# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 1 Introduction  
In current urban rail transit planning, the basic unit for passenger flow demand analysis and prediction is the traffic analysis zone (TAZ) (also referred to as a traffic zone) [1]. TAZs are based on or conform to administrative divisions, and for areas that have been investigated using an origin-destination (OD) matrix, the original division of thezones is preserved as much as possible. The applicability of the TAZ scheme significantly affects the results of passenger demand analysis and forecasting [2,3], as well as traffic safety [4,5] and the scientific reliability and rationality of transportation planning [6,7]. An effective TAZ division scheme aids in the development of macroscopic traffic prediction models [1,7, 8, 9, 10, 11]. However, a unified standard and relevant regulations for TAZ division are absent both domestically and internationally. In urban planning practice, practical experience and the principle of traffic district division serve as the primary basis for the division of traffic districts [12]. Where possible, engineering practice has outlined a series of traffic district division principles, such as subdistricts based on land use and economic and social characteristics, to maintain consistency and satisfy the principle of homogeneity, and to avoid breaking the division of administrative subdivisions, thus enabling the use of existing statistical data on the administrative district [13]. However, these division principles are all qualitative guidelines and lack a solid quantitative foundation; thus, traffic district division plans based on these qualitative principles are often highly subjective. Moreover, when dividing TAZs, if the zones are too large, some geographical information and regional travel proportions may be lost, and if they are too small, it may decrease statistical accuracy. Additionally, when the spatial partitioning system used for collecting and analyzing data is variable or arbitrary, the modifiable areal unit problem (MAUP) can occur, which involves two main issues: the scale effect and the zoning effect. The scale effect denotes a phenomenon where the analysis results change as spatial data are aggregated, changing their granularity or cell size. The zoning effect denotes a phenomenon where the analysis results change based on different aggregation methods (i.e., zoning schemes) at the same level of granularity and aggregation. Some studies have tried using relevant surveys and data-driven approaches to extract additional feature information in order to classify traffic neighborhoods. For example, based on traditional traffic neighborhoods that only convey geographical attributes, [PERSON] and colleagues applied deep neural networks to extract high-dimensional data such as spatial geographic attributes and vehicle trip information. They identified the most relevant features of trip origins and destinations through statistical measurements of relevance, constructing trip-based traffic neighborhoods while integrating various kinds of auxiliary information [14]. [PERSON] and colleagues, building on traditional traffic neighborhood delineation, derived potential land use and spatiotemporal mobility features based on big data and enhanced the traditional model using a multivariate data-driven approach. They integrated vehicle data with traffic neighborhoods to define regional configurations of origins and destinations for demand forecasting, aiming to minimize trips within the region [15]. [PERSON] and colleagues introduced data such as license plate information, transforming an intersection node OD matrix into rules for a traffic neighborhood OD matrix. They demonstrated the effectiveness of the traffic neighborhood transformation rules through two indicators: traffic allocation and the detection of traffic deviation [16]. [PERSON] and colleagues introduced a spatial analysis method that combines dynamic traffic state characteristics with static road network features to define tourist traffic neighborhoods [17].