Shared-Bicycle Modeling and Forecasting in the University Town of Xinxiang City
[1]
Xu Mengli, School of Biomedical Engineering, Xinxiang Medical University, Xinxiang, China; School of Biomedical Engineering, Xinxiang Medical University, Xinxiang, China.
[2]
Wu Yang, School of Biomedical Engineering, Xinxiang Medical University, Xinxiang, China; Xinxiang neural Sensing and Control Engineering Research Center, Xinxiang Medical University, Xinxiang, China.
[3]
Wang Yidong, School of Biomedical Engineering, Xinxiang Medical University, Xinxiang, China; Xinxiang neural Sensing and Control Engineering Research Center, Xinxiang Medical University, Xinxiang, China.
[4]
Zhang Chenqing, Xinxiang neural Sensing and Control Engineering Research Center, Xinxiang Medical University, Xinxiang, China.
[5]
Ren Wu, Xinxiang neural Sensing and Control Engineering Research Center, Xinxiang Medical University, Xinxiang, China.
[6]
Yan Huijuan, School of Pharmacy, Xinxiang Medical University, Xinxiang, China.
[7]
Ren Qiongqiong, Xinxiang neural Sensing and Control Engineering Research Center, Xinxiang Medical University, Xinxiang, China.
There are a large number of young people in the university town of Xinxiang City, and the shared bicycles are just starting. In order to adjust the type and quantity of bicycles, to avoid the imbalance of supply and demand of shared bicycles, and the imperfect pattern, the modelling and analysis of the shared bicycles in the university town were conducted. Firstly, the demand for shared bicycles was calculated according to the total population and bus stations. Secondly, using the time of bicycle use and the number of users as variables, a GM (1, 1) model was established using the grey forecasting method to predict the future users of Youon and Mobike; Finally, Based on the evaluation factors of shared bicycles, a fuzzy comprehensive evaluation model is established to evaluate the comprehensive competitiveness of existing shared bicycles and imported shared bicycles. The results show that the comprehensive competition index of Mobike and ofo in the university town of Xinxiang City are 0.8705 and 0.7955, which have certain development advantages. It verifies the rationality of introducing ofo bicycle into university town. The number of ofo introduced is about 1,000, and Mobike is about 1,000. The obtained data can provide methods and data support for the bicycle-sharing optimization problems in Xinxiang City and other cities.
Shared Bicycles, Mathematical Modeling, Grey Prediction, Fuzzy Comprehensive Evaluation
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