Passive Global Positioning System Research Articles

Candrive-Development of a Risk Stratification Tool for Older Drivers.

Assessing an older adult's fitness-to-drive is an important part of clinical decision making. However, most existing risk prediction tools only have a dichotomous design, which does not account for subtle differences in risk status for patients with complex medical conditions or changes over time. Our objective was to develop an older driver risk stratification tool (RST) to screen for medical fitness-to-drive in older adults. Participants were active drivers aged 70 and older from 7 sites across 4 Canadian provinces. They underwent in-person assessments every 4months with an annual comprehensive assessment. Participant vehicles were instrumented to provide vehicle and passive Global Positioning System (GPS) data. The primary outcome measure was police-reported, expert-validated, at-fault collision adjusted per annual kilometers driven. Predictor variables included physical, cognitive, and health assessment measures. A total of 928 older drivers were recruited for this study beginning in 2009. The average age at enrollment was 76.2 (standard deviation [SD]=4.8) with 62.1% male participants. The mean duration for participation was 4.9 (SD=1.6) years. The derived Candrive RST included 4 predictors. Out of 4483 person-years of driving, 74.8% fell within the lowest risk category. Only 2.9% of person-years were in the highest risk category where the relative risk for at-fault collisions was 5.26 (95% confidence interval=2.81-9.84) compared to the lowest risk group. For older drivers whose medical conditions create uncertainty regarding their fitness-to-drive, the Candrive RST may assist primary health care providers when initiating a conversation about driving and to guide further evaluation.

Using GPS Technology to Measure On-Time Running of Scheduled Bus Services

Assessing bus service running times has been a difficult and expensive task for many urban bus operators. This has restricted the ability of operators to collect adequate data to identify problems and improve service levels. Passive Global Positioning System (GPS) devices offer a low-cost means of collecting large amounts of highly accurate data, to be used in an ongoing performance assessment program. Some programming skills are required to break continuous GPS data into information that is meaningful to a scheduler. This article provides an overview of a software application developed to process and analyze GPS datasets collected by a bus operator in Sydney, Australia, in 2002-2003. The data collection procedure and processing algorithms are described, and examples are presented of output produced by the software. The algorithm developed to process the GPS data worked well. We conclude that passive GPS is a cost-effective method of collecting data on performance. For operators running buses on five or more routes, system development costs could be recovered within two to three years.

The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults

BackgroundThere is increasing interest in examining the life space mobility and activity participation of older adults in the community using sensor technology. Objective data from these technologies may overcome the limitations of self-reported surveys especially in older adults with age-associated cognitive impairment. This paper describes the development and validation of a prototype hybrid mobility tracker for assessing life space mobility and out-of-home activities amongst 33 community-ambulant older adults in Singapore.MethodsA hybrid mobility tracker was developed by combining a passive Global Positioning System logger, tri-axial accelerometer and radio-frequency identification. Objective measures of life space, derived from 1 week of tracking data using Geographic Information Systems, were the maximum Euclidean distance from home (Max Euclid) and the area of the minimum convex polygon surrounding all GPS waypoints (MCP area). Out-of-home activities were quantified by visually identifying the total number of activity nodes, or places where participants spent ≥5 min, from mobility tracks. Self-reported measure of life space in 4 weeks was obtained using the University of Alabama at Birmingham Study of Life Space Assessment (UAB-LSA) questionnaire. Self-reported out-of-home activities were recorded daily in a travel diary for 1 week. Bivariate correlations were used to examine convergent validity between objective and subjective measures of life space and out-of-home activities.ResultsThe mean age of participants was 69.2 ± 7.1 years. The mean UAB-LSA total score was 79.1 ± 17.4. The median (range) Max Euclid was 2.44 km (0.26–7.50) per day, and the median (range) MCP area was 3.31 km2 (0.03–34.23) per day. The UAB-LSA total score had good correlation with Max Euclid (r = 0.51, p = 0.002), and moderate correlation with MCP area (r = 0.46, p = 0.007). The median (range) total number of activity nodes measured by tracker of 20 (8–47) per week had a good correlation with the total activity count recorded in the travel diaries of 15 (6–40) per week (r = 0.52, p = 0.002).ConclusionsThe tracking system developed to understand out-of-home travel was feasible and reliable. Comparisons with the UAB-LSA and travel diaries showed that it provided reliable and valid spatiotemporal data to assess the life space mobility and activity participation of older adults.

Development of a global positioning system data-based trip-purpose inference method for hazardous materials transportation management

The shipments of hazardous materials (hazmat) which are indispensable for economic and social development have increased; accordingly, a rising number of incidents involving hazmat transportation may inflict more dread damages to both people and environment. This severe situation has prompted the need for deep mining trip purposes using trajectory information in order to enhance the hazmat-transportation regulatory. This paper presents an unsupervised two-phase framework for inferring multiple trip purposes (i.e. loading, unloading, in-yard, and other stops) based on the passive global positioning system (GPS) data during the hazmat-transportation process. In detail, a scalable ordering points to identify the clustering-structure mixture algorithm (SOMA) is first developed to group hazmat vehicles trip ends into hotspot places in phase I; In phase II, a two-stage trip-purpose identification approach is proposed with a combination of the fuzzy c-means (FCM) method and the point-of-interest (POI) information. The effectiveness and efficiency of the designed two-phase framework are evaluated through the real-world datasets, which are generated by more than 12,000 vehicles in Liaoning Province, China. The results demonstrate that the method can infer four types of freight trip purposes with an accuracy of 82.1%. The proposed approach framework can help analyze the vehicle trips associated with the loading states, which will provide effective decision-making support for the hazmat-transportation regulatory.

Assessment of Radiometer Calibration with GPS Radio Occultation for the MiRaTA CubeSat Mission.

The Microwave Radiometer Technology Acceleration (MiRaTA) is a 3U CubeSat mission sponsored by the NASA Earth Science Technology Office (ESTO). The science payload on MiRaTA consists of a tri-band microwave radiometer and Global Positioning System (GPS) radio occultation (GPSRO) sensor. The microwave radiometer takes measurements of all-weather temperature (V-band, 50-57 GHz), water vapor (G-band, 175-191 GHz), and cloud ice (G-band, 205 GHz) to provide observations used to improve weather forecasting. The Aerospace Corporation's GPSRO experiment, called the Compact TEC (Total Electron Content) and Atmospheric GPS Sensor (CTAGS), measures profiles of temperature and pressure in the upper troposphere/lower stratosphere (∼20 km) and electron density in the ionosphere (over 100 km). The MiRaTA mission will validate new technologies in both passive microwave radiometry and GPS radio occultation: (1) new ultra-compact and low-power technology for multi-channel and multi-band passive microwave radiometers, (2) the application of a commercial off the shelf (COTS) GPS receiver and custom patch antenna array technology to obtain neutral atmospheric GPSRO retrieval from a nanosatellite, and (3) a new approach to spaceborne microwave radiometer calibration using adjacent GPSRO measurements. In this paper, we focus on objective (3), developing operational models to meet a mission goal of 100 concurrent radiometer and GPSRO measurements, and estimating the temperature measurement precision for the CTAGS instrument based on thermal noise. Based on an analysis of thermal noise of the CTAGS instrument, the expected temperature retrieval precision is between 0.17 K and 1.4 K, which supports the improvement of radiometric calibration to 0.25 K.

Use of Multisource Global Positioning System Data to Characterize Multiday Driving Patterns and Fuel Usage in a Large Urban Region

The paper describes the use of Global Positioning System (GPS) data obtained from commercial and project-specific sources to examine the travel behavior and fuel consumption patterns of drivers over a 3-day period in Gauteng Province, South Africa. Data for commercial (truck and light delivery vehicle) traffic were obtained from a commercial fleet management provider that continuously tracked the movements of 42,000 vehicles. Data for private car users came from a panel of 720 drivers, whose multiday driving activity was tracked by mobile passive GPS loggers. The driving behavior of the two driver populations was analyzed and compared in terms of the total distance traveled: spatial patterns (e.g., the amount of travel on different types of road) and temporal variations (e.g., variations between times of day and between multiple days). The detailed nature of the GPS data permitted the estimation of fuel consumption at a very disaggregate level (by link and time of day) and the identification of differences between user groups; these factors have significant implications for transport and energy policy. A new indicator, the recovery ratio, was introduced to assess the relationship between fuel use and distance traveled on different classes of road and to help identify equity distortions between user groups. The paper also discusses research needs related to the collection and integration of GPS data from multiple sources for model calibration and program evaluation.

Postprocessing Procedures for Person-Based Global Positioning System Data Collected in the French National Travel Survey 2007–2008

A challenge in the postprocessing of data for a passive Global Positioning System–based travel survey, which constitutes the essence of this paper, is the development of a series of methods for automatically restoring continuous sequences, both in space and in time. Thus trips and activities occurring during the survey period should be identified in chronological order and should respect conventional definitions. On the basis of previous research results and a combination of the experience from the French National Travel Survey 2007–2008 and the Personal Mobility Survey in Lille, France, a series of methods has been developed and applied: (a) filtering invalid data, (b) cutting the data chains into “moving” and “stopping” segments, (c) estimating missing data, (d) assuming mode choice, (e) grouping stop points into visited locations, (f) identifying the type of visited points, (g) map-matching, and (h) connecting segments to trips. To use the maximum available information and guarantee the quality of the results, some feedback between the methods is implemented within the algorithm. The resulting processing system is ready for use in other applications.

Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology

The assessment of strategies designed to manage the continued growth in road-based freight and associated externalities has been hampered by a paucity of disaggregate data on commercial vehicle movements. When disaggregated data are available, the analysis of commercial vehicle route and trip chain structure can provide insightful information about urban commercial vehicle tours, travel patterns, and congestion levels. Over the past 15 years, the ability to collect detailed travel information has been expanded by developments in global positioning system (GPS) technology. In mid-2006, a GPS survey of commercial vehicles was piloted in Melbourne, Australia, to support a major update of freight data and modeling capabilities in the metropolitan region. The survey used passive GPS methods in which the truck driver's involvement in the data collection effort was minimal. The contributions of this research to the field of urban freight data collection were fourfold: (a) describing implementation issues with the data collection, (b) detailing the algorithms used to process the raw GPS data into meaningful travel and trip information, (c) presenting a discussion of pilot survey data tour results, and (d) discussing potential uses and limitations of GPS technology in urban freight modeling and planning. Despite processing challenges, GPS provides an appealing method to enrich commercial vehicle data collection and enhance our understanding of on-road behavior. Because increasing numbers of commercial vehicles become equipped with GPS receivers, only privacy concerns remain as a major barrier to gathering and using such data on a widespread basis in the future.

Impact of vehicle platform on global positioning system performance in intelligent transportation

The impact of the vehicle platform on the global positioning system (GPS) performance in automotive application was investigated. Five distinct vehicle platforms were used. Each vehicle platform was equipped with a GPS antenna mounted under the front instrument panel. Two sets of measurements were conducted. The first set consisted of the vehicle-level antenna radiation pattern measurements of a passive GPS antenna. The second set consisted of a series of field tests in highway, foliage and urban driving environments. GPS position availability and position accuracy were analysed from the field testing. Results from the antenna radiation pattern measurements and the field testing were analysed to study and quantify the impact of the vehicle platform on the GPS system performance.

Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues

With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.

2‐cm GPS altimetry over Crater Lake

Differences in electromagnetic path delay, between direct Global Positioning System (GPS) signals and those reflected from the surface of Crater Lake, have led to lake surface height estimates with 2‐cm precision in 1 second. This is the first high‐precision altimetric demonstration with GPS from sufficient altitude (≈ 480 m) to probe fundamental experimental errors, which bear on future air‐and spaceborne passive GPS altimetry. It also serves as the first demonstration of a new approach to altimetric remote sensing in the coastal region, an area that is poorly measured by conventional radar altimetry. Time‐series analyses suggest that tropospheric and thermal noise fluctuations dominate the altimetric error in this experiment. Estimating the differential delay from several simultaneously visible satellites may enable tropospheric error estimation and correction. Thermal noise on the reflected signal will be reduced with fully polarimetric observations and larger antenna apertures.