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. Although a great number of prediction methods have been presented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Beijing 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 predicted by gathering all the contributions. The results of experiments, 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 passenger 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NLAPSO．RBF的铁路货运量预测研究

铁路货运量需求预测在国家和区域经济发展规划、运输经营决策中具有重要作用。针对提高预测准确性与收敛速度问题,建立了基于RBF神经网络的预测模型。该模型具有最佳函数逼近性能和全局最优特性,适于预测计算,但有参数确定与优化的难题。提出一种基于非线性学习因子调节的粒子群优化（NLA-PSO）算法应用于RBF神经网络的参数优化,进而提高铁路货运量预测的精度与效率。通过1992--2011年铁路货运量预测的实例验证,将仿真结果与其他算法进行了比对,证明了方法的预测精度与收敛速度均优于其他算法,在铁路货运量预测计算上有效可行。     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

A multi-pattern deep fusion model for short-term bus passenger flow forecasting

Short-term passenger flow forecasting is one of the crucial components in transportation systems with data support for transportation planning and management. For forecasting bus passenger flow, this paper proposes a multi-pattern deep fusion (MPDF) approach that is constructed by fusing deep belief networks (DBNs) corresponding to multiple patterns. The dataset of the short-term bus passenger flow is first segmented into different clusters by an affinity propagation algorithm. The passenger flow distribution of these clusters is subsequently analyzed for identifying different patterns. In each pattern, a DBN is developed as a deep representation for the passenger flow. The outputs of the DBNs are finally fused by chronological order rearrangement. Taking a bus line in Guangzhou city of China as an example, the present MPDF approach is modeled. Five approaches, non-parametric and parametric models, are applied to the same case for comparison. The results show that, the proposed model overwhelms all the peer methods in terms of mean absolute percentage error, root-mean-square error, and determination coefficient criteria. In addition, there exists significant difference between the addressed model and the comparison models. It is recommended from the present study that the deep learning technique incorporating the pattern analysis is promising in forecasting the short-term passenger flow.     

组合模型在机场旅客吞吐量预测中的应用

研究机场运输优化控制问题,机场旅客吞吐量受到政治、经济、节假日、票价和天气等多种因素影响,具有周期性和非线性变化特点,传统单一预测方法只能描述其部分变化规律,预测精度低。为了提高机场旅客吞吐量预测,将灰色模型和BP神经网络相结合,形成一种机场旅客吞吐量组合预测模型。首先组合预测模型利用灰色模型对线性变化部分进行预测,然后采用BP神经网络对非线性变化部分进行预测,并对预测误差进行补偿。仿真结果表明,组合模型,解决了单一预测模型存在的缺陷,提高了机场旅客吞吐量预测精度,为机场旅客吞吐量预测提供一种新的思路。     

基于自适应门控图神经网络的交通流预测

交通流预测是智能交通系统中的重要组成部分,由于交通数据的复杂性,长期而又准确的交通流预测一直是时间序列预测中最具挑战性的任务之一。近年来,研究人员将基于图神经网络的时空图建模方法应用于交通流预测任务,并取得了良好的预测性能。然而,现有的图建模方法仅通过预定义的邻接结构反映道路网络中的空间依赖关系,忽略了各节点之间的序列关联关系对预测的重要性。针对这一局限性,提出了一种自适应门控图神经网络（Ada-GGNN）,其核心为通过空间传递模块同时捕获道路网络的空间结构及自适应的时序相关性,并通过门控机制学习节点上的时间序列特征。在两个真实交通网络数据集PeMSD7和Los-loop上的实验结果证明了该模型具有更优越的性能。     

Tourism demand forecasting using novel hybrid system

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.     

基于Prophet-ARIMA模型的民航周转量预测研究

周转量作为计算运输成本、客货运收入、劳动生产率、客货运平均行程和运输密度等指标的依据,能比较全面和确切地反映运输的成果以及运输生产产品的数量,其预测对民航的科学化发展有重要意义.与民航业的快速发展和民航市场的不断扩大相比,目前民航的预测模型种类较少.为探索一种更为有效的方法来提高民航周转量预测准确率,较为新颖的Prop...     

GCPSO优化混合核SVM的地铁车站客流预测

地铁中站点客流量为地铁运营调度部门提供实时调度管理依据。将径向基核函数与多项式核函数线性组合,构建了混合核支持向量回归机（SVM）预测模型。采用基于黄金分割的混沌粒子群（GCPSO）对混合核SVM的参数进行寻优,得到最佳的参数组合。利用该混合核SVM预测广州地铁3号线站点短期客流量。结果表明,GCPSO优化的混合核SVM预测模型对地铁站点的短期客流的预测精度高,预测数据和实测数据拟合良好,相对误差较小,明显优于SVM其他三种预测方法及Elman神经网络预测方法。     

基于小波包与长短时记忆融合的铁路旅客流量预测模型

短期铁路客运需求量的实时精准预测可以为实时调整客运服务结构提供依据.铁路旅客流量数据具有时变性、非线性和随机波动性等特点,传统的预测模型无法精准的预测短期内的客流量.本文提出一种基于小波包分解与长短时记忆融合的深度学习预测模型（WPA-LSTM）,首先用小波包分解将原始客运量时间序列分解重构成多个不同尺度的低频和高频序列,然后分别针对各个子序列进行LSTM模型训练和预测,最后将各子序列的预测值叠加作为WPA-LSTM模型的输出.采用某高铁367天的日旅客流量数据对模型进行实验验证,并与季节性模型和基于经验模态的长短时记忆融合模型进行对比,实验结果表明,WPA-LSTM模型可有效提高铁路旅客流量预测的精度.     

基于灰色神经网络的航线客流量预测

准确预测航线客流量对于航空公司制定航线销售政策有着重要的作用。现有研究中鲜见考虑民航旅客出行的随机性、客流量表现出的非线性特征以及对航线客流量影响因素的分析。针对以上问题,提出一种基于灰色神经网络的航线客流量预测模型。该模型运用灰色理论弱化数据序列的随机性,再结合非线性处理能力较强的BP神经网络,构建基于灰色神经网络的航线客流量预测模型。同时验证了平均折扣率对航线客流量的影响。实验结果表明,相比于灰色GM(1,2)模型、BP神经网络模型,灰色神经网络模型具有更高的航线客流量预测精度和更强的稳定性。     

Application of temporal difference learning rules in short‐term traffic flow prediction

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.     

Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14–18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods.     

基于栈式自编码的上海地铁短时流量预测

城市公共交通网每时每刻都承载巨大的客流量,客流量的增多为公共交通网和交通智能调度带来了巨大的压力。地铁站点短时的客流预测是智能地铁调度系统中重要的决策基础与技术支持。利用历史刷卡数据,提出了一种基于深度学习的地铁短时客流量预测方法,基于栈式自编码器构建深度神经网络模型,采用自下而上逐层非监督预训练,在预训练结束之后,采用反向传播BP算法自上而下来微调整个网络的参数。利用上海一个月范围内的地铁刷卡记录数据进行实验测试,实验结果优于小波神经网络Wavelet NN与自回归移动平均模型ARIMA。     

短时公交客流的弦不变量预测方法

短时公交客流预测是智能公交系统动态调度的基础.文中根据短时公交客流数据特性,提出基于弦理论的短时公交客流预测方法,模拟弦结构建立弦不变量客流预测模型(SI-PFPM),并采用遗传算法优化SI-PFPM中各参数.提出基于动态时间弯曲距离的仿射传播(AP)聚类算法,对短时公交客流时间序列进行聚类分析.利用SI-PFPM预测聚类子集数据,并分析预测残差,验证SI-PFPM可以预测短时公交客流的假设成立.最后将SI-PFPM的预测性能与现有方法进行对比分析,验证SI-PFPM对短时公交客流预测的有效性.     

面向短时地铁客流量预测的混合深度学习模型

城市交通客流量精准预测是智能交通系统的重要环节,是有效管控交通、规划最佳出行线路的关键。目前城市交通客流量短时预测研究主要集中在利用深度学习模型进行时空特征的提取,忽略了对模型优化的研究。针对短时地铁客流量预测存在的问题,提出一种混合深度学习模型ResGRU_Metro,将卷积神经网络、残差单元和门控循环单元相结合,捕获流量数据的时空特征。针对深度学习模型常用的损失函数难以对交通客流量峰值进行精准预测的问题,引入面向短时交通流量预测的加权平方误差,根据交通客流量的大小为预测误差赋予不同权重,并加大对交通客流量峰值处误差的惩罚,使神经网络在反向传播时更加关注峰值处的预测和误差,从而提升交通客流量峰值的预测精度。此外,通过耦合天气、空气质量等外部因子,改善模型的整体预测性能,增强模型的稳定性。实验结果表明,相比LR、PSVR、CNN等典型的预测模型,ResGRU_Metro模型有更高的预测精度,能够准确预测交通客流量的峰值。     

城市轨道交通客流需求分析模型及计算机仿真

针对传统“四阶段法”交通需求分析集聚特征显著、系统环境适应性差等问题以及城市交通系统很难进行实体仿真的缺陷,提出了一种基于城市综合公交统筹和TransCAD仿真的城市轨道交通客流分析模型。该模型基于“四阶段法”基本步骤,在第一阶段中,考虑了不同人口类别的出行强度和空间分布特征,给出了一种基于人口分类和出行目的的发生吸引模型;在交通分布中,引入K-因子和综合行程费用修正重力分布模型;在交通方式划分和分配中,结合合理系统结构边界和出行时间价值,对城市轨道交通客流分配进行精细配流。实验表明：通过对城市人口进行分类,考虑城市交通系统结构和各交通方式的出行广义费用可大大提高客流分析的精度和鲁棒性。     

Improving project-profit prediction using a two-stage forecasting system

Accurate project-profit prediction is a crucial issue because it can provide an early feasibility estimate for the project. In order to achieve accurate project-profit prediction, this study developed a novel two-stage forecasting system. In stage one, the proposed forecasting system adopts fuzzy clustering technology, fuzzy c-means (FCM) and kernel fuzzy c-means (KFCM), for the correct grouping of different projects. In stage two, least-squares support vector regression (LSSVR) technology is employed for forecasting the project-profit in different project groups, respectively. Moreover, genetic algorithms (GA) were simultaneously used to select the parameters of the LSSVR. The project data come from a real enterprise in Taiwan. In this study, some forecasting methodologies are also compared, for instance Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Networks (RBFNN), and Back Propagation Neural Network (BPNN), to predict project-profit in this real case. Empirical results indicate that the two-stage forecasting system (FCM+LSSVR and KFCM+LSSVR) has superior performance in terms of forecasting accuracy, compared to other methods. Furthermore, in observing the results of the two-stage forecasting system, it can be seen that FCM+LSSVR can achieve superior performance, and KFCM+LSSVR can achieve consistently good performance. Therefore, based on the empirical results, the two-stage forecasting system was verified to efficiently provide credible predictions for project-profit forecasting.     

基于分割注意力机制残差网络的城市区域客流量预测

客流量预测是城市交通资源和公共安全智能化管理的重要依据。为了综合考虑城市乘客人群流动自身的既有周期性、趋势性和突发性,以及与城市物理和社会空间的耦合关系,在时空残差网络的基础上,本文提出了基于深度时空数据的分割注意力机制残差网络的城市细粒度客流量预测模型。首先以不同时空间隔的区域客流量历史数据为基础,引入分割注意力机制模块,为各模态的数据分配不同的权重,动态捕捉更高相关性的抽象数据特征;在时空数据的基础上,引入城市功能区属性作为联合特征,结合节假日、气候等外部特征,形成deep&wide网络结构,有效记忆重要特征对客流量变化的贡献。基于北京出租车数据的区域客流量对比实验表明,相比于传统的深度时空残差网络和其他经典机器学习模型,引入了分割注意力机制和城市功能区特征的预测模型能够更好地提取数据多元化的特征,预测精度明显优于其他同类别方法。     

基于灰色预测模型的航线客流量预测

准确的预测航线的客流量,对于航空企业制定航线销售政策有着重要的作用.针对民航客流量预测具有诸多不确定性和数据不足的特点,在选取某航空公司近六年三亚-北京往返航线数据的基础上,提出以回归分析、灰色预测方法对该航线2016年的客流量进行分析预测.经实证分析,结果表明:灰色预测方法对航线客流量的预测的精确度较高,对航空公司预估客流量和制订销售政策有直接的指导意义.