# A Study on a Prediction Model of E-Bike Expansion Degree at Irregular Signalized Intersections
## 4 Conclusions  
In this paper, the expansion degree variation of straight-going e-bikes at irregular signalized intersections was studied. Firstly, the flow characteristics of straight-going e-bikes during a green light release period were analyzed. Then, K-means clustering method was used to analyze the spacing variation of the straight-going e-bike. The release stage and release group were determined according to the characteristics of flow and spacing. Finally, a prediction model for the expansion degree of straight-going e-bikes at irregular signalized intersections was proposed based on the BAS-BP neural network. _RMSE_ and _MRE_ were selected to compare the performance of the prediction model before and after BAS algorithm optimization. Conclusions were drawn as follows.  
(1) The straight-going e-bike at irregular signalized intersections could be divided into two release stages and three release groups during the green light release period. At the initial stage of release, the flow rate of the e-bike was higher and the expansion degree varies widely. Additionally, the expansion degree of Group 1 changed obviously.  
(2) In terms of predicting the expansion degree of straight-going e-bikes at irregular signalized intersections, the prediction model of BAS-BP neural network was obviously better than that of BP neural network. _MRE_ of BAS-BP neural network prediction model and BP neural network prediction model were 0.75 and 0.94, respectively. The accuracy of _MRE_ was improved by about 25.3%. _RMSE_ of BAS-BP neural network prediction model and BP neural network prediction model were 4.38 and 5.60, respectively. The accuracy of _RMSE_ was improved by about 27.9%.  
The expansion degree of straight-going e-bikes at irregular signalized intersections was the research object of this paper. However, the expansion degree of left-turn and right-turn e-bikes at irregular signalized intersections could be studied in the future.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Times**} & \multicolumn{4}{c}{_MRE_} & \multicolumn{4}{c}{_RMSE_} \\ \cline{2-9}  & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Testing Set**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Testing Set**} \\ \cline{2-9}  & **BP** & **BAS-BP** & **BP** & **BAS-BP** & **BP** & **BAS-BP** & **BP** & **BAS-BP** \\ \hline
1 & 0.92 & 0.81 & 0.91 & 0.78 & 5.06 & 4.35 & 4.79 & 4.33 \\
2  
**Author Contributions:** All authors contributed substantially to this study. Individual contributions were investigation, T.T. and H.L.; methodology, T.T., J.M. and Z.Y.; and software, M.Z. and C.Z.; and data analysis, T.T.; writing--original draft preparation, T.T., M.Z. and C.Z.; writing--review and editing, J.M. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Transportation Science and Technology Project of Jiangsu Province, No.2018Y15.  
**Insituational Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are not publicly available due to the institutional restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.