Optimization of vehicle speed forecasting horizont on the intercity highway
DOI:
https://doi.org/10.15587/1729-4061.2020.204273Keywords:
intelligent transport systems, speed forecasting, data flow analysis, optimal movement program, main transportationAbstract
The movement of the car in the traffic on intercity routes was investigated. Traffic should be energy efficient, safe and comply with the desired schedule. A method for analyzing the input data flow based on a simulation model has been developed. The proposed simulation algorithm is based on the use of available information resources for driving a car. Traffic control involves choosing a speed with known road and traffic restrictions. The presented algorithm allows to consider the expediency of each of speed increase opportunities over the forecast horizon. The content of the algorithm is the optimal redistribution of time resources. Indicators of control quality are absolute deviations from the optimal energy-saving program of free movement and from the planned schedule. The movement of a freight road train on the long-distance highway E−371 was performed. It was found that the total amount of information increases with increasing distance of scanned traffic. However, the share of reliable information is reduced. It was found that the dependence of the quality of vehicle traffic control on the size of the forecast horizon is piecewise-continuous. The dependence has an extreme value of the horizon in each continuous section, at which the deviation from the optimal program is minimal. The obtained results can be applied in modern intelligent transport systems. The research results make it possible to develop and adhere to optimal long-term traffic programs on highways. It solves the problem of managing large data streams. Large amounts of information for forecasting can be submitted in parts with reasonable frequency using the developed methodologyReferences
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Copyright (c) 2020 Myroslav Oliskevych, Roman Pelo, Irina Prokudina, Valentin Silenko, Oleg Sorokivskyi, Olga Zaiats
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