Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

Research on traffic flow forecasting model based on cusp catastrophe theory

This paper intends to describe the relationship between traffic parameters by using cusp catastrophe theory and to deduce highway capacity and corresponding speed forecasting value through suitable transformation of catastrophe model. The five properties of a catastrophe system are outlined briefly, and then the data collected on freeways of Zhujiang River Delta, Guangdong province, China are examined to ascertain whether they exhibit qualitative properties and attributes of the catastrophe model. The forecasting value of speed and capacity for freeway segments are given based on the catastrophe model. Furthermore, speed-flow curve on freeway is drawn by plotting out congested and uncongested traffic flow and the capacity value for the same freeway segment is also obtained from speed-flow curve to test the feasibility of the application of cusp catastrophe theory in traffic flow analysis. The calculating results of catastrophe model coincide with those of traditional traffic flow models regressed from field observed data, which indicates that the deficiency of traditional analysis of relationship between speed, flow and occupancy in two-dimension can be compensated by analysis of the relationship among speed, flow and occupancy based on catastrophe model in three-dimension. Finally, the prospects and problems of its application in traffic flow research in China are discussed.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

Short-term traffic flow online forecasting based on kernel adaptive filter

Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.

Multi-Layer Forecast Project of Rain-Induced Debris Flow

Based on four kinds of methods—numerical weather prediction model, cloud image of stationary meteorological satellite, echo image of meteorological radar and telemetric rain gauge, multi space-time scale precipitation prediction products have been achieved, and multi-layer project of debris flow forecast is established with different space-time scale to get different forecast precision. The forecast system has the advantages in combination of regions and ravines, rational compounding of time and space scale. The project, which has debris flow forecast models of Sichuan province, Liangshan district and single ravine, can forecast debris flow in 3 layers and meets the demand of hazard mitigation in corresponding layer.

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.

Applicability of Galway River Flow Forecasting and Modeling System (GFFMS) for Lake Tana Basin, Ethiopia

Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

CT衍生的血流储备分数对梗阻性冠状动脉疾病患者主要不良心血管事件的预测价值研究

背景目前,血流储备分数(FFR)是评估冠状动脉血流的功能和生理学的金标准,与之相比,CT衍生的血流储备分数(CT-FFR)反映冠状动脉病变处血流动力学改变,以及在区分病变特异性缺血方面,均有较高的诊断性能和鉴别能力。目的评价CT-FFR对冠状动脉梗阻性稳定性胸痛患者发生MACE的预测价值。方法本研究纳入2017年1月—2021年6月在宁夏医科大学总医院因稳定性胸痛行冠状动脉CT血管造影(CCTA)检查的患者116例为研究对象,中位随访时间2(0,25)个月。按照随访期内是否发生主要不良心血管事件(MACE)将研究对象分为MACE组(55例)和非MACE组(61例)。比较两组间冠状动脉管腔狭窄程度和CT-FFR之间差异性;再分别根据狭窄程度及CT-FFR中位数将患者分类,比较不同分类患者MACE总发生率和随访<3个月、3~6个月、>6个月MACE的发生率。采用Spearman秩相关分析探讨冠状动脉管腔狭窄程度与CT-FFR之间的相关性;采用多因素Logistic回归分析探讨患者发生MACE的影响因素;绘制狭窄程度、CT-FFR及二者结合后预测冠状动脉梗阻性稳定性胸痛患者发生MACE的受试者工作特征(ROC)曲线,并依据ROC曲线下面积(AUC)比较不同指标的预测性能。结果116例患者冠状动脉管腔狭窄程度中位数为70%(60%,80%),中位CT-FFR为0.79(0.74,0.85)。MACE组患者冠状动脉管腔狭窄程度高于非MACE组(Z=-4.41,P<0.001),CT-FFR低于非MACE组(Z=-5.54,P<0.001)。冠状动脉管腔狭窄程度70%~90%患者MACE发生率高于50%~69%患者(χ^(2)=19.221,P<0.001);CTFFR≤0.8患者MACE发生率高于CT-FFR>0.8患者(χ^(2)=30.025,P<0.001);不同冠状动脉管腔狭窄程度联合不同CT-FFR患者MACE发生率比较,差异有统计学意义(χ^(2)=37.789,P<0.001)。冠状动脉管腔狭窄程度70%~90%患者随访时间<3个月MACE发生率高于50%~69%患者,CT-FFR≤0.8患者随访时间<3个月MACE发生率高于CT-FFR>0.8患者,狭窄程度70%~90%+CT-FFR≤0.8的患者随访时间<3个月MACE发生率高于其他分类(P<0.05)。Spearman秩相关分析结果显示,冠状动脉管腔狭窄程度与CT-FFR呈负相关(rs=-0.5326,P<0.001)。多因素Logistic回归分析结果显示,冠状动脉管腔狭窄程度70%~90%(OR=3.085,95%CI=1.147~8.298,P=0.026)、CT-FFR≤0.8(OR=6.527,95%CI=2.560~16.641,P<0.001)是患者发生MACE的危险因素。冠状动脉管腔狭窄程度联合CT-FFR预测患者发生MACE的价值更高(AUC=0.812,95%CI=0.731~0.892,P<0.001)。结论冠状动脉管腔狭窄程度70%~90%、CT-FFR≤0.8可能是患者发生MACE的危险因素。与狭窄程度相比,CT-FFR对预测冠状动脉阻塞性稳定性胸痛患者发生MACE具有增益价值,狭窄程度与CT-FFR结合后的预测性能更佳。

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

Applying the Dynamic Two-Step Method to Forecast Remaining Oil Distribution of Lower Series ,Xiaermen Oilfield

The distribution of remaining oil is often described qualitatively. The remaining oil distributed in the whole reservoir is calculated according to the characteristics of the space distribution of the saturation of remaining oil. Logging data are required to accomplish this. However, many such projects cannot be completed. Since the old study of remaining oil distribution could not be quantified efficiently, the ＂dynamic two-step method＂ is presented. Firstly, the water cut of every flow unit in one well at one time is calculated according to the comprehensive water cut of a single well at one time. Secondly, the remaining oil saturation of the flow unit of the well at one time is calculated based on the water cut of the flow unit at a given time. The results show that ＂dynamic two-step method＂ has characteristics of simplicity and convenience, and is especially suitable for the study of remaining oil distribution at high water-cut stage. The distribution of remaining oil presented banding and potato form, remaining oil was relatively concentrated in faultage neighborhood and imperfect well netting position, and the net thickness of the place was great. This proposal can provide an effective way to forecast remaining oil distribution and enhance oil recovery, especially applied at the high water-cut stage.

Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning.Due to the complexity of road traffic flow data,traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy.In this study,a combined flow direction level traffic flow prediction graph convolutional network(GCN)and long short-term memory(LSTM)model based on spatiotemporal characteristics is proposed.First,a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks.Additionally,due to the capability to handle long-term dependencies,the longterm memory is used to predict the time series of traffic flow and extract the time features.The proposed model is evaluated using real-world data,which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China.The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model,and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

Traffic simulation and forecasting system in Beijing

Transport system is a time-varying, huge and complex system. In order to have the traffic management department make pre-appropriate traffic management measures to adjust the traffic management control program, and release travel information to travelers, to provide optimal path options to ensure that the transport system operates efficiently and safely, we have to monitor the changing of the state of road traffic and to accurately evaluate the state of the traffic, then to predict the future state of traffic. This paper represents the construction of the road traffic flow simulation including the logical structure and the physical structure, and introduces the system functions of forecasting system in Beijing.

Development of Upstream Data-Input Models to Estimate Downstream Peak Flow in Two Mediterranean River Basins of Chile

Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.

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.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

The Dynamic Analysis of the Cash Flows on ATM

Based on the time series of cash flows on ATM, the varying rule of withdrawal is analyzed. The model of autoregression and moving average is established by Matlab and the reliability is checked. According to the model, the cash flows on ATM are forecasted in the coming 10 days. It is important for banks to prepare the cash.