# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 2 Literature Review  
### Data Sources  
Historically, the most popular data source for creating Origin-Destination (OD) passenger demand matrices has been household travel surveys. In these surveys, respondents are asked to provide detailed information about their travel activities over a typical period (e.g., a day, a week). This method can offer very high accuracy as the questions can be highly detailed, and the sample size can be substantial. Another similar data source still used today is the national census data. Both sources are highly reliable, but their main drawback is that they are conducted infrequently due to the significant time, workforce, and financial resources required. Nevertheless, they remain essential as they serve as a fundamental base upon which new data are added using more modern data collection methods.  
In the era where technology has become an integral part of our daily lives, data availability is continuously expanding. According to Canalys [7], approximately 82% of people in Europe use smartphones, while according to PewResearch [8], 3 out of 4 users have their Global Positioning System (GPS) enabled, as it is required for the functionality of certain applications. Similar percentages are observed in the United States (U.S.), while in developing countries, the figures are lower but still statistically significant. These location-based data are collected and can be exploited for research purposes by various applications. The key difference compared to traditional data sources is that these data are available in real-time, offering rich insights into the movement of individuals, peak hours, and road congestion.  
Another type of data that originates from smartphones is social media data. According to DataReportal [9], approximately 80% of residents in various regions are social media users, with approximately 30% of them sharing their location via check-ins. This creates a vast new data source that can be leveraged, as it could provide even more detailed insights depending on the declared location and the purpose of travel. In recent years, many studies have conducted traffic analysis using social media data (e.g., Gkiotsalitis and Stathopoulos [10]).  
Big data also include information collected from mobile networks. This type of data has been used by [PERSON] et al. [11] in an effort to simulate the population composition and subsequently create an agent-based model (ABM), as well as by [PERSON] et al. [12]. Locations are collected through mobile phone towers, which exist in sufficient numbers to support such research. With the appropriate assumptions, this type of data is also highly useful for deriving significant conclusions regarding transport systems and traveler behavior. Another type of data that can be utilized comes from smart cards used in public transportation, as demonstrated in the study by [PERSON] and [PERSON] [13], where they attempted to create an OD matrix. With appropriate assumptions, these data can provide valuable insights into the characteristics of travel patterns. Lastly, data are also obtained from road sensors, which are devices installed at fixed locations on the road surface that can detect various travel characteristics, such as vehicle type and speed. These sensors contribute to more accurate assessments of traffic flow.