城市共享单车需求的多特征CNN-BiLSTM预测方法

Multi-feature CNN-BiLSTM prediction method for urban bike-sharing demand

共享单车的需求量预测对于共享单车企业的精细化运营十分重要,是解决单车区域供需平衡的前提。为精准预测共享单车需求量,本文首先分析了纽约Citi Bike 2017—2019年共享单车数据的时空特征,探究了共享单车出行的分布规律;其次,融合卷积神经网络的特征提取能力和双向长短期记忆网络的双向时序数据处理能力,构建了卷积双向长短时记忆网络CNN-BiLSTM模型;然后,结合出行数据、气象数据及单车时空出行特征,采用相关性分析法探究了单车需求量的显著影响因子,并作为模型输入特征,对模型进行训练,生成单车预测模型;最后,选取CNN、LSTM、BiLSTM、CNN-LSTM作为对比模型,对CNN-BiLSTM预测模型效果进行评价。对比结果表明,CNN-BiLSTM模型的MAE和RMSE最小,分别为0.035和0.058;R^(2)最大,为0.922,模型预测效果最佳。本文可为实际的共享单车调度与再分配提供参考依据。

The demand prediction of shared bicycles is important for the refined operation of shared bicycles.This serves as the premise of solving the balance of supply and demand in the bicycle area.Aiming at accurately predicting the demand for bike-sharing,this study firstly analyzes the spatiotemporal characteristics of the bike-sharing data of Citi Bike in New York from 2017 to 2019 and explores the distribution patterns of bike-sharing trips.Subsequently,the study integrates the feature extraction ability of convolutional neural networks and the bidirectional temporal data processing ability of bi-directional long-short term memory to construct a convolutional bidirectional long-short-term memory network CNN-BiLSTM model.The input features of the CNN-BiLSTM model are determined using correlation analysis from travel data,meteorological data,and space-time travel characteristics of bicycles,and then a well-built bicycle prediction model is generated.Finally,CNN,LSTM,BiLSTM,and CNN-LSTM are selected as benchmarks to evaluate the performance of the CNN-BiLSTM prediction.Results show that the CNN-BiLSTM model has the smallest evaluation indicators of MAE and RMSE with 0.035 and 0.058,and the largest R^(2)with 0.922,and achieves the best prediction performance.This research provides a reference for the actual scheduling and redistribution of shared bicycles.