基于多尺度时空聚类的共享单车潮汐特征挖掘与需求预测研究

姜晓, 白璐斌, 楼夏寅, 李梅, 刘晖

Usage Patterns Identification and Flow Prediction of Bike-sharing System based on Multiscale Spatiotemporal Clustering

JIANG Xiao, BAI Lubin, LOU Xiayin, LI Mei, LIU Hui

表5 LSTM模型预测社区单车需求结果评价

Tab. 5 Evaluation of LSTM model prediction results

社区 评价指标 MAE RMSE PEARSON/% AcR/% 0 12.394 26.682 84.651 86.888 1 27.065 37.996 97.572 94.228 2 21.540 38.921 96.790 96.674 3 60.711 96.166 92.481 86.226 4 48.237 66.411 98.163 97.235 5 11.158 16.725 98.011 95.098 6 6.461 8.509 97.987 95.845 7 33.671 42.844 99.099 95.559 8 5.448 10.145 75.370 91.557 9 44.250 64.302 97.784 97.264 10 25.329 55.762 98.099 95.295 11 19.842 29.365 99.111 97.393 12 35.355 50.229 99.335 95.543 13 2.250 3.806 54.333 78.498 14 7.0785 9.752 95.637 93.854 15 7.211 10.692 92.511 93.463 16 9.106 17.276 78.129 89.304 17 9.171 19.647 87.105 92.711 18 59.250 108.202 95.537 87.758 19 45.013 70.014 97.275 96.156 20 15.644 29.260 98.862 97.189 21 33.316 47.215 99.282 95.957 22 49.750 79.096 90.956 76.763 23 11.092 22.382 68.255 83.070 24 1.671 2.315 82.898 73.008 均值 24.080 38.548 91.010 91.301