No abstract

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.

Transportation is the forerunner of economic and social development. Therefore, the high-quality development of comprehensive transportation is of great significance to ensure overall economic and social progress and the smooth implementation of major national strategies. The essence of high-quality development in transportation is to realize the optimal allocation of transportation resources. This study handles two aspects. First, the factors that reflect the quality of comprehensive transportation development are defined and analyzed, and an evaluation system is proposed to build China’s comprehensive transportation development quality with transportation efficiency as the core is proposed, taking into account transportation infrastructure and transportation scale. Second, the static comprehensive evaluation value is calculated by the entropy weight method, and then the incentive control model is constructed by introducing incentive factors to achieve a dynamic comprehensive evaluation of comprehensive transportation development. The research results not only propose new indicators but also evaluate different modes of transportation within the same dimension. The results show that the quality of comprehensive transportation development in China is generally on the rise, but there are obvious regional differences. The proposed model is derived from evaluation cases in transportation-related fields and has not yet been applied in the transportation field. It can help understand the development status of the industry and assist in policy formulation.

Regional cargo transportation optimization is the key to the overall efficiency improvement of logistics. Heavy trucks are especially important in connecting regional long-distance transportation and heavy cargo transportation. Relying on transport flow data, we take 6-axle trucks as an example, and build a K-means clustering model to label freight vehicle groups based on the analysis of truck trip intensity and travel time differences, and subsequently design the Random Forest-Recursive Feature Elimination (RF-RFE) algorithm to rank the importance of freight features, and use the filtered feature indicators to verify the travel differences of different groups.The results show that (1) heavy-duty trucks have a higher proportion of nighttime trips, staggered features with other models, and assume more medium and long-distance transport functions; (2) from the K-means++ clustering results, six-axle truck transport can be divided into three types: heavy-duty long-distance transport type, heavy-duty short-distance transport type and light-duty short-distance transport type. (3) RF-RFE model feature ranking in vehicle weighing and travel distance importance ranking the top two, ranking correct rate higher than up to 91%, indicating that loading and travel distance can effectively distinguish heavy-duty truck operation characteristics.