# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 1 Introduction
They used contour coefficients to evaluate the quality of clustering, enabling the rapid division of urban traffic neighborhoods [23]. With the rapid development of urban transportation systems, the travel structure of urban residents has undergone significant changes. For example, according to the results of the Fifth Resident Travel Survey in Beijing, the proportion of public transportation trips increased from 30% in 2005 to 50% in 2014, with the proportion of rail transit trips continuously increasing [24]. However, suppose that rail transit stations are located at the boundaries of traffic neighborhoods. In that case, actual travel will significantly differ from travel centered on the traffic neighborhood, leading to decreased prediction accuracy [25]. Furthermore, when the spatial partitioning system used for collecting and analyzing data is variable or arbitrary, the modifiable areal unit problem (MAUP) may arise [5; 26]. In response to the MAUP issue in the process of traffic neighborhood division, [PERSON] and colleagues [27] studied several spatial cells that can be used as TAZs, including statistical area levels, postal districts, state election districts,grid cells, and the Tyson polygon Melbourne integrated traffic model developed around the center of mass. The aforementioned clustering-based methods often use fuzzy clustering, k-means, and other clustering algorithms, generally based on administrative divisions or city university data for traffic neighborhood division. The former improves upon the administrative division method, emphasizing the feasibility of the method, which is not suitable for engineering practice; the latter builds models based on spatial and agglomeration features formed by urban big data, with relatively single indicators, that cannot fully capture the rationality of traffic neighborhood division. Most existing studies on traffic zone aggregation rely on clustering methodologies such as fuzzy clustering and k-means clustering algorithms. One category of methods uses travel information, socioeconomic attributes, and land-use characteristics within administrative divisions to construct features of travel similarity and spatial similarity for traffic zone division. Since these methods depend on traditional data and subdivide or merge traffic zones based on administrative boundaries, their primary goal is to demonstrate the feasibility of traffic zone division methods. However, such approaches are unsuitable for practical engineering applications. Another category of studies uses urban big data for traffic zone division. These methods build models using features such as spatial similarity, travel similarity, and aggregation derived from urban big data. However, the metrics used to describe traffic zones in such studies are relatively limited and are unable to fully capture the process of traffic zone division. Based on the aforementioned issues, in this study, we intend to use individual travel data and their spatiotemporal discrete mapping relationships, combined with the quantitative principles of traffic neighborhood division, to comprehensively investigate aspects such as travel similarity, homogeneity, statistical accuracy, and geographic information loss. We propose a spatial optimization model for traffic neighborhoods. A series of optimization schemes are divided automatically based on the objective function and constraints, and then, the optimal traffic neighborhood division scheme is selected according to the evaluation indices. The effectiveness of the method proposed in this paper is validated using bus travel data from downtown Beijing. Taking China as an example, many cities in China have adopted bus smart cards, and the bus line coverage and bus travel proportion of large cities are relatively high. The obtained passenger flow results reflect urban bus operations and residents' bus travel characteristics.