When discussing transportation big data, many focus on terminal data collection devices and foundational network technologies. However, our current analysis indicates that information sharing and data mining should receive greater emphasis.

At present, transportation big data faces complex challenges: insufficient data sharing and exchange, numerous data sources, and complex data presentation forms. Overcoming these obstacles and achieving data interoperability are essential to continuously enhance intelligent perception and data capabilities, thereby deepening application innovation in the transportation sector.

To build a technology support system integrating big data security and application for comprehensive transportation, and to optimize multimodal collaborative resource allocation across industries, it is necessary to actively improve big data standard systems, establish large-scale, systematic big data sets, and perform data deposition, mining, and analysis within the transportation sector. Through visualization techniques, this enables smarter and more user-friendly transportation planning, design, and service models.