基于改进非全连接神经网络的站点客流预测模型

Station Ridership Prediction Model Based on Improved Non-Fully Connected Neural Network

在地面公交运输中,站点客流量数据是公交线网规划最重要的基础数据之一。站点周边兴趣点(POI)的类型、数量以及距离会导致站点客流量出现不同的趋势。神经网络是研究客流预测的常用方案,然而由于POI对客流的影响存在相互独立性,这一重要特征并未在传统全连接神经网络的结构中得以体现,易使预测效果不尽人意。基于POI与客流量关系的特殊性,改进全连接神经网络的基本结构,构建一种特定的非全连接神经网络,利用所有公交站点客流量的历史数据及各类POI分布,实现对站点各时间段的客流量的模拟及预测。模型设定一种连接矩阵实现特定的连接方式,并根据客流量的性质额外赋予部分隐藏层实际意义,构造组合误差传递函数,增强神经网络的可解释性。该模型可以快速收敛至全局最优解,改进传统全连接神经网络的收敛速度慢、拟合效果差、易陷入局部最优解等问题。实验结果表明,该模型单位时间内的客流量预测偏差在50人以内的概率达到88%以上,对比其他常见预测模型均有优质表现,并且能准确模拟每日客流的变化趋势。

Station ridership data are among the most important basic data in the network planning of routine bus systems.The type,number,and distance of the Point of Interest(POI)around a station can lead to different ridership trends.However,this important feature is not reflected in the structure of traditional fully connected neural networks that are commonly used to study ridership prediction because of the mutual independence of POI influence on ridership,which tends to make prediction results unsatisfactory.This study improves the basic structure of a fully connected neural network by considering the specificity of the relationship between POI and ridership and constructs a specific,non-fully connected neural network.The simulation and prediction of ridership at each time period of the station are achieved using historical ridership data at all bus stations as well as weights of various POI types.The model creates a connection matrix to realize a non-fully connected network,thereby constructing a composite error transfer function to associate meaning with some of the hidden layers,to enhance the interpretability of the neural network based on the nature of ridership.The proposed neural network addresses some of the problems of traditional neural networks,such as slow convergence,poor fitting,and entrapment into local optima.Experiments demonstrate that the proposed model converges to the global optimal solution more rapidly and the probability of accurate prediction exceeded 88%when applying the model to 50 people to predict ridership per hour.The model has an excellent effect compared to other common prediction models and can accurately simulate the daily ridership trend.