Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.

Research on Railway Passenger Flow Prediction Method Based on GA Improved BP Neural Network

This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability.

The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method

This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.

Prediction of Passenger Flow at Sanya Airport Based on Combined Methods

It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adopted Holt-Winter Seasonal Model,ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018.In order to reduce the prediction error and improve the prediction accuracy at meanwhile,the combinatorial weighted method is adopted to predict the data in a combined manner.Upon verification,this method has been proved to be an effective approach to predict the airport passenger flow.

Traffic flow prediction based on BILSTM model and data denoising scheme

Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.

A prediction model to forecast passenger flow based on flight arrangement in airport terminals

Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals.In this paper,field investigations were carried out in four typical airport terminals with different scales and operation states to reveal the characteristics of passenger flow.A prediction model is established to forecast passengers’distribution in the main areas of an airport terminal based on its flight arrangement.The results indicate the dislocation peaks of passenger numbers in these areas,due to the airport’s departure process.The peak time interval is about 30 min between the check-in hall and the security check area,and 60-80 min between the check-in hall and the departure hall.RD value(i.e.,the ratio of the actual passenger number in a certain area to the design value)is used to describe this peak shifting feature.When the annual passenger throughput of an airport terminal reaches or even exceeds its design value,the total peak RD value is normally 0.6-0.8.For the airport affected by COVID-19,the peak RD is only 0.2,which reflects the decline in terminal passenger numbers during the pandemic.This research provides useful insight into the characteristics of passenger flow in airport terminals,and is beneficial for their design and operation.

基于组合深度学习的轨道交通短时进站客流预测模型

针对轨道交通短时进站客流考虑不充分和特征学习不全面而导致预测精度不高的问题,选取客流特征、天气、空气质量和道路交通拥堵指数等多个因素,提出了一种基于组合深度学习的轨道交通短时进站客流预测模型(CNN-ResNet-BiLSTM)。基于卷积神经网络(CNN)对多因素客流时间序列进行自动提取,在CNN网络中插入多个残差神经网络(ResNet)来加深网络深度,利用双向长短时记忆神经网络(BiLSTM)捕捉前后两个方向的客流时间序列特征并得到预测结果;以杭州市全网80个站点工作日的进站客流为例,验证了该模型的有效性。研究结果表明:与常用的几种模型相比,多因素CNN-ResNet-BiLSTM组合模型的均方根误差(E RMS)至少降低了8.50%,平均绝对误差(E MA)至少降低了6.74%,平均绝对百分比误差(E MPA)至少降低了6.52%。

高速铁路日常客运量的EMD-Informer组合预测方法

铁路客流需求的科学预测是进行运输组织方案决策的重要依据。以高速铁路历史客票数据为基础,结合经验模态分解(empirical mode decomposition,EMD)与机器深度学习中的注意力机制,提出高速铁路日客流量的EMD-Informer组合预测方法。首先采用EMD方法分解高速铁路客流量序列,获得具有周期特征和线路客流内在特征的模态分量,再利用Informer模型分别训练和预测各模态分解分量,并通过多头注意力机制高效挖掘客流数据内在规律和捕捉数据序列中的关键特征,在此基础上重组各分量预测值,从而得到高速铁路日常客流的整体高精度预测值。同时,根据结合问题特征的大量实验,明确可供实际运用参考的超参数设置规则。基于京沪高速铁路全线的实例计算分析表明,相对对比预测方法,EMD-Informer组合预测方法在高速铁路客流的单步预测及超前预测上均具有明显更小的预测误差。

基于卷积长短时记忆网络的短时公交客流量预测

针对传统的短时客流预测方法没有考虑到时序特征中跨时段客流之间的相似性问题,提出一种改进k-means聚类算法与卷积神经网络和长短时记忆网络相结合的短时客流量预测模型k-CNN-LSTM。通过k-means算法对跨时段时序数据进行聚类,使用间隔统计确定k值,构建交通流矩阵模型,采用CNN-LSTM网络处理具有时空特征的短时客流。该模型能够对具有空间相关性的数据进行较为准确的预测。使用真实数据集对模型进行检验和参数调优,实验结果表明:k-CNN-LSTM模型较其他模型有相对较高的预测精度。

Probability tree based passenger flow prediction and its application to the Beijing subway system

In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.

基于迁移学习的轨道交通特殊OD客流预测研究

客流预测一直是轨道交通运营公司关注的重点,由于受到运输能力的限制等因素影响,部分OD的实际客流数据与真实需求有偏差,出现异常或者样本缺失,从而造成总体样本量偏小,直接采用这些样本进行预测会明显影响预测精度,但通过还原样本值增加样本量难度太大。根据上述特点选择基于实例的迁移学习,先确定源域的对象和范围,从源域中选择合适的样本补充到总体样本中,共同组成最终的训练样本数据集,完成迁移学习。同时选择改进的Boost算法,通过误差调整样本权重,不断迭代,得到最终的预测模型。结果表明:基于实例的迁移学习结合改进Boost算法的预测精度要好于传统集成学习、ARIMA模型、多元回归模型,为轨道交通运营公司对特定OD的客流预测提供新的有益尝试。

城市轨道交通乘客画像构建方法及应用策略研究

用户画像广泛应用于个性化推荐、广告精准投放等领域,但目前轨道交通领域的相关研究尚不完善,存在乘客画像的信息少、维度单一、指标设计和挖掘不充分等问题。通过从运营管理者和乘客服务多角度系统梳理互联网+城市轨道交通乘客画像的新需求,进而结合AFC和土地等多源数据,构建满足新需求下的轨道交通乘客画像指标体系,并给出关键性指标的计算和推断方法。以北京地铁为例,构建并验证轨道交通乘客画像构建方法的有效性,分析了乘客画像在辅助精细化客流预测方面的应用效果。结果表明,将反映乘客个体出行特征的返程客流加入到S-ARIMA模型中,相比S-ARIMA模型均方根误差(RMSE)下降9.02,对称平均绝对百分比误差(SMAPE)下降0.16%,有效提高客流预测精度。乘客画像未来还能应用在更多的场景,支撑轨道交通的智慧化、一体化出行服务。

基于非线性卡尔曼滤波的城市轨道交通客流密度短时预测方法

[目的]为应对大客流事件,需准确识别城市轨道交通对大客流时空分布状态及演化规律,有必要基于EKF(扩展卡尔曼滤波)和UKF(无迹卡尔曼滤波),对城市轨道交通客流密度进行短时预测。[方法]从车站和断面两个层面,介绍了自动售检票设备数据的处理方法,并划分了城市轨道交通车站及断面的舒适度等级。通过定义客流密度状态方程和量测方程,分别介绍了EKF模型和UKF模型的城市轨道交通客流密度短时预测计算方法。以国内某城市轨道交通网络化运营城市某条线路为案例,比较了EKF模型及UKF模型的预测精度。[结果及结论]算例结果表明,EKF模型及UKF模型均能通过实时采集当前时段车站自动售检票设备数据来预测下一时段的车站客流密度和断面客流密度,适用于城市轨道交通客流密度短时预测场景。相比于EKF模型,UKF模型全天分时段预测值更接近真实变化趋势,UKF模型预测值与真实值的散点分布更趋集中收敛;UKF模型的均方根误差、平均绝对误差及平均绝对百分比误差均相对更低,说明UKF模型预测精度相对更高。

基于多任务学习的轨道交通短时客流预测研究

为了精准预测轨道交通的短时客流量,有效缓解城市交通拥堵,提出了一种基于多任务学习的轨道交通短时客流预测模型,该模型采用残差卷积神经网络和嵌套式长短期记忆神经网络提取客流的时空相关性,引入注意力机制加强模块对特征的提取效果。考虑轨道交通运营的特点,模型进一步选取列车运行特征、轨道交通站点周边公交站点以及兴趣点数据作为外部特征,以提高轨道交通短时客流预测精度。基于北京地铁历史客流数据,在10、30、60 min等多时间粒度场景下进行实验。结果显示,该方法通过多任务学习的方式建模分析站点进出站客流之间的相互影响,提高了模型的预测性能和泛化能力,为城市轨道交通短时客流预测问题提供了新的思路。

城际铁路综合客运枢纽停车设施预测研究

本文以江苏省淮安市洪泽站综合客运枢纽为例,对小型旅游城市城际铁路综合客运枢纽客流及停车设施进行预测研究。首先,以回归分析法和趋势外推法为基础模型,构建了同时考虑时间因素和主成分因素的城际铁路综合客运枢纽客流预测模型;其次,根据预测模型对洪泽站客流量、对外发送客流量、交通集散方式、各交通方式客流量进行预测;最后,根据预测客流量进一步预测出综合交通枢纽主要停车设施规模。结果表明:洪泽站综合客运枢纽大型停车位预测值为133个、机动车停车位预测值为323个,非机动车停车位预测值为300个。

基于SCBANet模型的九寨沟景区客流量短期预测

针对景区客流量短期预测时存在的特征提取能力差、预测误差大、难以捕捉非常规变化等问题,提出了一种神经网络模型SCBANet,该模型结合了时空归一化、卷积神经网络、双向长短期记忆网络以及注意力机制。首先利用时空归一化的两个模块分别对客流量数据的高频分量与局部分量进行细化;然后利用卷积神经网络对处理后的数据进行特征提取,接着双向长短期记忆网络利用提取到的特征进行景区客流量的预测,最后使用注意力机制捕捉过去不同时间频次对景区客流量的影响,从而提高预测的精确度并捕捉非常规变化。实验结果表明,与其他算法相比,SCBANet模型预测误差可下降97.63%,对未来一周景区客流量预测的每日相对误差均在4%以下,因此更适用于景区短期客流量的预测。

地铁短时客流预测改进LSTM方法

短时客流预测可为轨道交通运营部门规划调度提供参考,其中短时客流预测的精准性尤为重要,为进一步提高城市轨道站点短时客流预测精准性,提出一种结合集合经验模式分解算法和贝叶斯优化算法的改进LSTM方法。先使用集合经验模式分解算法(EEMD)对地铁站点的客流数据进行分解,以减少数据噪声干扰;再通过贝叶斯优化算法(BOA)对长短时记忆神经网络(LSTM)的超参数进行优化,从而提高模型的参数精确性。采用真实的客流数据验证结果表明:相较于单一LSTM以及单层组合模型,双重叠加后的EEMD-BOA-LSTM组合模型预测结果平均绝对误差降低21.8%~44.8%,均方根误差降低16.9%~47.4%,对短时客流的预测结果误差改善显著。

基于深层时空图卷积网络的短时到港客流时空分布预测

针对空港综合交通枢纽各区域短时到港客流感知能力较弱的问题,提出一种基于深层时空图卷积网络的预测方法。以空港综合交通枢纽的空间连通特点和到港旅客的行为规律为依据,构建深层图卷积网络提取临近时间段内到港客流量分布的空间特征,并运用门控循环单元提取空间特征序列的时间依赖性,同时利用当前与历史航班信息对预测结果进行修正,实现对目标时间段内各区域内到港客流的预测。基于国内某大型空港综合交通枢纽内到港客流的历史数据展开验证,结果表明,与代表性的预测模型(历史均值模型、自回归差分滑动平均模型、支持向量机回归模型、长短时记忆神经网络、门控循环单元模型、时间图卷积网络)相比,该方法在测试集上的均方根误差和平均绝对值误差均取得最小值,相较于预测精度第二的时间图卷积网络,预测时间范围为5、15、30 min时,均方根误差分别降低了4.19%、7.15%、7.79%,平均绝对值误差分别降低了9.72%、5.05%、8.89%,说明该方法能够更真实地反映不同区域不同时间段内的客流变化趋势,有助于合理地进行空港综合交通枢纽的运力资源配置。