A Driver Modeling Methodology Using Hypothetical Reasoning for Multiagent Traffic Simulation

Author(s):  
Yusuke Tanaka ◽  
Yuu Nakajima ◽  
Hiromitsu Hattori ◽  
Toru Ishida
2019 ◽  
Author(s):  
Robin Glebes ◽  
Joshua Dustin ◽  
Jan-Anders Mansson

Author(s):  
Victor L. Shabanov ◽  
Marianna Ya Vasilchenko ◽  
Elena A. Derunova ◽  
Andrey P. Potapov

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.


2021 ◽  
pp. 1-12
Author(s):  
Zhe Li

 In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.


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