Statistical Measurement and Spatiotemporal Trend Analysis of the Coordinated Development between Intelligent Transportation and Economy in China

在大数据人工智能背景下，本文以智能交通运输与经济协调发展为研究对象，通过网络爬虫技术获取多维度时间序列数据，经清洗和插值处理后构建评价指标体系，采用熵权法-TOPSIS法对全国31个省份进行评分，发现广东、上海等省份经济与交通发展水平存在显著差异。基于耦合协调模型将省份划分为高水平耦合、协调上升、过渡和失调衰退四类，揭示区域发展不均衡特征。进一步通过灰色GM(1, N)模型对8个代表省份进行协调性预测与时空趋势分析，结果表明协调度较高省份发展态势良好，而低协调度省份需优化两者互动关系以实现可持续发展。研究创新性融合多模型方法，量化了智能交通与经济发展的协同效应，为政府制定区域差异化政策提供了科学依据。Under the background of big data and artificial intelligence, this study focuses on the coordinated development of intelligent transportation and the economy. Multi-dimensional time-series data were obtained through web crawling technology, and an evaluation index system was constructed after data cleaning and interpolation. The entropy weight-TOPSIS method was employed to score 31 provinces in China, revealing significant disparities in the development levels of the economy and transportation in provinces such as Guangdong and Shanghai. Based on the coupling coordination model, the provinces were categorized into four types: high-level coupling, coordinated rise, transition, and imbalanced decline, highlighting the uneven regional development characteristics. Further, the gray GM(1, N) model was used to predict coordination and analyze spatiotemporal trends for eight representative provinces. The results indicate that provinces with higher coordination degrees exhibit favorable development trends, while those with lower coordination degrees need to optimize their interactive relationships to achieve sustainable development. This study innovatively integrates multiple modeling methods to quantify the synergistic effects of intelligent transportation and economic development, providing a scientific basis for governments to formulate regionally differentiated policies.