# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 2 Literature Review
### Modeling Travel Activity Patterns  
The second important aspect examined and studied in the literature review concerns the different modeling approaches. The challenging task of modeling, and what we strive for, is predicting mobility patterns. Regarding the creation of activity patterns, prediction becomes even more difficult since travelers' behavior is complex and influenced by many different variables. Different modeling methods provide flexibility in research, as they can better adapt depending on the type of data and the specific case, allowing for a more accurate representation of reality. Traditionally, the four-step model (Trip Generation-Trip Distribution-Mode Choice-Trip Assignment) has been used. As analyzed by [PERSON] [14] for the city of Dhaka, it becomes clear that the assumptions of the trip generation and the trip distribution steps of the four-step model are more simplistic, possibly relying on growth factors for prediction. The activity patterns of travelers are difficult to estimate because the available data lacks the level of detail that would be necessary. With technological advancements, newer modeling methods have been applied that can incorporate more data and generate multidimensional conclusions. One such approach is agent-based modeling. These models focus on creating a specific environment with predefined rules, within which the behavior of agents (or individuals) is studied. In most cases, a population is synthesized based on demographic and socioeconomic data obtained from census data and surveys, aiming to represent the real population as accurately as possible. The closer the agents reflect the real population, the better the model and the decisions derived from it. Several research efforts were made on this topic, each focusing on different aspects of analysis. For example, [PERSON] and [PERSON] [4] in Paris, focus on controlling travel modes to develop activity-travel patterns. [PERSON] et al. [15] perform a study on the city of Berlin and examine mode choice along with the temporal and spatial distribution of trips based on points of interest, providing insights into how different locations influence travel behavior. Similarly, [PERSON] [16] in Stockholm, focuses on activity durations and mode choices, shedding light on how individuals allocate time across different activities and transportation options. [PERSON] [17] applied Bayesian Networks to enhance the realism of population synthesis for agent-based modeling. Modeling activity-travel patterns has also been essential for simulation purposes and solving problems such as understanding how each area is utilized (e.g., residential neighborhood, industrial zone, etc.). Based on this logic, Land Use and Transportation Interaction (LUTI) models have been developed. These models capture the dynamic interaction between land use and transportation, considering how land-use changes affect transport demand and how transportation influences the development and distribution of land use. Moreover, they allow the simulation of different policy and planning scenarios, such as the construction of new transport infrastructure or changes in zoning regulations. Such studies have been analyzed by [PERSON] et al. [18] and [PERSON] et al. [19], attempting to quantify transport accessibility based on land use using different parameters and objectives in each study. Furthermore, in the research by [PERSON] et al. [20] on Bogota, the analysis aims to create travel activity patterns, specifically predicting Home-Work-Home (HWH) and Home-Other-Home (HOH) tours. Or in other studies, such as the one by [PERSON] et al. [21], the authors demonstrate how integrating autonomous vehicles and park-and-ride strategies in MATSim simulations can influence daily activity plans and mobility patterns, further underscoring the flexibility and potential of agent-based modeling frameworks in transport planning. Another crucial approach is machine learning.