# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 5 Discussion  
Considering the results of our study, we can assert that the missing durations are filled with greater accuracy through the Cox-based machine learning model and the Cox frailty model with 2 latent classes, as compared to a simple Cox model, and significantly better than assuming a normal or Weibull distribution (as it is done in the standard scenario by the ABM model). By utilizing our proposed Cox-based workflow, one may notice that there are no major differences in the daily movement schedules by purpose of movement and use of trans  
Figure 8: A comparative analysis of _Car Mode_ trip density before and after the intervention: Areas shaded in light blue indicate lower traffic density, whereas regions highlighted in magenta correspond to periods of higher traffic concentration (sourced from PAM). Figure 7: A comparative analysis of _Market Trips_ density before and after the intervention: Areas shaded in light blue indicate lower traffic density, whereas regions highlighted in magenta correspond to periods of higher traffic concentration (sourced from PAM). portation modes with all the different approaches followed. However, significant changes can be distinguished, mainly toward the end of the day, as the majority of interventions concern durations in activity chains where the \"return home at the end of the day\" was not mentioned. In that regard, the authors have noticed considerable differentiations in the synthetic data that are generated with the proposed workflow. Some more specific conclusions regarding the traffic behavior of the travelers in the generated datasets are:  
1. In shopping-related trips, an increase in duration range is observed, with several trips lasting 2-3 h, which is expected. Higher activity is noted in the afternoon, particularly between 18:00 and 19:30. 2. Work-related trips show greater variability in duration and end times, unlike the unprocessed data, where trip endings concentrate in the afternoon. 3. Other and service-related trips display a previously absent duration range, with more trips lasting 1.5-3 h. While most occur in the afternoon, service-related trips show increased morning activity. 4. Educational trips remain largely unchanged, likely due to minimal intervention. Their start and end times, as well as durations, follow a pattern that can be considered more irregular than other types of purpose trips. 5. Increased percentage of use of cars and trains/metro during peak afternoon hours. 6. Slightly reduced percentage of use of motorcycles and buses in the afternoon. 7. Higher usage percentage of e-scooters and bicycles at night. 8. Greater walking activity in the afternoon. 9. Rise in both percentage and absolute number of trips during peak hours (17:00-20:00, 22:00-00:00), with a relative decline at 16:00 and 20:00. 10. The high-income category aligns with typical working hours, showing peak trip percentages in both morning and afternoon. The low-income category exhibits a midday peak, absent in other groups. 11. Fewer trips are associated with the high-income category, while the remaining groups follow similar patterns. 12. Older age groups correspond to fewer trips, while younger groups travel more in the synthetic data produced by the updated workflow. Some limitations of the proposed research are discussed next. It is very important to mention that for this study, a small survey conducted by NTUA was used, comprising 513 observations and different daily activity chains. By adding complexity to the model with the two latent classes, which essentially represent two variables of the models for unobservable patterns affecting durations (hidden groups), it becomes clear that the more data the model is provided with for training, the more reliable results it will produce. Specifically, since all models are created separately for each travel purpose, this does reduce complexity, but at the same time, it significantly reduces the data, making it difficult to uncover these hidden patterns.