Abstract
Due to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time.
Highlights
With the continuous progress in the field of vehicle hardware technology, coupled with mobile Internet applications and other technology-driven applications, the amount of realtime vehicle information has increased rapidly
This collection of the purely electric bus’s location data and energy consumption data as the basic data, combined with the factors affecting the quantitative expression of the bus operation process, was used for analyzing the operating driving range’s influencing factors and establishing a database for the purely electric bus
The RBF neural network algorithm was optimized by gene expression programming (GEP), and the influencing factors of the operating driving range of the pure electric bus were calculated
Summary
With the continuous progress in the field of vehicle hardware technology, coupled with mobile Internet applications and other technology-driven applications, the amount of realtime vehicle information ( text information) has increased rapidly. The actual use of the data generated by the influence degree impact assessment is urgently required to solve one of the problems of the related applications This topic has attracted considerable research attention far. In this study, we considered the normal operation of a purely electric bus in the process of running data through the GEP optimization of the RBF neural network, using the driving range of the purely electric bus as the research object, the influence of the main factor analysis, and the continued screening of various factors affecting the driving range of the electric buses, to provide decision support for the purely electric bus line planning and adjustment, vehicle scheduling, and charging station planning
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