The unprecedented COVID-19 pandemic impacts negatively on the security and development of human society. Comparison and analysis of intercity highway travel patterns before and during the COVID-19 pandemic can bring vital insights for the prevention and control of the pandemic. Empirical studies are conducted using cellular network-based datasets associated with two groups of city pairs in China heavily affected by COVID-19. Spatial matching, full-sample extrapolation, and trajectory feature analysis are adopted to attain travel volumes of intercity highways during four different periods. The reliability of origin-destination (OD) matrices calculated based on the cellular network-based dataset is demonstrated by comparing with the fluctuation trend of traffic count data. The empirical studies show that the OD flows associated with passenger cars on intercity highways in China decreased significantly during COVID-19. With the effective implementation of the pandemic prevention control policy and the orderly promotion of the recovery to work and production, the volumes of intercity highway OD flows returned to the pre-pandemic level in mid-April 2020. Besides, the peak of passenger car trips decreases and the time span for truck trips gets longer owing to implemented control measures in dealing with COVID-19. The results can be applied to the calculation of OD flows between most adjacent cities and analyze the intercity highway traffic travel patterns changes, which provide insightful implications for making intercity travel safety prevention and control policies under epidemic conditions.

In the high-altitude regions of Northwest China, where the levels of transportation infrastructure and public travel information services are relatively weak, problems like inadequate consistency, accuracy and timeliness of information services, insufficiently rich content, poorly targeted services, unsatisfactory public experience and large high-altitude risks remain in contrast to tourists’ growing travel demands for safe, convenient, efficient and comfortable transportation. This study discusses the key points in designing the top-level technical framework and application of transportation and tourism big data system in China’s typical high-altitude Qinghai regions. Particular focuses are on the convergence, sharing, integration and opening of transportation and tourism data under self-driving tour-based high-altitude conditions, as well as the comprehensive utilization of technical ideas and application models. Meanwhile, the main application scenarios of the system are explored for all levels of governmental sectors, the public and businesses. As the research show, the establishment of the system has positive implications for enhancing the coordination and linkage efficiencies between high-altitude scenic spots and surrounding road networks, achieving the “integrated” transportation-tourism information services characteristic of high-altitude regions, and improving the comprehensive analytical and decision-making capacity for transportation and tourism in these regions. The application system can improve the diversified service experience of public travel dominated by self-driving tours to a remarkable extent.

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