# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 1 Introduction  
Many applications in the area of transportation system design rely on accurate modeling of human mobility demand. So-called mobility plans constitute a prominent representation of such demand, which is needed to design new mobility systems for growing and modern cities using, for example, state-of-the-art traffic simulators such as Multi-Agent Transport Simulation (MATSim) [1] or Simulation of Urban Mobility (SUMO) [2]. A set of mobility plans includes information on the coordinates of origins and destinations, as well as departure and arrival times for every individual trip occurring within a population, timeframe, and spatial environment. An exemplary excerpt of one individual's mobility plan is illustrated in Figure 1.  
Synthesizing realistic mobility plans by means of abstracting human travel behavior within demand models has been the subject of many prior studies [3, 4, 5]. Distinguished by their levels of aggregation, activity-based and trip-based models are the two prominent types of demand models known today. Activity-based models produce mobility plans in which all movements are coherent within the frame of a microscopic entity, such as an individual person or car. In activity-based models, travel demand originates from the desire of individuals to participate in activities and is the result of trips taken to reach these activities [4]. Elaborate models of this type generate mobility plans that contain plausible mobility patterns and activity sequences on a microscopic level in addition to realistic, macroscopic traffic demand with regard to, for example, mileage, times, and locations. Activity-based models are based on simulating human behavior to explain and forecast traffic reactions to transportation policy or system changes, but this simultaneously makes them highly complex and expensive to implement, thus preventing established use [6]. Asa result, activity-based demand models are scarcely available. In contrast, trip-based approaches are significantly more common and simpler to implement but fail to be realistic on a microscopic level. They realistically model traffic demand macroscopically by simulating aggregated traffic flows on network links [7]. The purpose of this work is to develop and validate a data-driven method for generating activity-based mobility plans that are realistic and coherent on an individual level without the complexity introduced by the behavioral nature of activity-based models. This places it between trip-based and activity-based demand models, as illustrated in Figure 2.  
To this end, the output of an existing trip-based model is combined with data from a general mobility survey to arrive at a model that is capable of producing activity-based mobility plans that are microscopically coherent without losing macroscopic accuracy. This is achieved by merging the macroscopic characteristics of the general mobility survey with the microscopic and location-specific traits of the trip-based model to remodel the statistical properties of the input data without the explicit consideration of dynamic system behaviors that initially led to the observations within the input data sets. Our model is thus empirical and does not provide any basis for extrapolation but is systematically easier to implement than activity-based demand models. The presented model is designed and tested using existing data and models for the city of Munich. Neverthe  
Figure 1: Exemplary mobility plan of all trips of an individual on one day. The last activity of the preceding day provides the work trip’s origin. Figure 2: The presented methodology is located between trip-based and activity-based demand models. applicable to any situation in which both a trip-based model and a general mobility survey for the study area are at hand, and building a full activity-based demand model is infeasible or inefficient. To reduce application complexity, the model is confined to a single mode of transportation, a structurally homogeneous spatial environment, and weekdays.