# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 4 Case Validation and Analysis
Traffic districts with an area larger than 2.5 km\({}^{2}\) resulted from the re-aggregation of grids that had not been previously aggregated, which caused the areas of some traffic districts to exceed the above range. Traffic districts with an area smaller than or equal to 0.5 km\({}^{2}\) occurred because during the aggregation process, grids adjacent to already aggregated areas were merged into other traffic districts, forcing the aggregation process to terminate prematurely. Based on the final traffic district division scheme, the bus station OD matrix, and the travel volume corresponding to each grid, the travel proportion within the optimal traffic district division scheme was determined and is shown in Figure 10a. The analysis indicates that more than 90% of the traffic districts have a travel proportion of less than 15%, ensuring minimal loss of travel information during the division of traffic districts. To further verify the reliability and effectiveness of the TAZ division method proposed in this study, our scheme was compared with the Beijing 2008 and 2010 TAZs. Figure 11 shows the boundaries of townships in Beijing and the boundaries of TAZs in 2008 and 2010. By comparing the three types of boundaries, it was found that the TAZs in 2008 were mostly subdivided on the basis of administrative divisions, while those in 2010 were subdivided based on the 2008 TAZs. Based on the macroscopic evaluation indexes of traffic subdivisions, systematic calculations were carried out for each subdistrict, and the calculation results are shown in Table 3. The traffic cell program proposed in this study further reduces the proportion of trips in the average area to avoid the loss of travel information; the coefficient of variation of travel density is lower than the corresponding value of the traffic cell program in 2008 and 2010 to ensure the homogeneity of traffic cell trips. It can be seen that in 2008 and 2010, the traffic district meets the traffic district boundaries and administrative boundaries, making it compatible with the principle of easy access to socioeconomic, demographic, and employment data in traffic planning and traffic modeling, but it is difficult to ensure the accuracy of the traffic district program based on the relevant predictive analysis. \begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} **Number of TAZ** \\ **Division Schemes** \\ \end{tabular} & **Closeness \(C_{i}\)** \\ \hline
285 & 0.6624 & 260 & 0.5945 \\
305 & 0.6598 & 325 & 0.5938 \\
300 & 0.6535 & 330 & 0.5907 \\
280 & 0.6420 & 255 & 0.5975 \\
270 & 0.6409 & 320 & 0.5819 \\
310 & 0.6369 & 335 & 0.5649 \\
295 & 0.6327 & 250 & 0.5607 \\
315 & 0.6251 & 245 & 0.5477 \\
275 & 0.6222 & 240 & 0.5252 \\
265 & 0.6053 & 340 & 0.4748 \\
290 & 0.5989 & - & - \\ \hline \end{tabular}
\end{table} 
Table 2: The proximity of TAZ schemes to the optimal scheme. Figure 10: Distribution of micro-indicators of optimal TAZ division scheme; (**a**) distribution of TAZ area; (**b**) distribution of travel proportion in TAZ. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & \begin{tabular}{c} **Optimal TAZ Division** \\ **Scheme (285 TAZs)** \\ \end{tabular} & \
\begin{tabular}{c} **TAZ Division Scheme in** \\ **2008 (304 TAZs)** \\ \end{tabular} &
\begin{tabular}{c} **TAZ Division Scheme in** \\ **2010 (364 TAZs)** \\ \end{tabular} \\ \hline Travel density variation coefficient & 2,008,169 & 3,266,995 & 4,385,743 \\ Average travel proportion in the TAZ & 3.95\% & 6.49\% & 5.18\% \\ Average area & 1.09 km\({}^{2}\) & 0.98 km\({}^{2}\) & 0.83 km\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of indicators of TAZ division schemes. Figure 9: The optimal Traffic Analysis Zone division scheme (285 TAZs). In this figure, each colored region represents a distinct TAZ. The color coding indicates different TAZs, with the optimal division scheme consisting of 285 TAZs. The boundary lines are based on GIS data and bus network information from March 2016.