## 6 Discussion  
According to Table 2, the first experimental study showed that the transportation network of urban agglomerations in the Yangtze River Delta has been increasingly optimized. The frequency of high-speed railways and the traffic flow of expressways between cities in the urban agglomerations in the Yangtze River Delta has increased, and the contact frequency between Shanghai, Suzhou, Jiaxing, Nanjing, Danyang, Suzhou, Changzhou, Kunshan, and other cities has increased significantly, accelerating the flow of factors. The total travel time of high-speed railway and expressway network in the Yangtze River Delta urban agglomeration has been shortened. In 2020, the transportation of passengers and goods in the Yangtze River Delta urban agglomeration was affected to some extent by the COVID-19 epidemic. At the same time, the Yangtze River Delta urban agglomeration transportation integration index showed a rising trend from 2011 to 2020. This indicates that, with the support of state policies, the level of integration of Yangtze River Delta urban agglomeration transportation has been raised, the high-speed traffic network has had a time compression effect on the Yangtze River Delta urban agglomeration, and a \"kernel city linkage has formed, creating a periphery for the overall development pattern of day-to-day communication\". The results of Table 3 show that the transportation network of urban agglomerations in Yangtze River Delta mainly took \"Shanghai-Nanjing-Hangzhou\" as the core, and Suzhou and Hefei as the secondary cores in the periphery, presenting a spatial pattern of multi-center structure. The core city of the transportation network in the Yangtze River Delta had a high index value of the transportation network structure, which reflects its function as a transportation hub. The level of transportation integration in the marginal areas of Shanghai, Hangzhou, Nanjing, and Hefei was low. To accelerate the flow of various factors \begin{table}
\begin{tabular}{c c} \hline \hline
**Test Parameters** & **Statistics** \\ \hline LM-ERR & 4.3298 ** \\ LM-LAG & 2.7305 * \\ RLM-ERR & 1.9433 \\ RLM-LAG & 0.3439 \\ \hline \hline \end{tabular} Note: the significance of * and ** are 10\% and 5\%, respectively.  
\end{table} 
Table 8: Test of spatial autocorrelation of industrial location entropy in Yangtze River Delta Urban agglomeration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **2011–2019** & **2011–2020** \\ \hline \(\lambda\) & \(1.0203\times 10^{-4}\) ** & \(9.8267\times 10^{-5}\) ** \\ Wa & \(-0.0974\) *** & \(-0.1031\) *** \\ Ur & 0.3694 *** & 0.3935 *** \\ EX & \(-0.0109\) * & \(-0.0159\) ** \\ W*Wa & 1.5956 *** & 1.6602 *** \\ W*Ur & \(-6.6549\) *** & \(-6.7344\) *** \\ W*EX & \(-0.0151\) & 0.1312 \\ \hline \hline \end{tabular} Note: the significance of *, ** and *** are 10\%, 5\% and 1\%, respectively.  
\end{table} 
Table 9: The Spatial Durbin Model (3) regression estimation results. and optimize the development of regional industries, these regions need to speed up the construction of relevant transportation infrastructure and improve regional accessibility so as to promote the integrated development of transportation with higher quality in the Yangtze River Delta urban agglomeration. In the second experiment, the development of transportation integration in urban agglomeration affected the industrial agglomeration level of urban agglomeration through the change in labor cost. However, the transportation integration in the urban agglomeration was also optimized owing to the improvement in the high-speed rail network in the Yangtze River Delta. These subsequently dissolved the existing traditional geographical barriers in a gradual manner, somewhat weakening the influence of location on industrial development, Shanghai, Jiangsu, and Zhejiang saw a decrease in industrial output in 2020 owing to COVID-19, which undermined the level of industrial agglomeration.