The transportation dominance is a reflection of the regional transportation infrastructure support capacity. Studying the spatial pattern of transportation dominance of Hunan Province is conducive to promoting external communication and economic development. This paper constructed a traffic dominance model based on the data of roads, railroads, airports and other transportation infrastructures in Hunan Province in 2019, and analyzed the spatial pattern of traffic dominance in Hunan Province by using Arc GIS software. It is found that there are spatial differences in the traffic dominance degree of Hunan Province. the traffic dominance degree of Chang Zhu Tan city cluster is significantly higher than that of western Hunan Province. This provides theoretical support for the economic and social development of Hunan Province.

Automatic parking technology is a key concept in the research of automatic driving. Path planning algorithm is one of the most common problems in automatic parking systems. In order to solve the problem of current parking "difficult", an improved automatic parking path planning algorithm based on RRT algorithm is proposed. Firstly, the vehicle model is established. In order to avoid collisions, the obstacle expansion method is adopted, and multiple transition target points are set by reverse derivation. Combined with the minimum radius constraint, the efficiency of RRT path generation is improved. On the premise of meeting the constraints of vehicle kinematics collision, the basic RRT generation path is optimized, the greedy strategy is used to reduce the redundant nodes in the path, and the path is smoothed with the B-spline curve. This algorithm has strong practicability in real automatic parking process.

With the rapid development of mobile Internet, especially the popularization of mobile smart phones, the online car-hailing industry has developed rapidly in recent years, and more and more people choose more convenient online car-hailing travel. This paper uses Hangzhou Didi online car-hailing orders and other relevant data to study the temporal and spatial distribution characteristics of online car-hailing demand, and establishes and verifies models for the short-term forecast of regional demand, which improves the accuracy and efficiency of the forecast. It is of great significance to improve the efficiency of passenger travel, the dispatch level of online car-hailing operating companies, reduce environmental pollution, and reduce the pressure on urban road traffic. This article uses the KNN-LSTM neural network as the basis and uses the historical nearby demand time series of the area as input to predict the demand for the next time slice in the area, and finally realize the prediction of the demand for online car-hailing in a certain period of time. 

The present paper discusses extensively the essentials of gravity data correction and standardized models while presenting the most basic conceptualization with an in-depth consideration of the situation while proposing and presenting a binomial approximation to the ‘Ellipsoid Gravity. The gravity data acquired from the La Coste –Romberg gravimeter in a practical sense is appropriately treated and thus the standardized model and constants become extremely useful and crucial. An in - depth illustration and example was drawn as referenced illustrating gravity anomaly bouguer effect correction on the sea level plain adjacent to the continental infinite slab plate while the free – air correction was incorporated as shown in the given illustration and review citation in.