Road freight is not only a basic service industry for the national economy development, but also a sub-sector that the largest in size, the largest number of practitioners, the highest degree of marketisation, and people's production and life closely related to the transportation industry. Container road transportation as a main transportation modes of port distribution, is a segment market of logistics. With the development of market, road container transport freight index has been widely concerned by the whole society. This paper uses Spearman rank correlation coefficient and stationarity test, cointegration test and impulse corresponding analysis based on VAR model to explore the influencing factors on road container transport freight index with case study of Ningbo. The results show that there is a positive correlation between road container transport freight index in Ningbo and regional macro factors and operating cost factors. Additionally, the fluctuation of freight rate has a certain relationship with the influencing factors.
Road freight plays an increasingly important role in economic and social system. According to the statistics, it undertakes 75% of the total freight volume in 2021, which is one of the important sources of urban economic development. It is one of the important sources of urban economic development. Aiming at the research on the coordinated development of road freight transport and regional economy, this paper establishes an improved model for the analysis of road freight transport and economy correlation characteristics based on the dynamic operation data and statistical data of freight transport vehicles and uses the theories of agglomeration and connection index. Taking the Yangtze River delta as an example, this paper uses multi-source data such as dynamic track and statistical data of heavy cargo vehicles, analyzing the road freight and economic characteristics among Shanghai, Anhui, Zhejiang and Jiangsu, and exploring the relationship between kilometer freight and economic development in the Yangtze River delta region.
In recent years, the carbon emissions in the transport sector have been maintained at annual growth rate over 5%, making it one of the major sources of Greenhouse Gas (GHG) emissions in China. In the background of low-carbon transformation, the "carbon peaking and carbon neutrality" target has put forward higher requirements for energy saving and carbon reduction in the transport sector. Heavy-duty trucks are a major energy consumer in the transport sector and are a key area for carbon neutrality. With the theory of human-machine-environment system engineering, this research figures out the factors affecting the energy consumption of vehicles. Based on the dynamic data of Heavy-duty trucks Global Positioning System (GPS), the research uses big data processing and statistical analysis to estimate the CO2 emissions of heavy-duty trucks over 12 tons in China and puts forward relevant policy suggestions for the development of energy-saving and carbon-reduction in the road freight industry. This paper provides theoretical support and reference for the green transformation of China's transportation industry.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.