and traffic state parameter estimates.
Author: Juan Carlos Herrera
Publisher:
ISBN:
Category :
Languages : en
Pages : 296
Get Book Here
Book Description
Author: Juan Carlos Herrera
Publisher:
ISBN:
Category : Cell phones
Languages : en
Pages : 26
Get Book Here
Book Description
Author: Youngbin Yim
Publisher:
ISBN:
Category : Cell phones
Languages : en
Pages : 69
Get Book Here
Book Description
Author: Cem Kaptan
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
The widespread availability of sensors, improved computing, and storage capabilities, and ubiquity of networking services have led to the transformation of the conventional transportation services. Achieving the smart transportation goal can be either via dedicated or non-dedicated methods. The former denotes utilization of sensors that are explicitly deployed and configured for pre-defined sensing tasks whereas the latter exploits opportunistic and participatory sensing paradigms. With that motivation in mind, the contributions of this thesis are three-fold: \textit{(i)} Sensor emulation, \textit{(ii)} Crowd-sensing based GPS-less tracking, and \textit{(iii)} Reliable data acquisition in crowd-sensed GPS-less tracking. Emulating non-dedicated sensors in a simulation environment enables us to perform large-scale crowd-sensing tasks. We introduce a variety of vehicular crowd-sensing-based frameworks to track public transportation vehicles that move over static routes in a smart city setting without \textit{GPS}-enabled devices because of the major downsides of \textit{GPS} (e.g. high energy consumption, inaccurate localization in certain environments such as indoor, and privacy violation due to direct location sharing). To this end, we propose a novel framework, in our initial approach, to emulate the functionality of a sensor by using multiple available soft sensors and machine intelligence algorithms. As a case study, the localization of city buses in a smart city setting is investigated by using the accelerometer and microphones of the passengers and supervised machine intelligence running in the cloud. In this application, the \textit{GPS} functionality is emulated by using these two soft sensors. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with more than 90\% accuracy in estimating the location of public buses while preserving the actual location privacy of the smartphone users. This approach results in smartphone battery energy savings of 38--46\% (as compared to \textit{GPS}-based approaches) due to the elimination of the power-hungry \textit{GPS} devices. Additional sensor recruitment schemes with various sensor combinations of accelerometer and microphone are developed in an extension work to examine both localization accuracy and energy consumption performance. Furthermore, in a subsequent work, crowd-sensed data undergoes an unsupervised machine learning module that estimates the location of the vehicle. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with 95\% accuracy in estimating the location of public vehicles in the best case and with 80\% accuracy in the worst case. Since data trustworthiness is vital when data is crowd-solicited, assessment and quantification of the trustworthiness of participating sensors play a key role in the accuracy of the acquired information. To this end, we propose a reliability-aware participant recruitment scheme that assesses the trustworthiness of individual participants. Our simulation results lead to approximately 96\% localization accuracy under the reliability-aware recruitment scheme compared to 93\% localization accuracy the reliability-unaware participant recruitment scheme. Moreover, we introduce two trustworthiness-aware recruitment schemes in another work: Reliability-driven naive recruitment (\textit{RDNR}) and Reliability-driven exclusive recruitment (\textit{RDER}). We evaluate the performance of \textit{RDNR}, \textit{RDER}, and non-restrictive recruitment (reliability-unaware). Through simulations, we show that over 85\% and 98\% accuracy can be achieved in the worst and best cases, respectively, while consuming less energy than \textit{GPS}-based localization approaches.
Author:
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 2
Get Book Here
Book Description
Author: Amar Deep Pandey
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Global Positioning System (GPS)-based road traffic prediction is one of the predominating technology in the modern technological era, which facilitates smooth navigation and reduces mobility time. Google Maps is used worldwide for traffic congestion and delay prediction which relies upon the GPS location of the individual's smartphone to predict traffic congestion and delay by stored data and current GPS locations. However, this method sometimes malfunctions due to the uneven distribution of passengers in different vehicle types on the roadway as there are far more passengers in buses as compared with trucks, if few buses are present in the traffic stream then it will show congestion and delay in traffic. So, it is hard to correctly predict the congestion and delay in traffic without using classified vehicle count as the ratio of the area occupied by the vehicle on the roadway and the number of passengers in it is unevenly distributed for different vehicle types. Google Maps have some limitations as it does not incorporate details regarding the classified vehicle count and categories of vehicles as there are distinct categories of vehicles operating on the roadways, with varying sizes, speeds, and passenger capacities. Thus, it would be beneficial to overlay the information of GPS localization, using Google Maps, with classified vehicle count and vehicle categories to estimate better road traffic congestion and delay. Thus the augmentation of Google Maps is required by integrating the classified traffic volume count with categories of vehicles, the present work envisages the same. For the present study, two mid-sized Indian cities in the state of Uttar Pradesh (Varanasi and Gorakhpur) were selected due to the diverse nature of mixed road traffic. For classified vehicle count data, video recording was carried out by using video recording cameras at various sites in both cities. The data of classified vehicles for both directions of traffic streams were manually counted by project staff from the video recordings and GPS coordinates were also integrated with datasets. Subsequently, various other hand-crafted features were extracted before training the machine learning-based forecasting models (ARIMA and SVM) for traffic volume prediction for a specified GPS location. The classified road traffic vehicle count was predicted using previously observed values, thereby helping in making a good decision about route selection and traffic management. Further, this work annotates the forecasted data overlay with GPS value as per the traffic condition to build a XGBoost-based classification model. The build classifier can classify the road conditions in real-time. The rigorous experimental results and real-world evaluation depicted the effectiveness of the proposed technique on the collected dataset.
Author: Tariq Alsahfi
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 143
Get Book Here
Book Description
Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ..) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applica- tions in different fields can utilize such trajectory data, for example, traffic control management, social behavior analysis, wildlife migrations and movements, ship tra- jectories, shoppers behavior in a mall, facial nerve trajectory, location-based services (LBS) and many others. Fortunately, there are now many trajectory data sets avail- able that collected from moving objects such as cars with enabled GPS devices. Two main challenges arise when dealing with TD: 1) storing and analyzing TD data due to a large amount of data that arrives in a streaming and unpredictable rate. 2) inaccurate capturing of the exact location of moving objects due to the errors caused by GPS devices. In order to tackle these two problems and gain useful knowledge from TD, in this dissertation, we provide a framework called Trajectory Data Ware- house (TDW). This framework aims to review existing studies on storing, managing, nd analyzing TD using data warehouse technologies. Furthermore, we provide the requirements for building the TDW with different applications using the TDW. Despite the second challenge, in this dissertation also, we utilize the vast amount of TD by building a digital road map. Because road maps are important in our personal lives and are widely used in many different applications; therefore, an up-to- date road map is essential. We propose a novel method to generate road maps using GPS trajectories that is accurate with good coverage area, has a minimum number of vertices and edges, and several details of the road network. Besides, our algorithm extracts road features such as turn restrictions, average speed, road length, road type, and the number of cars traveling in a specific portion of the road. To demonstrate the accuracy of our proposed algorithm, we conduct experiments using two real data sets and compare our results with two baseline methods. The comparisons indicate that our algorithm is able to achieve higher F-score in terms of accuracy and generates a detailed road map that is not overly complex. Lastly, we present a data fusion framework for heterogeneous data Sources for Intelligent Transportation Systems (ITS). This framework aims to provide data fusion techniques to integrate and extract features from heterogeneous data sources to be ready for deep learning training approaches. We also generate preprocessed real-world traffic datasets that are publicly available to solve ITS-related problems. The traffic datasets have rich features such as traffic flow, average speed, vehicle occupancy, weather conditions, incidents information, congestion reports, point of interest locations, and temporal features. Furthermore, we provide two applications to show the importance of our data fusion techniques. (1) Traffic datasets analysis and visualization, where we build a data cube to provide in-depth analysis of the dataset. Also, a visual-interactive GIS tool that presents the results in different methods. (2) Traffic flow forecasting using deep learning, we performed a comprehensive study on how different features can improve the traffic flow prediction models. The results show that deep learning approaches achieved better results when extra features are considered.
Author: collective of authors
Publisher: Czech Institute of Academic Education
ISBN: 808820304X
Category : Business & Economics
Languages : en
Pages : 343
Get Book Here
Book Description
International Academic Conference on Global Education, Teaching and Learning and International Academic Conference on Management, Economics, Business and Marketing and International Academic Conference on Transport, Logistics, Tourism and Sport Science. Vienna, Austria 2017 (IAC-GETL + IAC-MEBM 2017 + IAC-TLTS 2017), November 24 - 25, 2017.
Author: Chi-Hua Chen
Publisher: MDPI
ISBN: 303651192X
Category : Technology & Engineering
Languages : en
Pages : 162
Get Book Here
Book Description
This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.
