Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Evaluating traffic networks is crucial for administration of roadway systems, to better address congestion, safety, and air quality issues around the globe. However, challenges in implementation abound, including major investment costs, large and dynamic data streams and a need for real-time response. Recently developed Global Positioning System (GPS) data loggers are a promising tool for traffic monitoring, thanks to their low cost, ready availability on smartphones, and ability to simultaneously track many travelers and vehicles, relative to expensive, built-in traffic GPS. GPS data from many travelers provides real-time details of traffic conditions and can improve active traffic management using various big-data analytics. We demonstrate how to couple such GPS data to estimate relative roadway speeds in order to improve system management. By analyzing real-time traffic surveillance software with high data coupling and concurrent processing, a new coupling method for real-time traffic evaluation is proposed. Experimental results show efficient coupling of all available GPS data with road condition can improve traffic state estimation accuracy. This new method may increase matching accuracy by more than 1 m in vehicle position. Over 98% of GPS data can be successfully matched to service routes when the low-cost GPS devices are used to detect real-time traffic conditions. The results of traffic network evaluation could well serve as a driving assistant for connected and autonomous vehicles (C/AVs) and other traffic operations.

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

Acquiring global positioning system (GPS) data can help researchers understand the relationship between older adult’s location/activity and their health, but existing GPS data collection modes suffer from issues with precision, consistency, and acceptability. Some limitations of current GPS mobile apps are satellite service disruption, power saving mode interference, miscalibration of device’s compass, and user-error during data collection which result in missing or inaccurate data. We present a novel multilingual GPS mobile app and live administrative dashboard to address these limitations. The app performs triangulation between GPS satellite data, nearest cellular tower, and WiFi router location data which are processed through a unique GPS algorithm with a mean error of only 1.5 meters. Since GPS data results in coordinates commonly reverse geo-coded to place-based datasets, the accuracy is critical in determining place type or other characteristics. The app is additionally designed with redundancy and fault tolerance in mind to run continuously in the background of a user’s smartphone, so there is no data loss from accidentally closing the application or restarting the phone. Moreover, this new application has a flexible sampling rate ranging from 1 second to 20 minutes. Sampling frequency flexibility can adjust the battery life and reduce the need to charge as frequently. Last, application programming interface (API) integrations allow for live data stream, which can improve monitoring and troubleshooting in the field. For research with older adult populations, these features can not only improve the accuracy of collected data but also increase the acceptability of collecting GPS data.

Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

Defining the source region of the Indian Ocean Tsunami from GPS, altimeters, tide gauges and tsunami models

The 2002 Denali fault, Alaska, earthquake ( M w 7.9) caused one or more components of most broadband seismometers in western Canada to clip, yet did not trigger strong-motion instruments, thus leaving a substantial gap in the seismic record of this event. However, the large-amplitude surface waves generated by this event were well recorded by the Global Positioning System (GPS) in the same region, out to epicentral distances of more than 3000 km. In this article, we explore the capabilities of GPS seismology, specifically how the relative strengths of GPS and seismic data sets can be exploited in order to more effectively study earthquake source characteristics and wave-propagation effects. High-rate (1-Hz) GPS data from 23 stations throughout western North America have been analyzed to derive displacement waveforms for this event, and the impact of instrumentation (GPS receiver model) and error-reduction strategy (modified sidereal and spatial filtering) on the noise characteristics of displacement time series at each GPS site was assessed. After applying error-reduction methods to GPS displacements, the final average noise floors of 0.5 cm in the horizontal and 1.5 cm in the vertical indicate that large dynamic displacements are observable by GPS. We validate the GPS displacements by comparing broadband seismic recordings (integrated to displacement) with GPS recordings for four effectively colocated sets of instruments. We show excellent agreement between the unclipped seismic and GPS recordings of the surface waves from the Denali earthquake over the period range of 10–50 sec and for ground displacements exceeding about 1.0 cm. Thus, a large GPS displacement data set is now available for western North America, an area where records of this event were previously missing or incomplete. The final GPS seismograms are archived at Incorporated Research Institutions for Seismology (IRIS) for public use in future studies of the 2002 Denali earthquake.

Surveys are a critical resource for social, economic, and health research. The ability to efficiently collect these data and develop accurate post-survey adjustments depends upon reliable data about effort required to recruit sampled units. Level-of-effort paradata are data generated by interviewers during the process of collecting data in surveys. These data are often used as predictors in nonresponse adjustment models or to guide data collection efforts. However, recent research has found that these data may include measurement errors, which would lead to inaccurate decisions in the field or reduced effectiveness for adjustment purposes (Biemer, et al., 2013; West and Little, 2013). In order to assess whether errors occur in level-of-effort paradata, we introduce a new source of validation data for call records -- Global Positioning System (GPS) data generated by smartphones carried by interviewers. We examine the quality of the GPS data and then use the GPS data to characterize interviewer travel within sampled area segments. We also link the GPS data with the interviewer-reported call records in order to identify potential errors in the call records. Given the lack of a gold standard, we perform a sensitivity analysis under various assumptions to see how this would change our conclusions.

Global Positioning System (GPS) data for phase delay differential between Ll and L2 frequencies are analyzed by a model in which the combined effect of hardware and phase ambiguity terms is represented by a constant for each receiver‐satellite path and the ionospheric contribution is represented by the RIBG model (ray trace through the combined ICED (ionospheric conductivity and electron density), Bent, and Gallagher ionospheric models). RIBG contains a detailed global empirical climatological ionospheric model of the electron density versus height profile (EDP) from 80 km up to the plasmapause, which is combined with a full three‐dimensional ray trace propagation model. Discrete inverse theory (DIT) is used to fit the driving parameters of RIBG to about 2 hours worth of data from a single GPS receiver at various geographical locations. Validity is demonstrated by the ability of the GPS‐updated RIBG model to predict independent measurements of (1) vertical total electron content (TEC) over the ocean out to substantial distances from a GPS receiver, often in excess of 2000 km at midlatitudes, and (2) the electron density versus height profile (EDP). Performance variation with magnetic latitude is noted. Flexibility in RIBG to adjust different driving parameters for different parts of the EDP, in order to obtain a best fit of GPS data, benefits the EDP prediction. Implications of this approach for GPS navigation and other radio system applications are discussed.

With sparse and uneven site distribution, Global Positioning System (GPS) data is just barely able to infer low-degree coefficients in the surface mass field. The unresolved higher-degree coefficients turn out to introduce aliasing errors into the estimates of low-degree coefficients. To reduce the aliasing errors, the optimal truncation degree should be employed. Using surface displacements simulated from loading models, we theoretically prove that the optimal truncation degree should be degree 6–7 for a GPS inversion and degree 20 for combing GPS and Ocean Bottom Pressure (OBP) with no additional regularization. The optimal truncation degree should be decreased to degree 4–5 for real GPS data. Additionally, we prove that a Scaled Sensitivity Matrix (SSM) approach can be used to quantify the aliasing errors due to any one or any combination of unresolved higher degrees, which is beneficial to identify the major error source from among all the unresolved higher degrees. Results show that the unresolved higher degrees lower than degree 20 are the major error source for global inversion. We also theoretically prove that the SSM approach can be used to mitigate the aliasing errors in a GPS inversion, if the neglected higher degrees are well known from other sources.

This paper describes a process of converting raw Global Positioning System (GPS) data to a routable road map. In fact, it is a large scale database collected from thousands of vehicles circulating on Tunisian public roads. Moreover, the paper contains the architecture used to collect GPS data from these vehicles using GPRS connection and all the steps until getting the road traces. The data flow is composed of many steps which are: Collecting data which consists of extracting National Marine Electronics Association(NMEA) sentences; Filtering raw GPS nodes to eliminate outliers and noise caused by several sources of errors; Clustering step, in which we used two methods partitional (k-means)and hierarchical (agglomerative)clustering techniques. We compare them and we choose the most suitable for our work. In fact, K-means algorithm is carried out in order to partition data and facilitate handling the big data sets; Generating a Tunisian map network from our database and map-matching it with Google maps in order to make a comparison between them.

The Global Positioning System (GPS) data processing method is a key to obtaining the accuracy of the 3-D coordinates. Therefore, there is commercial software issued by tool vendors and can be accessed by users. Among the software are Hi-Target Geomatics Office (HGO) and Trimble Business Center (TBC). Both of these software can be used to process GPS data by post-processing and can be accessed by users. In this study, a comparison of the results of GPS data processing using the two software will be carried out by comparing the accuracy of each software for processing GPS data at baseline of 5 – 10 m and 5 – 20 km. The results of data processing that was carried out in this study found that GPS data processing using both software at a baseline of 5 – 10 m did not show significant differences with the two having a difference of 1 mm in each coordinate component. However, at 5 – 20 km baseline the results of GPS data processing using TBC are more accurate with a difference of -0.0050 m in Northing, -0.0800 m in Easting and -0.0550 m in Elevation compared to the actual coordinates. With these results, the TBC software is more recommended.

Transportation management, as a part of the supply chain management, is a complex process that consists of planning and delivering goods to customers. The paper presents a complete multi-phase intelligent and adaptive transportation management system, which includes data collection, parameter tuning, and the heuristic algorithm based on the Tabu search for vehicle routing. The paper describes the procedure for collecting Global Positioning System (GPS) data and analyzing the compliance with the proposed routes based on the data collected. The described routing algorithm is powerful and supports many real-world limitations. An algorithm for the anomaly detection in the GPS data is presented as well as the usage of collected GPS data to improve the future results of the algorithm. The concept was implemented and tested on real data in some of the largest distribution companies in Bosnia and Herzegovina. The proposed approach resulted with more than satisfactory results in real-world application.