Cell Phone Location Data for Travel Behavior Analysis (2018) / Chapter Skim
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Pages 78-89
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78 7.1 Roadmap to the Chapter Chapter 5 discussed how respondents' trajectories were analyzed and what assumptions were made to infer respondents' stay locations from triangulated call detail record (CDR) data.
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Trips by Purpose and Time of Day 79 journey-to-work estimates. These comparisons highlight the promise and the challenge of using and understanding CDR data for various planning and modeling purposes.
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80 Cell Phone Location Data for Travel Behavior Analysis 7.3 Modeling Departure Time This section describes in detail how time of day was incorporated into the CDR data analysis. Daily trips were estimated from filtered users by analyzing consecutive observations at different stay points during a given time window.
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Trips by Purpose and Time of Day 81 hourly distributions for weekdays, weekends, and each of the three trip purposes. The patterns of weekday observations in the cell phone CDR data are illustrated in Figure 7-1.
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82 Cell Phone Location Data for Travel Behavior Analysis Assigning a purpose and departure time to every stay inferred from the cell phone sample allows O-D trips made by a user to be constructed for any given day. As is the case with household survey data, the O-D trips were allocated to a traffic analysis zone or district for analysis purposes.
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Trips by Purpose and Time of Day 83 Given the much larger size of the cell phone user sample versus a traditional survey sample, the CDR method also has the advantage of smaller expansion weights than a traditional survey. In Boston, the majority of these expansion factors were found to be less than 10, although expansion factors can be larger in places with a lower level of cell phone market penetration.
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84 Cell Phone Location Data for Travel Behavior Analysis is a close match between the cell phone–derived patterns and each of the three surveys. The four lines track closely with two distinct a.m.
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Trips by Purpose and Time of Day 85 travel survey data for the Boston region. In general, the patterns of activity stay duration for home, work, and "other" locations at 1-hour intervals for the two data sets were comparable.
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86 Cell Phone Location Data for Travel Behavior Analysis Table 7-1 summarizes estimates of total daily trips by purpose. CDR trip estimates are compared with the Massachusetts Travel Survey (MTS)
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Trips by Purpose and Time of Day 87 Finally, the correlation coefficients in Table 7-1 suggest that aggregating trips from the tract to the town level yields the greatest improvement for HBW trips. This may reflect the role of tract size, especially in the smaller zones in downtown Boston, where many of the morning commute trips end.
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88 Cell Phone Location Data for Travel Behavior Analysis Table 7-3 summarizes the comparison of CDR and 2006–2010 CTPP commuting flows: • The estimates of the total number of work trips are comparable. • The percentages of intertract and intertown flows are similar, which suggests a consistency at the spatial level.
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Trips by Purpose and Time of Day 89 to aggregate CDR data to commonly used geographic units such as the traffic analysis district, which was developed following the 2010 Census in support of the CTPP. 7.9 Summary This chapter outlines the methods used to analyze CDR data to produce travel estimates that were compared with traditional surveys and can be further compared with traditional model outputs.
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