# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 1 Introduction  
Urban transportation systems face mounting pressure due to rapid urbanization and evolving mobility demands. In many modern cities, worsening traffic congestion underscores the urgent need for strategic interventions to improve transport networks and facilitate smoother daily commutes. Effective planning is essential to achieving these goals, as poorly designed interventions can result in inefficiencies, public dissatisfaction, and adverse environmental impacts. To support this process, urban and transport planners employ digital modeling tools that enhance efficiency and provide a data-driven foundation for decision-making. These tools allow planners to simulate various transportation scenarios, assess the potential impact of different policies, and optimize infrastructure improvements based on real-world conditions. By integrating scientific methodologies and well-defined criteria, digital modeling ensures that interventions are both effective and sustainable. A crucial component of digital modeling in transportation planning is the use of activity-travel patterns, which capture individual or household trips by identifying the starting and ending points of travel within an urban area. These data provide valuable insights into commuter behavior, peak traffic flows, and demand for public transportation. By incorporating travel-activity patterns into digital models, planners can design targeted solutions that address congestion peaks, improve transit accessibility, and createmore efficient, well-connected transportation networks that meet the needs of growing urban populations. Except for a few metropolitan areas around the world, usually located in Europe [1] or the United States [2], most cities worldwide are currently attempting to establish methods and technical procedures for systematically collecting and updating activity-travel patterns data. However, these efforts are costly, requiring substantial personnel resources and person-month hours to effectively collect and update the data. While technology has reduced costs for effectively capturing activity-travel patterns [3], the excessive data collection needs lead researchers and practitioners toward synthetic data generation methodologies. In synthetic generation methodologies, a population is synthesized based on assumptions and statistical models, with the goal of generating activity-travel patterns and supplementing an existing dataset. In all cases, the generation of synthetic activity-travel patterns requires a pre-existing set of datasets like a spatial zoning system, census data, income information, household travel surveys, address databases, and service and facilities census data [4]. The method proposed in this article demonstrates its potential as an effective alternative for achieving higher accuracy in the generation of synthetic activity-travel patterns in urban environments. By adopting an activity-based approach, the study analyzes an existing travel-activity dataset (OD matrix) and produces additional synthetic patterns that more closely reflect real-world behavior compared to existing methods found in the literature or current practice. A key contribution of the approach is the use and comparison of Cox-based models, including standard Cox models, Cox models with latent classes, and Cox model-based machine learning models, which enable more accurate estimation of activity durations within the dataset. This enhancement can be viewed as an extension of the study by [PERSON] et al. [5], who used a simple Cox model in a related application. In our work, we also deal with incomplete or illogical data, and we aim to predict the closest possible durations based on individual characteristics, thereby enriching the database and yielding more reliable results. It is also important to mention that the current study focuses on the case of Athens, Greece. According to the 2023 Traffic Index Ranking by TomTom [6], Athens ranks 31 st among the cities with the worst traffic in the world (measuring the average time required for every 10 km).