Cell Phone Location Data for Travel Behavior Analysis (2018) / Chapter Skim
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Pages 110-132
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110 9.1 Roadmap to the Chapter Chapters 4 through 8 highlighted three-way comparisons between survey data, traditional models, and call detail record (CDR) -derived estimates to help bridge the gap between research and practice.
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Guidelines for Practitioners 111 9.2 Administrative Considerations The specification, purchase, and licensing of CDR data is a complex transaction with cost and legal implications. The terms of payment need to be considered in the financial planning undertaken by public agencies.
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112 Cell Phone Location Data for Travel Behavior Analysis If the public agency does not have the signatory authority to enter into legal agreements, then legal staff of the signatory agency will need to enter into a legally binding agreement. An example of an MPO administered by a separate legal entity is the Greater Buffalo Niagara Regional Transportation Council, which is administered by the Niagara Frontier Transportation Authority.
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Guidelines for Practitioners 113 The design and availability of this document during the preliminary stages of project development will ensure that any privacy issues are dealt with in a comprehensive and transparent manner. 9.3 Data Considerations Chapters 6 through 8 discuss how CDR data and the O-D trip tables derived from them are similar to and different from travel survey results and model outputs.
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114 Cell Phone Location Data for Travel Behavior Analysis • Contextual information available in each data source to – Differentiate between commercial and passenger travel, – Identify activities and travel purposes, and – Use socioeconomic information to expand the sample. The data elements shown in Tables 9-1 and 9-2 can serve as a data checklist to help agency staff identify the specific features of CDR data, their ability to provide the required information, and their relative value compared with traditional surveys, GPS logger data, smartphone surveys, and Bluetooth data.
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Guidelines for Practitioners 115 9.3.1 Raw CDR Data This report benefited from access to the raw, disaggregate, and anonymized 2010 CDR data that were used for research purposes. However, current thinking and legal privacy considerations make it unlikely that raw cell phone CDR data will be available to practitioners in the future.
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116 Cell Phone Location Data for Travel Behavior Analysis the model estimation and discovery process is anchored in the behavioral paradigm approach but is not feasible with CDR data. Agency staff will be able to specify their customized data requirements to CDR data vendors.
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Guidelines for Practitioners 117 • The population of cell phone users in the CDR sample and its representativeness of the population at large; • The incidence of different market segments in the sample of cell phone users and the method used to expand the sample; and • The methods used to detect home and work locations, stops, and activities that are then used to infer daily travel by purpose and time of day. Agency staff will develop more confidence in a vendor's analyses and products if they are convinced that the processed CDR data generate results that are broadly consistent and comparable to the outcomes of traditional data sources and modeling methods.
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118 Cell Phone Location Data for Travel Behavior Analysis Agencies can specify to the vendor the desired level of aggregation on the basis of CDR spatial properties, activity centers in the region, and the existence of other sources of data at comparable levels of aggregation. Given that vendors work with point-level data, they can provide customized aggregations of zonal data based on a model's TAZs, Census geographic boundaries, or an agency's custom-built layers.
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Guidelines for Practitioners 119 In contrast, CDR data are limited in the detail they offer for activities and purposes. As described in Chapters 5 and 6, the analysis of CDR data relies on heuristic rules and algorithms to infer home and work locations.
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120 Cell Phone Location Data for Travel Behavior Analysis to the web. Smartphone devices now include active and passive data transmissions, such as podcast or music downloads, e-mail updates, data on maps and directions, and the use of various apps.
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Guidelines for Practitioners 121 different ways without any major limitation on the number of time periods or the duration of each time period. The location and timing of the inferred CDR trip ends are processed, aggregated, and expanded by the vendor to preserve the confidentiality of individual subscribers.
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122 Cell Phone Location Data for Travel Behavior Analysis across market segments. Therefore, the population of cell phone subscribers within the zone identified as one's home location is used as a simpler measure of expected CDR use and travel activity.
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Guidelines for Practitioners 123 Type of Model CDR Data Personal GPS Derived Data Smartphone Survey Custom Bluetooth Data Estimation of regional models No socioeconomic data. No detailed activity, purpose, mode and tour data.
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124 Cell Phone Location Data for Travel Behavior Analysis that can be obtained from GPS loggers, smartphone apps, and Bluetooth devices. The detailed discussion of modeling options can help agency staff prioritize their plans to use CDR and other nontraditional data to support, augment, or replace one or more model components.
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Guidelines for Practitioners 125 CDR-derived travel and trip table estimates provide an alternative source of data for validating individual model components of trip generation and distribution as well as estimates of travel by time of day. However, these CDR-derived estimates are not available by market segment such as income, auto availability, or household size.
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126 Cell Phone Location Data for Travel Behavior Analysis • In addition to the CDR data, intermediate year traffic counts, transit ridership data, and socioeconomic data can be used to paint a complete picture of the updated base year travel patterns. 9.4.4 Corridor, Traffic Impact, and Microsimulation Studies CDR data can support corridor-level studies in cases of large, long, and well-defined corridors or parts of an urban area to allow meaningful estimates of travel within, to, and from the corridor or area boundaries.
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Guidelines for Practitioners 127 Agency staff need to specify the CDR data purchase, especially with respect to zone definition, and provide input to sample expansion: • The zone system needs to be outlined carefully. When specifying the special generator, agency staff need to account for the spatial inaccuracy of the CDR data and choose an appropriate buffer around the generator.
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128 Cell Phone Location Data for Travel Behavior Analysis visitor models, CDR data can provide valuable information for modeling long-distance travel by a region's residents. Agencies typically model long-distance travel by using broad classification schemes to identify differences in total trip making, destinations, and modes used within their population.
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Guidelines for Practitioners 129 The cost of cell phone service has also been decreasing over time, especially for text and data, with packages now often including unlimited data transmissions. Cell phones are becoming more of a necessity for many households, including those in lower-income market segments.
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130 Cell Phone Location Data for Travel Behavior Analysis inferring activities and trips by purpose, the increased volume and quality of CDR data make it easier to address data gaps. 9.5.2 Status of Relevant Research in Locational Data The growing penetration of cell phones among different age cohorts, the increased use of cell phones for Internet data access, and the improvements in the technology embedded in smartphones provide opportunities to leverage locational data for use by transportation planners, modelers, and decision makers.
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Guidelines for Practitioners 131 individual socioeconomic information used in travel demand modeling. It builds a tour-based model with passive data by using a person-based discrete event simulation framework.
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132 Cell Phone Location Data for Travel Behavior Analysis models of travel behavior and the computer scientists and physicists who use big data to address human mobility patterns (Chen et al.
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