Jean Wolf

Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data

Several recent pilot studies combined Global Positioning System (GPS) technology with travel survey data collection to evaluate opportunities for improving the quantity and accuracy of travel data. These studies used GPS to supplement traditional data elements collected in paper or electronic travel diaries. Although many traditional trip elements can be obtained from the GPS data, trip purpose has remained an important element, requiring the use of a diary to continue. Presented are the results of a proof-of-concept study conducted at the Georgia Institute of Technology that examined the feasibility of using GPS data loggers to completely replace, rather than supplement, traditional travel diaries. In this approach, all GPS data collected must be processed so that all essential trip data elements, including trip purpose, are derived. If this processing is done correctly and quickly, then the computer-assisted telephone interview retrieval call could be shortened significantly, reducing both respondent burden and telephone interview times. The study used GPS data loggers to collect travel data in personal vehicles. The GPS data were then processed within a geographic information system (GIS) to derive most of the traditional travel diary elements. These derived data were compared with data recorded on paper diaries by the survey participants and were found to match or exceed the reporting quality of the participants. Most important, this study demonstrated that it is feasible to derive trip purpose from the GPS data by using a spatially accurate and comprehensive GIS.

Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey

Trip underreporting has long been a problem in household travel surveys because of the self-reporting nature of traditional survey methods. Memory decay, failure to understand or to follow survey instructions, unwillingness to report full details of travel, and simple carelessness have all contributed to the incomplete collection of travel data in self-reporting surveys. Because household trip survey data are the primary input into trip generation models, it has a potentially serious impact on transportation model outputs, such as vehicle miles of travel (VMT) and travel time. Global Positioning System (GPS) technology has been used as a supplement in the collection of personal travel data. Previous studies confirmed the feasibility of applying GPS technology to improve both the accuracy and the completeness of travel data. An analysis of the impact of trip underreporting on modeled VMT and travel times is presented. This analysis compared VMT and travel time estimates with GPS-measured data. These VMT and travel time estimates were derived by the trip assignment module of each regions travel demand model by using the trips reported in computer-assisted telephone inter views. This analysis used a subset of data from the California Statewide Household Travel Survey GPS Study and was made possible through the cooperation of the metropolitan planning organizations of the three study areas (Alameda, Sacramento, and San Diego, California).

ACCURACY ISSUES WITH ROUTE CHOICE DATA COLLECTION BY USING GLOBAL POSITIONING SYSTEM

Advancements in global positioning system (GPS) technology now make GPS route choice data collection for travel diary studies and other transportation applications a reality. Opportunities abound for increased quantities of data, for improved quality of data, and for new data elements that were once considered too burdensome or expensive to capture. For example, automated travel diaries can electronically capture trip purpose, origin and destination location names, and driver and passenger names at the push of a button. An accompanying GPS receiver can accurately capture origin and destination locations, departure and arrival times, as well as trip lengths and travel routes. This wealth of data can be used to validate or calibrate travel demand models, for in-vehicle information systems analysis, and for modeling mobile source emissions across a given network. These data collection and processing advancements do have their costs, however. In fact, care and caution should be exercised when GPS technologies are selected and used to collect route choice data. The focus of this paper is on the accuracy issues related to route choice data collection and processing using GPS technology. Vendor specifications, observation techniques, data collection procedures, data postprocessing, and the importance of using a reliable and accurate geographic information system (GIS) database are examined in detail. Critical issues in the calculation of GPS accuracy are reviewed. Finally, recent experience in Atlanta is reported, and recommendations designed to reduce the introduction of error into automated route choice data collection are provided.

ACCURACY OF GLOBAL POSITIONING SYSTEM FOR DETERMINING DRIVER PERFORMANCE PARAMETERS

Global Positioning System (GPS) technology can continuously monitor the time and location of vehicle usage. By recording and analyzing detailed vehicle activity data, researchers can analyze the safety and environmental implications of driver behavior and trip-making patterns. In 2000, NHTSA awarded the Georgia Institute of Technology a contract to equip 1,100 vehicles with a GPS-enhanced device to collect speed and location data. The objective was to acquire more accurate information on the role of excessive speed on crash frequency and severity. GPS technology allows the researcher to continuously measure driver speed, acceleration, and location. When merged with roadway characteristics within a geographic information system (GIS) environment, determinations of driver risk-taking behavior can be made. Second, continuous logging of GPS data allows researchers to capture high-resolution vehicle activity immediately before a crash event, reducing the potential error and bias introduced during determination of precrash speed estimates. Until May 1, 2000, the military degraded the position accuracy of GPS signals for commercial use, known as selective availability. For researchers, life without selective availability is a great improvement. Travel routes can clearly be discerned without the addition of differential correction units. The accuracy of speed, acceleration, and position data obtained from GPS signals for use in determining driver performance parameters without selective availability were tested. The test included four GPS packages, both corrected and uncorrected, simultaneously validated against a distance-measuring instrument. Equipment configuration, data collection methods, and sources of error are reported. The results suggested that non-corrected data can be used to obtain data within a reasonable range of the application requirements. Even without selective availability, GPS accuracy is still problematic in urban canyons and under heavy tree canopies. Although filtering for urban canyon outliers is labor intensive in a continuous monitoring situation, improvements in GIS hold promise for automation of this task.

Global Positioning System-Assisted Prompted Recall Household Travel Survey to Support Development of Advanced Travel Model in Jerusalem, Israel

The paper describes recent experience with the application of an innovative Global Positioning System (GPS)–assisted prompted recall (PR) method for a large-scale household travel survey (HTS) in Jerusalem, Israel. The survey was designed to support development of an advanced activity-based model (ABM). The requirements for an HTS to support an advanced ABM are discussed, and the corresponding decisions for survey methods are substantiated. Development of an advanced ABM requires individual records for the entire daily pattern without gaps, missing trips, overlaps, or other data inconsistencies found in a conventional HTS. A consistent record of joint activities and trips of multiple household members is essential. In addition, high levels of spatial and temporal resolution are required. The GPS-assisted PR survey has been identified as the most promising methodology for meeting these requirements. The experience of the first phase of the Jerusalem HTS in 2010 proved the feasibility of the GPS-PR method for all population sectors including specific Orthodox Jewish and Arab populations, which typically featured large household sizes. Various structural comparisons of trip and tour rates obtained during the first phase of the Jerusalem GPS-assisted HTS (3,000 households) with the non-GPS surveys previously implemented in Jerusalem and several metropolitan regions in the United States as well as comparisons between the GPS and non-GPS subsamples within the Jerusalem HTS were made. The results confirmed the ability of the GPS-PR approach to create full and consistent daily records of individual activity travel patterns and practically eliminate the underreporting issues that have plagued HTS.

Household Travel Surveys with GPS: An Experiment

This paper documents the results of a pilot test done for the Oregon Household Travel Survey. The pilot was designed to enable the Oregon Department of Transportation to determine the role of a Global Positioning System (GPS) in the upcoming survey effort. Specifically, a three-pronged approach was employed. Households were randomly selected for inclusion in the study and then assigned to one of three groups: (a) the traditional survey approach, (b) the traditional approach with GPS, and (c) GPS only. A total of 299 households from the city of Portland, Oregon, were recruited into the pilot, with 235 completing all required activities. A comprehensive evaluation of the similarities and differences in results across the three groups showed differences in respondent burden, completeness of travel details obtained, and costs. Results from this experiment also showed differences in nonresponse bias. The traditional survey had an expected nonresponse for the large households, low-income households, and young adults. Minority participation was on par with census figures. The GPS groups showed higher participation rates for young adults and nonminorities. These data confirmed the general thought that GPS was an effective tool for mitigating nonresponse among young adults. However, the minority nonresponse bias increased significantly with technology, suggesting that other methods would be more appropriate. With regard to completeness of data, geocoding rates are higher for the GPS groups, and there are significant differences in trip departure times, which could affect peak hour and time-of-day modeling. As expected, the costs were higher for the GPS groups, but the expectation is that these costs will fall as processes are standardized across studies and new technologies are introduced.

Binary recursive partitioning method for modeling hot-stabilized emissions from motor vehicles

An alternative statistical modeling approach, hierarchical tree-based regression (HTBR), is presented for developing modal correction factors for hydrocarbon (HC) emissions from motor vehicles. The term modal refers to operating modes of vehicle activity such as cruise, idle, deceleration, and acceleration. Explanation of the statistical theory is provided, followed by a presentation of specific modeling results for HCs. The modeling results are based on 4,800 vehicle emissions tests representing 29 laboratory testing cycles. HTBR methods are indicated to overcome statistical difficulties that are problematic for classical ordinary least-squares (OLS) regression, a commonly applied statistical technique for analyzing emissions data. HTBR methods are more adept at treating interactions and monotonic transformations on independent variables, better at handling categorical independent variables with more than two levels, not adversely affected by multicollinearity, and good at capturing nonadditive behavior across the range of independent variables. Unfortunately, HTBR theory is less well developed than OLS regression theory, and statistical parameter properties, such as efficiency, unbiasedness, and consistency, need further development. The HTBR modeling results for HCs are insightful. Hydrocarbon emissions from normal-emitting motor vehicles are most sensitive to changes in power (instantaneous speed2 ƃ acceleration) requirements of a given driving sequence, while high-emitting vehicles are sensitive to both the amount of idle activity and positive kinetic energy (instantaneous speed ƃ acceleration) in a given driving sequence. Vehicle model year, engine size (cubic centimeters of displacement), curbside weight, and fuel delivery type (fuel injected, throttle body injected, carbureted), also were indicated to influence emission rates. Finally, high- and normal-emitting vehicles are sensitive to different operational and vehicle specific factors.

HIGH-EMITTING VEHICLE CHARACTERIZATION USING REGRESSION TREE ANALYSIS

A small fraction of motor vehicles on the roadway emit a disproportionate fraction of pollutant emissions, especially for carbon monoxide and hydrocarbons. Generally, these “high emitters” or “super emitters” exhibit higher emissions rates under all operating conditions than do “normal emitters.” Since the instantaneous emissions response between normal- and high-emitting vehicles can differ by one or more orders of magnitude, so do their average emissions over a “typical” trip. Identifying the proportion of normal- and high-emitting vehicles in an urban area and quantifying their emissions is vital for accurate emission inventory accounting. A methodology by which high and normal emitters can be classified is presented. Unlike previous emitter classification approaches, the approach is data driven and relies entirely on hot-stabilized emissions results. A statistical classification scheme, better known as hierarchical tree based regression, is used to separate vehicles into homogenous emitter categories. The approach is shown to have a number of advantages. First, it is flexible with respect to both the number of classes and types of variables used to identify classes. Second, it considers the influence of a large number of vehicle and technology attributes on emitter status. Third, it ensures that the highest emitters can be isolated from the normal emitters, so that separate emission rate models can be developed for these vehicles. Finally, the approach does not combine the effects of starts and hot-stabilized operations within the definition of high emitter, leading to a classification scheme whereby vehicles with poor start emissions characteristics will not be incorrectly classified as vehicles with poor hot-stabilized emission characteristics.

Hierarchical Tree-Based Versus Ordinary Least Squares Linear Regression Models: Theory and Example Applied to Trip Generation

Given the continual need for transportation professionals to forecast trends and the increasing availability of sophisticated and improved modeling methods in new and improved software packages, new methods should be explored to determine whether they can replace or supplement more classical statistical methods. One commonly used classical statistical technique for relating a continuous dependent variable with one or more independent variables (continuous or discrete) is ordinary least squares (OLS) regression. This method is routinely applied in transportation to forecast such things as energy use, trip attractions, trip productions, automobile emissions, and growth in vehicle miles traveled (VMT). Despite its widespread use and tremendous utility, however, OLS regression has limitations. It does not deal well with multicollinear independent variables, interactions between independent variables must be specified, the functional relationship between dependent and independent variables must be known (or approximated well), it cannot handle missing data well, and it does not treat satisfactorily discrete variables with more than two levels. Hierarchical tree-based regression (HTBR) may provide a better model for forecasting continuous response variables in transportation applications when the shortcomings of OLS regression are present. The theory of HTBR methods is presented. Then, an example using trip generation data is used to illustrate the types of models that result from OLS regression and HTBR methods. Finally, the limitations of HTBR are presented.

Adding maps (GPS) to accelerometry data to improve study participants’ recall of physical activity: a methodological advance in physical activity research

Objective Obtaining the ‘when, where and why’ of healthy bouts of moderate-to-vigorous physical activity (MVPA) provides insights into natural PA. Design In Salt Lake City, Utah, adults wore accelerometer and Global Positioning System (GPS) loggers for a week in a cross-sectional study to establish baseline travel and activity patterns near a planned Complete Street intervention involving a new rail line, new sidewalks and a bike path. Results At the end of the week, research assistants met with the 918 participants who had at least three 10 h days of good accelerometer readings. Accelerometer and GPS data were uploaded and integrated within a custom application, and participants were provided with maps and time information for past MVPA bouts of ≥3 min to help them recall bout details. Participants said that ‘getting someplace’ was, on average, a more important motivation for their bouts than leisure or exercise. A series of recall tests showed that participants recalled most bouts they were asked about, regardless of the duration of the bout, suggesting that participant perceptions of their shorter lifestyle bouts can be studied with this methodology. Visual prompting with a map depicting where each bout took place yielded more accurate recall than prompting with time cues alone. Conclusions These techniques provide a novel way to understand participant memories of the context and subjective assessments associated with healthy bouts of PA. Prompts with time-stamped maps that illustrate places of MVPA offer an effective method to improve understanding of activity and its supportive sociophysical contexts.