# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 5 Conclusions  
Based on a comprehensive review of studies related to transit card data and traffic zone division, this study utilizes transit card data within Beijing's Fourth Ring Road to construct a traffic zone aggregation model following relevant traffic zone division guidelines. Subsequently, the traffic zones within the study area are delineated, and the optimal traffic zone division scheme is selected, compared with other traffic zone schemes, and verified to reflect the actual travel behavior of residents. Our research findings and conclusions are as follows:  
(1) Preprocessing of transit card data and development of matching and integration methodology. In this study, we conducted preprocessing of transit card data and designed a matching and integration methodology for the data. Based on a detailed description of both transit card data and transit network GIS data, several rules were proposed to preprocess the data, ensuring data quality during application. To address mismatches between transit card data and transit network GIS data, field surveys were conducted to collect bus stop sign information, which was used to match the two datasets. Additionally, bus stops with identical codes on the same route, as well as stops with the same name but located on different routes or in close proximity, were integrated. This methodology is of significant importance for analyses based on transit card data. (2) Construction of traffic zone aggregation model based on travel similarity. Drawing on prior research on traffic zone division and guided by the principles of traffic zone division, a traffic zone aggregation objective function and constraints were developed to construct a traffic zone aggregation model. Using the area within Beijing's Fourth Ring Road as the study area, the model generated 21 traffic zone division schemes, with the number of traffic zones ranging from a minimum of 240 to a maximum of 340. (3) Selection of optimal traffic zone scheme based on TOPSIS comprehensive evaluation method and comparative analysis. The optimal traffic zone scheme was selected from the 21 schemes using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) comprehensive evaluation method, and it was compared with Beijing's traffic zone division plans from 2008 and 2010. The results demonstrate that the traffic zone division scheme based on the aggregation model offers certain advantages. The optimal traffic zone scheme includes 285 zones, with each zone's area generally falling within the range of [0.5 km\({}^{2}\), 2.5 km\({}^{2}\)]. The proportion of intrazonal travel in the optimal scheme remains below 15% overall and is superior to that in the 2008 and 2010 traffic zone division schemes. This indicates that the optimal scheme better reflects the actual travel behavior of residents. Based on Beijing bus card data and bus network GIS data, a traffic zone aggregation model was constructed in this study to divide the traffic zone in the fourth ring area, and the traffic zone division method was optimized to a certain extent. However, in order to place the model into practice, there are still some problems to be solved:  
(1) The method of traffic zone division used in this study was based on the principle of relevant zone division. Regarding the data used in this study, they are only considered in terms of the homogeneity, information loss, area, and shape of the traffic zone. The division of the traffic zone under other relevant principles needs to be discussed further. (2) Restricted by personal privacy and relevant laws and regulations, only the bus card data were considered in the model construction process. In the future, when passengers' travel information is more transparent, more comprehensive multimodal data should be considered. (3) With the development of society, the travel mode of residents will change considerably. In the future, we should more closely combine actual passenger flow forecasting and other projects, and update the plan according to the specific application scenario.