# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 2 The Related Literature and Problem Statement
### The Related Literature  
Due to the importance of mobility insights, a large body of the literature and practical mobility studies is available today. In contrast to the technical transportation perspective, which focuses and builds on origin-destination matrices as a proxy variable for mobility demand, mobility studies, which are rooted in the social sciences, require information on the reasons for transport demand. Hence, the assessment of human needs that induce activity patterns, location choices, and decision making adds to the bigger picture of human mobility. The mobility data sets available as a foundation for mobility studies can be divided into two groups based on the type of data sources: trace data, providing the locations of individual entities, and survey data, providing the mobility schedules of individual entities [8; 9; 10]. Trace data, when mined from existing data like call detail records [11; 12] or social media data [13; 14], have large samples sizes (\(n\geq 10^{5}\)), but oftentimes, no information regarding activities or persons emitting the traces are available [8; 9]. In cases in which trace data can be attributed to identifiable persons, privacy preservation is a major challenge [15]. Aside from mining, trace data can be purposefully created using global positioning system (GPS) trackers. This enables capturing sociodemographic data and activity purposes, yet the sample sizes are small due to the high incurred cost (commonly \(n\leq 10^{3}\)) [9]. In contrast to trace data, the survey data results are from censuses or travel surveys. A fundamental element of survey data is sociodemographic information. Censuses provide static data about residential and employment locations as well as high-level mobility metrics, such as the average commuting distance [8; 9]. Travel surveys contain detailed information about the respondents' mobility, especially with regard to trip purpose, mode of transport, time, and distance [16; 17; 18]. Compared with trace data, survey data have the advantage of providing sociodemographic information as well as information about the conducted activities. Their disadvantages are the high incurred effort, a coarse geospatial resolution to maintain anonymity, and dependence on the reporting accuracy on human memory [8; 9]. The high effort additionally leads to them being infrequent and reflecting transportation changes with a delay [11].  
Aside from mobility data collection through, for example, tracing and surveying, the identification of movement and behavioral patterns in raw data and the subsequent abstraction of such patterns allow for mobility forecasting and extrapolation. Partial models of mobility patterns include, among others, driving willingness models [19; 20; 21; 22; 23], location selection models [5; 12; 14; 24; 25; 26], detour behavior models [27; 28; 29; 30; 31; 32; 33], and models for activity scheduling [18; 34; 35; 36; 37; 38; 39; 40]. Integrated models, aimed at modeling the overall movements of larger populations, are clustered into trip-based and activity-based mobility models. These are reviewed in the following sections.