Marcelo Oliveira

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.

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.

Evaluation of Two Methods for Identifying Trip Purpose in GPS-Based Household Travel Surveys

Data needs for developing travel demand models have increased at the same time that household travel survey (HTS) participation rates have generally fallen over recent decades. GPS-assisted HTS methods are recognized today as the most promising direction in further enhancement of individual travel data collection. The principal advantage of the GPS-assisted survey technology is that a full stream of locations visited by the person is identified with a high level of spatial and temporal resolution, but automatic identification of trip purpose remains an issue that is difficult to solve. This paper evaluates the performance of two methods, choice modeling and decision tree analysis, that can be used to build models capable of identifying trip purpose. The developed methods assume that basic household- and person-level data, typically collected in an HTS, are available, as are supporting spatial data sets. The methods presented are then evaluated for a case study that employed data from the 2011 Atlanta Regional Commission HTS. The developed models produced encouraging results with overall accuracy greater than 70% across all purposes and around 90% for mandatory activities (i.e., work and school). The performance of the developed models was evaluated in terms of error rates by purpose category and the impact of ancillary spatial data. The paper concludes with a summary of the findings and recommendations for practitioners.

Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests

With the high costs associated with primary data collection, methods to improve the use and accessibility of newer sources of data such as Global Positioning System (GPS) data can benefit many transportation practitioners. GPS data can have multiple uses beyond traditional applications such as estimates of speed and travel times. GPS-related data that have been collected from automatic vehicle location systems, from highway sensors, as supplemental information to traditional travel surveys, and via passive technologies [e.g., Bluetooth, radio frequency identification (RFID), and smartphones] have shown promise for additional planning purposes. Some challenges to increased use of GPS data include addressing data bias; balancing precision, coverage, and confidentiality; resolving institutional issues such as data ownership; and addressing the complexity of combining these data with other sources to discern behavioral relationships. This report provides guidelines on the use of multiple sources of GPS data to understand travel behavior and activity. The guidelines are intended to provide a jump-start for processing GPS data for travel behavior purposes and provide key information elements that practitioners should consider when using GPS data. The report is structured in two volumes. Volume 1 presents the methods used and results of tests conducted. Volume II translates the results of the tests conducted into guidelines for planners and researchers to implement these procedures.

Applying GPS Data to Understand Travel Behavior, Volume II: Guidelines

With the high costs associated with primary data collection, methods to improve the use and accessibility of newer sources of data such as Global Positioning System (GPS) data can benefit many transportation practitioners. GPS data can have multiple uses beyond traditional applications such as estimates of speed and travel times. GPS-related data that have been collected from automatic vehicle location systems, from highway sensors, as supplemental information to traditional travel surveys, and via passive technologies [e.g., Bluetooth, radio frequency identification (RFID), and smartphones] have shown promise for additional planning purposes. Some challenges to increased use of GPS data include addressing data bias; balancing precision, coverage, and confidentiality; resolving institutional issues such as data ownership; and addressing the complexity of combining these data with other sources to discern behavioral relationships. This report provides guidelines on the use of multiple sources of GPS data to understand travel behavior and activity. The guidelines are intended to provide a jump-start for processing GPS data for travel behavior purposes and provide key information elements that practitioners should consider when using GPS data. The report is structured in two volumes. Volume 1 presents the methods used and results of tests conducted. Volume II translates the results of the tests conducted into guidelines for planners and researchers to implement these procedures.

Household-Level Global Positioning System Travel Data to Measure Regional Traffic Congestion

Regional performance metrics for traffic congestion typically rely on facility-based measurements and forecasted regional impacts. Global Positioning System (GPS) travel data from household travel surveys or panel studies can provide an alternative source of performance data that characterize the specific experiences of households in the region. Such data can also supplement regional performance data for congestion management programs and can be directly related to the experiences of the regions residents and the development of livability and mobility standards. GPS travel data from the 2010 Front Range Counts Travel Survey were used to generate vehicle travel performance metrics in the Denver, Colorado, area. At the regional and household levels, statistics regarding travel time index, number of stops, and overall delay were calculated. This new approach is compared with standard congestion measurement practice to highlight advantages and disadvantages. This supplemental household approach provides planners and policy makers with insights that can guide mitigation strategies and investment plans.

Improving Data Quality, Accuracy, and Response in On-Board Surveys: Application of Innovative Technologies

Transit agencies conduct origin–destination on-board surveys periodically to gather information regarding travel patterns and demographic data of their users and to collect customer satisfaction information. These surveys constitute a highly valuable means of obtaining important information on an agencys customers to provide a basis for effective transit planning and for regional travel demand modeling efforts. This paper describes the application of innovative technologies in the data collection process to improve data quality, data completeness, and data collection management. This includes the simultaneous collection of boarding and alighting count data at the stop level using Global Positioning System–enabled personal digital assistants, association of distributed surveys to boarding locations, and a web-based sample and productivity management system. These technologies allow for automatic collection of boarding location, arrival and departure times, and transit trip times. An imputation procedure was developed to derive the most likely alighting location of each collected sample. Joint application of these technologies reduces survey length and thereby minimizes respondent burden.

Study of radar detector use on Georgia highways

Police radar is known to have an effect on the speed of drivers. This effect derives from the presence of vehicles equipped with radar detectors in the traffic stream. The most common method for determining radar detector use is visual examination of the traffic stream. Other methods employ specially developed receivers, often called radar detector detectors. As a response to the development of such a radar detector detector, radar detector manufacturers inserted countermeasures in their designs with the objective of avoiding their detection. Presented is the Georgia Institute of Technology Research Institute radar detector detector, which was developed by using advanced surveillance technology and handles the countermeasures of current radar detectors. This system was used to determine radar detector densities at three sites (rural two-lane road, four-lane state route, and six-lane Interstate) around the Atlanta, Georgia, metro area. The data collected were analyzed and compared against commonly used statistical probability distributions. Common distributions were fitted to the data, whenever appropriate. The determined radar densities by site and time of day were compared by using a nonparametric analysis of variance test. This analysis revealed that facility type has a significant impact on radar detector density, whereas time of day showed a significant effect for only one of the sites (state route).

Demographic Characterization of Anonymous Trace Travel Data

Research was undertaken to determine whether demographic characteristics of individual travelers could be derived from travel pattern information when no information about the individual was available. This question is relevant in the context of anonymously collected travel information, such as cell phone traces, when used for travel demand modeling. Determining the demographics of a traveler from such data could partially obviate the need for large-scale collection of travel survey data, depending on the purpose for which the data were to be used. This research complements methodologies used to identify activity stops, purposes, and mode types from raw trace data and presumes that such methods exist and are available. The paper documents the development of procedures for taking raw activity streams estimated from GPS trace data and converting these into activity travel pattern characteristics that are then combined with basic land use information and used to estimate various models of demographic characteristics. The work status, education level, age, and license possession of individuals and the presence of children in their households were all estimated successfully with substantial increases in performance versus null model expectations for both training and test data sets. The gender, household size, and number of vehicles proved more difficult to estimate, and performance was lower on the test data set; these aspects indicate overfitting in these models. Overall, the demographic models appear to have potential for characterizing anonymous data streams, which could extend the usability and applicability of such data sources to the travel demand context.