Abstract
To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum.
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22 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-022-03928-1
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Acknowledgements
The authors acknowledge the Natural Science Foundation of Inner Mongolia of China (2015MS0616), Inner Mongolia Scientific and technological innovation guide reward funds project: Facilities Agricultural IOT key equipment and system development and industrialization demonstration, Inner Mongolia Science and Technology Plan Project (201502015).
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Wang, X., Qing-dao-er-ji, R. RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling. Cluster Comput 22 (Suppl 6), 15439–15446 (2019). https://doi.org/10.1007/s10586-018-2625-x
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DOI: https://doi.org/10.1007/s10586-018-2625-x