# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 1 Introduction
Furthermore, [PERSON] and colleagues, when studying vehicle ownership behavior, examined the spatial correlation between traffic neighborhoods. Based on travel survey data from Changchun residents, they calculated the model parameters, confirming that the spatial correlation characteristics of traffic neighborhoods, such as residential density, land use mix, intersection density, and public transportation stop density, all significantly negatively affect household vehicle ownership [18]. The aforementioned studies introduce additional survey data based on traditional traffic neighborhood division. Although these studies have achieved good results for specific areas, on the one hand, additional survey data are not easily obtained on a large scale, and on the other hand, the increased data requirements of these methods reduce their versatility, making them difficult to apply to traffic neighborhood division research and other regions. With regard to the forms of traffic neighborhood division, [PERSON] and colleagues explored the application of fine-grid management methods in transportation planning. Based on a case study in Chuanhui, China, they proposed a data-driven fine-grid division method for determining TAZs. The results showed that the fine-grid division method for determining TAZs based on quadrilateral grids can achieve lower levels of predictive value bias and variable correlation bias when the number of TAZs is large [19]. In addition, [PERSON] and colleagues examined the impacts of different regular geometric shapes on traffic demand issues. They divided TAZs into squares, rhombuses, triangles, and hexagons, then conducted a detailed evaluation of the changes in various metrics between the traditional TAZ division method and the division method with different geometries. The results confirmed the effectiveness of the regular division method [1]. [PERSON] and colleagues used the smallest available census units to divide traffic neighborhoods and employed a geographically weighted negative binomial regression model for a comparative analysis of macro-crash prediction. The results indicated that some features of the built environment are significant predictors of traffic crashes, and the new division method can better capture the differences between these features and traffic safety in different spaces [20]. In addition to considering the static spatial constraints of traffic neighborhoods, some studies have considered the dynamic development of traffic neighborhoods. Considering that regional development may lead to the reclassification of existing transportation subdivisions, [PERSON] and colleagues introduced six methods of assigning travel data to rezoned transportation subdivisions. Illustrated using 2001 and 2016 travel data from Toronto, Canada, the results confirmed the significant impact of different traffic neighborhood division methods on transportation policy [21]. The aforementioned methods, regarding the form of the dynamic and static subdivision of traffic neighborhoods, have partly confirmed the impact of traffic neighborhood shape on the mapping of spatial relationships. However, no further research has been conducted on the division principles, limiting the improvement of the division results of these studies. Regarding the division principles, a series of works have also been developed to improve the clustering methods used in traditional traffic neighborhood division. For example, to provide features more in line with passengers' travel needs, [PERSON] and colleagues proposed traffic neighborhoods based on the division of bus passenger corridors. They divided the corridor into two levels: the direct impact area and indirect impact area. Through big data, they selected more suitable clustering indices for traffic neighborhood division and introduced clustering factors to improve the accuracy deficit of traditional clustering methods [22]. [PERSON] and colleagues took the locations of smart-card ports on urban roads as the clustering object and implemented a new DBSCAN clustering algorithm.