On New Measures for Detection of Data Quality Risks in Mobility Panel Surveys

Abstract

Multiday and multiperiod panel surveys are state-of-the-art methods to assess changes in individual travel behavior. Though important for transport planners, these surveys are rather time-consuming for participants and therefore might lead to erroneous and biased mobility data. Variability in the data quality significantly affects statistical analyses of mobility figures as well as common microscopic travel demand models that use the mobility data as the basis for generating activity plans. Supplementary to the well-known approach of weighting biases in key figures of mobility, this paper focuses on methods for detecting data quality differences between individual travel diaries. These quality measures address aspects of motivation loss at different stages of the survey. A classification approach based on these new quality measures helps to detect erroneous data and possible dropouts. The results might help reduce dropouts in general by addressing the potential dropouts individually in advance and boosting their motivation. Quality measures are tested with recent data from the German Mobility Panel. For participants older than 60 years of age, the quality measures show good classification results in regard to accuracy, but for participants younger than 35 years of age the quality measures are not effectual in identifying dropouts. Such an individual approach combined with the partial inspection and correction of travel diaries may be useful for microscopic travel demand modeling based on external activity chains.

Comparing GPS-Based Community Mobility Measures with Self-report Assessments in Older Adults with Parkinson’s Disease

“…Such retrospective measures are subject to random (eg, memory) error, as well as recall bias due to social desirability and floor effects (13). Alternatively, daily diary entries may reduce memory error but fail to account for changes in mobility across time (14). Since no "gold standard" measure of CM currently exists, self-reporting remains the most common approach to CM assessment (15,16).…”

Modeling travel demand over a period of one week: The mobiTopp model

“…Central input of the population synthesis is the data of a household travel survey. Preferably, a survey for the planning area is used; however nationwide surveys like the German Mobility Panel (Wirtz et al, 2013) can be used as replacement. The population of each zone is generated by repeated random draws of households and the corresponding persons from the survey data.…”

“…The car ownership model consists of a car segment model and a car engine model. The car segment model is a multinomial logit model estimated on the data of the German Mobility Panel (Wirtz et al, 2013). The car engine model combines the ideas of propensity to own an electric car and suitability of an electric car.…”

Measuring Stability of Mode Choice Behavior

“…Patterns 14 to 19 illustrate stability in the sense of repetition with few modes used (Patterns 14 to 17) and with many modes used (Patterns 18 and 19). Pattern 20 is a pattern not showing any stability regarding the three perspectives of stability considered here.For an analysis based on empirical data, the German Mobility Panel is used(16,17). The German Mobility Panel is an annual household travel survey over seven consecutive days conducted in Germany since 1994.…”

“…They report every trip with its attributes day of the week, start time, end time, distance traveled, trip purpose, and mode. The data are thoroughly checked for quality by using semiautomated procedures and manual inspection(17). The sample size consists of around 1,800 persons in 1,000 households for each year.…”