The distribution of passenger car flow on intercity highways is crucial for traffic planning and management, and analyzing its intrinsic influencing factors is also urgent for solving various traffic problems. In this paper, the Random Forest algorithm is used to identify the key factors affecting the distribution of different passenger car flows, and the degree of dependence of the distribution of different types of passenger car flows on different factors is further analyzed through visualization. The results show that: tertiary gross product and urban population are the significant features affecting the traffic volume of small-sized buses; gross product and urban population are the significant features affecting the traffic volume of medium-sized buses; tertiary gross product and urban population are also the significant features affecting the traffic volume of large-sized buses.
Highway freight transport emissions provide a high contribution to carbon emissions in the transport industry, and analysing the driving factors is conducive to building a low-carbon transport structure. This paper explores the indicators of economic dynamic series level, traffic development and energy consumption structure that affect the carbon emissions of the transport industry by using the pathway analysis, and analyses the dependence of carbon emissions on the truck model through visualisation. The results show that the direct throughput coefficients of GDP of the secondary industry, freight turnover, and the structure of truck models are 0.590, 0.317, and 0.204, respectively, indicating that the economic growth has a positive pulling effect on the carbon emissions of the transport industry, and the reliance on the other two types of indicators is small. The indirect throughput coefficients were 0.358, 0.619 and 0.613, indicating that transport development and model structure are highly dependent on the economy, and that adjusting the model structure with the help of economic development can effectively promote the implementation of emission reduction measures.
The spatial and temporal characteristics of regional traffic flows and their socio-economic significance are investigated by using machine learning and ArcGIS technology with multiple attributes of highway toll station data. The study found that: (1) the overall characteristics of the traffic flow at 10~11, 14~16 and 17~19 hours show a "three-peak" structure, while the spatial distribution of high, medium and low traffic types has obvious clustering characteristics. (2) The specific features of the K-means++ based highway toll station classification are "point-line surface" structure in space, with Kunming West Toll Station, Dujiaying Toll Station, Kunming North Toll Station and Liangmiansi Toll Station as unique Node, Kunchu line and other toll stations along the axis, the rest of the toll stations constitute the surface; time, each type of toll station traffic flow also shows the "three peaks" structure, but there are "peak" and "sub-peak The "peak" and "sub-peak" are divided. (3) Based on ArcGIS technology, the dynamic visualization spatial expression of traffic flow "three peaks" separated by time series reflects the distinctive "day-night" pattern of human travel activities across regions.
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.