  With the booming economy in China, many researches have pointed out that the
improvement of regional transportation infrastructure among other factors had
an important effect on economic growth. Utilizing a large-scale dataset which
includes 3.5 billion entry and exit records of vehicles along highways
generated from toll collection systems, we attempt to establish the relevance
of mid-distance land transport patterns to regional economic status through
transportation network analyses. We apply standard measurements of complex
networks to analyze the highway transportation networks. A set of traffic flow
features are computed and correlated to the regional economic development
indicator. The multi-linear regression models explain about 89% to 96% of the
variation of cities' GDP across three provinces in China. We then fit gravity
models using annual traffic volumes of cars, buses, and freight trucks between
pairs of cities for each province separately as well as for the whole dataset.
We find the temporal changes of distance-decay effects on spatial interactions
between cities in transportation networks, which link to the economic
development patterns of each province. We conclude that transportation big data
reveal the status of regional economic development and contain valuable
information of human mobility, production linkages, and logistics for regional
management and planning. Our research offers insights into the investigation of
regional economic development status using highway transportation big data.
