Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests (2014) / Chapter Skim
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1 Transportation professionals have been enamored with the potential uses of Global Positioning System (GPS) data ever since GPS became fully operational in 1995.
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2In 2011, the Transportation Research Board initiated a study to evaluate these GPS data sources and to provide guidance on the use of these sources by transportation planners, travel modelers, and travel survey practitioners; this study is NCHRP Project 8-89, "Applying GPS Data to Understand Travel Behavior." This chapter reports on a broad literature review conducted on GPS data sources, actual and potential uses of these data in the field of transportation, standards for GPS data collection and storage, and concerns about the various sources with respect to coverage, bias, accuracy, and privacy. To supplement this literature review, comprehensive questionnaires were sent to industry experts in the areas of travel surveys, travel behavior research, travel demand modeling, and traffic data provision.
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3 in the technology itself and the demand for more accurate travel data. Initially, GPS data collection was used mostly to provide corrections for trip rates obtained from traditional household travel surveys or to demonstrate the feasibility of doing so.
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4travel and activity diaries and associated retrieval methods. Efforts in this area have been conducted along two main lines: (1)
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5 was remarkable in its aggressiveness with respect to technology adoption by the planning agency and acceptance by diverse population groups within the region. Furthermore, the survey platform made it possible to conduct PR interviews immediately following GPS data downloads to the laptops in the participants' homes, with no opportunity for interviewer preprocessing or cleaning of the GPS trip data prior to participant review.
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6can become dominant in household travel surveys; these challenges include: 1. Market fragmentation, 2.
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7 The relevant travel demand model's data requirements have a large influence on the data elements collected in a household travel survey. Simple four-step TDMs may require basic household, person, vehicle, and trip-level information, whereas advanced activity-based models require more precise details about household, person, and vehicle characteristics, as well as expanded information about actual travel behavior, travel options, and costs.
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8study conducted in Cincinnati and an 8,800 household GPSbased travel survey conducted in Jerusalem.) The Cleveland survey collected detailed socio-demographic and travel data from 4,545 households, including a 30% subset who participated in a GPS-based prompted-recall interview designed to confirm trip details via CATI or CASI survey methods.
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9 during model development, the systematic use of these data sets for other purposes is just now beginning to grow. Perhaps most intriguing is the use of passive GPS data collected by survey participants to replace traditional diary-based reporting methods (as discussed in the previous section)
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10 to restore the individual disaggregate details that include not only the number of trips by purpose but also their sequence and timing based on a small subsample. This becomes especially important as scheduling models begin to account for intra-household interactions among household members, which require consistent schedules for all household members.
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11 lation models and dynamic traffic assignment (DTA) models need detailed speed and condition data for the specific model scope.
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12 from probe vehicles, GPS-based travel surveys, or consumer product origin–destination data sets. Trip ends are geocoded and assigned to a traffic analysis zone (TAZ)
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13 variability, thereby giving planners more options for improving emissions estimates. GPS data also provide measurement of time between engine starts.
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14 enabled device to assess user fees, either through onboard calculation or communication with a centralized management center (Pierce et al.
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15 ized areas with populations exceeding 200,000, go through a certification review every 4 years per 23 CFR § 450.334(b)
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16 surveys (U.S. Office of Management and Budget 2006)
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17 a serial protocol; parsing these messages yields information that can be used for navigation or logged for later processing. The GPS exchange format (GPX)
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18 up receiver positional reacquisition. Cold start events can be detected by comparing the end and start locations of adjoining trips and searching for distance gaps.
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19 SOW points follows the same logic, but in reverse order, while each point after a time difference of at least 80 s is marked as a potential EOG point. Travel Mode Identification Different approaches have been used to associate a travel mode with a sequence of GPS points.
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20 quite possible. Processing enhancements such as including trip chain logic, evaluating related geospatial data, and expanding to traveler-specific behaviors across multiple days may improve trip mode identification; however, research results in these areas were not formally published at the time the literature review was performed for this NCHRP study.
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21 added. Once each decision to extend the route with a link is made and accepted, this link will remain on the final selected route.
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22 (2008) with probabilistic multinomial logit models (MNL)
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23 • Smartphone use is not biased by race, but is biased toward those with a high income and those who are well-educated, urban/suburban, and under age 50. As transportation planners and researchers identify new methods to access travel data, statistics such as these can provide some insight into survey design and data usage limitations.
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24 crumbs created by everyday technologies. The transportation community has been slower than several other disciplines in finding applied roles for consumer data to support traditional transportation planning.
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25 logging logic, point resolution/frequency, post-processing algorithms, coverage, bias, quality, and privacy protection. One method of validation that can be implemented in these early travel behavior products is to compare results to other travel surveys like the NHTS.
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27 data from fleet vehicles, personal vehicles, roadside sensorbased systems, cell phone data, smartphone applications, RFID, and other sources. They have products that have been generated specifically for public agencies that are based on archived travel condition data.
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28 Bias: There is potential bias in traffic data given the heavy reliance on fleet vehicles. Bluetooth sensor data are biased toward drivers that are more likely to have Bluetooth-enabled devices.
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29 where the Federal Communication Commission required all cell phone manufacturers to have built-in location-detecting technology that would allow wireless network operators to provide latitude and longitude information on callers within 300 m to support emergency response. This requirement was part of the Wireless Communications and Public Safety Act of 1999 (Wolf 2000; Karim 2004)
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30 location, application and feature usage, network traffic data, service options you choose, mobile and device number, and other similar information may be used for billing purposes, to deliver and maintain products and services, or to help you with servicerelated issues or questions." "We may collect and process information about your actual location, like GPS signals sent by a mobile device. We may also use various technologies to determine location, such as sensor data from your device that may, for example, provide information on nearby Wi-Fi access points and cell towers." "We may share aggregated, non-personally identifiable information publicly and with our partners – like publishers, advertisers or connected sites." "This type of information may be aggregated or anonymized for business and marketing uses by us or by third parties." "We may also draw upon this Personal Information in order to adapt the Services of our community to your needs, to research the effectiveness of our network and Services, and to develop new tools for the community." "We receive and store any information you enter on our Service or provide to us in any other way.
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31 day, and a trip travel time. Not all individuals or vehicles have active Bluetooth transmitters.
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32 a transit vehicle to debit stored value from the card. Some systems require that the passenger tap out to exit a transit station.
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33 the resulting system to scale out to meet demand. Unfortunately, when data are segmented in this way, additional steps are required to conduct analysis over the entire data set.
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34 is considered an expert in his or her respective discipline/ category based on the research team's knowledge, available publications, and conference presentations. Representatives from different firms and research organizations were selected within each industry category to give adequate coverage and to minimize bias.
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35 along with a few key, representative quotes. The responses appear in Table 1-11, Table 1-12, and Table 1-13, respectively.
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Question Summary Relevant Quotes 5. GPS-based travel behavior details included in deliverables Travel surveys generally provide, at a minimum, the trip segments (by mode if using person-based devices)
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37 9. Limitations/ concerns about GPS Major concern exists regarding bias introduced through using GPS (i.e., respondents with privacy concerns regarding the collection method may select themselves out of the survey)
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38 Question Summary Relevant Quotes 1. Current use of technology Many have used GPS data collection devices that include in-vehicle, wearable GPS loggers, smartphones, PDAs, or in-tablet PCs.
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Table 1-12. (Continued)
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40 Question Summary Relevant Quotes 8. Advantages of GPS There are several clear advantages to GPS studies as opposed to traditional surveys, including: – Ability to collect all movements, precise times, locations, and routes; – Respondent burden reduction on data collection, with the individuals not needing to remember exact times and locations; longer reporting and ability to collect multiple days of travel to examine variability of travel; – Ability to capture route choice and speed; – Improved data quality: not reliant on self-report; and – Ability to look at activity time/space prisms and use in travel micro-simulation.
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41 Table 1-13. Summary table of transportation modeler responses.
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42 Question Summary Relevant Quotes 5. Other sources of origin– destination data Several modelers are exploring the use of cell phone and sensor-based systems for origin–destination travel times.
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43 Question Summary Relevant Quotes 10. Benefits of GPS for understanding travel behavior Increased accuracy of spatial and temporal travel details Completeness and minimization of underreporting Spatial and temporal resolution Reduced burden on the respondent to report addresses and timing for all trips/locations, thereby significantly speeding up the survey Attractive high-tech image of the survey, especially if the prompted-recall method applied is integrated with GIS (such as in the case of the Jerusalem HTS, where the recruitment rate was 70%–80%)
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