A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

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.

Short-time prediction for traffic flow based on wavelet de-noising and LSTM model

Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.

Design of Expressway Toll Station Based on Neural Network and Traffic Flow

This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different charging mode of construction cost and waiting time of the United States. Secondly, exploring traffic conditions is divided into two kinds, based on the traffic flow speed-density flow model. Then, a fuzzy-BP neural network model is constructed, with capacity, cost, and safety factor as the input layers and performance as the output layer. It is concluded that this scheme will reduce the occurrence of traffic accidents, so it is desirable. Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents.

An environment-friendly control method for congestion flow ahead of expressway toll stations

To alleviate the resulting increase in energy consumption and emissions and other issues caused by the traffic congestion ahead of the expressway toll station, a novel traffic flowcontrol method is put forward based on the environment-friendly conception. The technical thinking of inducing the slowly moving traffic into a batch pass is determined based on the conclusion of the research, traffic flowtheory and traffic sensing detection technology. The model of stop times is established and the parameters of the system are optimized in accordance with the principle of minimizing the fuel consumption. The optimal location selection of traffic control lights and Detector 2 for queue of different lengths at toll stations are calculated based on the model. Finally, the effect of the congestion flowcontrol system is verified via the Paramics simulation system. The result shows that the control system is capable of reducing90% of fuel consumption for vehicles going through toll stations.

An Innovative Approach for the Short-term Traffic Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation applications,such as traffic control,navigation,path planning,etc.,which are closely related to people's daily life.In the last twenty years,many traffic flow prediction approaches have been proposed.However,some of these approaches use the regression based mechanisms,which cannot achieve accurate short-term traffic flow predication.While,other approaches use the neural network based mechanisms,which cannot work well with limited amount of training data.To this end,a light weight tensor-based traffic flow prediction approach is proposed,which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time.In the proposed approach,first,a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals.Then,a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor.Finally,the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction.The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.

考虑交通流量预测的光−氢−电耦合系统规划

为解决制氢、加氢站建设成本高、氢燃料汽车加氢具有不确定性特点等问题,提出一种考虑交通流量预测的光−氢−电耦合系统规划模型。首先,建立包含氢气生产、压缩和储存并通过充氢装置注入至氢燃料电池汽车内的现场制氢模型;其次,提出交通流量的预测方法,为制氢、加氢站系统建模和运行提供支持;最后,以最小化成本为目标,构建考虑交通流量预测的光−氢−电耦合系统规划模型,然后以IEEE30标准算例,以30路网节点为算例分析系统,研究发现制氢、加氢站联合光伏电站有利于降低系统成本;制氢补贴政策的实施使氢气需求增加28%,制氢、加氢站数量增加20%;电解槽制氢量快速响应峰谷分时电价变化。结果表明所提模型具有有效性。

融合多相关收费站数据的高速公路交通流预测

经济间的紧密联系使城市群区域间多相关收费站交通数据间存在空间关联,准确描述该联系对提升高速公路交通流预测的精度具有积极作用。然而,由于受诸多不确定性的影响,该关联性难以捕捉和量化。针对这一缺陷,该文提出一种ATGCN-Res GRU深度学习高速公路交通流预测方法。通过结合注意力机制构建高、中、低注意度的3个GCN拓扑网络,并根据各个网络的注意度加权获得空间学习数据,将多相关收费站的联系进行量化和分级;同时,为了避免过平滑问题,将两个GRU(Gated Recurrent Unit)模块通过残差连接,进一步提升算法捕捉时间规律的能力;最后,使用特征融合层和全连接层输出预测值。利用该算法预测广东省某高速收费站的交通流量,试验结果表明:该文提出的方法能够有效提升预测精度,与经典模型多元集成CNN-LSTM、CNN-Bi LSTM和DL-SVR相比,平均绝对误差(EMAE)分别减小了7.95、4.52、12.88,均方根误差(ERMSE)分别减小了12.03、6.12、19.05。

基于GAT-LSTM模型的高速公路站点流量预测方法

为了实现对高速公路站点流量的准确预测,提出了一种利用组合模型捕捉高速公路收费站流量时空特征以提高交通流量预测精度的方法,首先对高速公路收费数据进行挖掘,得到交通流量时空数据集;其次通过分析其时空特征,揭示高速公路收费站之间流量的时空演化规律及关联机制;最后结合深度学习模型预测高速公路交通流量。以江西省九江主线收费站为实验对象,收集了2021年5月1日至2021年12月31日的收费数据进行处理,将提取特征后的流量时空数据作为模型输入,得到出口流量分析预测结果。利用平均绝对误差、均方根误差和平均绝对百分比误差3项指标对模型预测效果进行评价,结果显示该模型能够利用时空特征有效提高流量的预测精度,与单一模型相比具有更好的预测性能。

基于OD的高速公路收费站出入口流量预测方法

目前,高速公路收费站拥堵问题成为影响高速公路通行效率的重要因素,为收费站管理人员提供精准高效的拥堵流量预警具有重要的现实意义。文章基于GC-LSTM模型进行改进,提出了一种称为GC-LSTM-OD的高速公路出入口多步流量预测模型。该模型能够有效预测流量演变趋势,同时具备适应非常态情况的能力。为验证模型性能,文章使用了沿江高速太仓-常州段的现实流量数据,实验结果表明,相较于其他模型,GC-LSTM-OD在多步流量预测方面表现出更加优异的性能。

高速公路收费站交通流运行特性分析与通行能力研究

通过实测调研获取广西高速公路主线收费站车道通行速度、车辆服务时间并进行分析,得出不同类型收费车道交通流运行特性,结合分析结果对既有收费站通行能力计算方法进行比较说明,提出基于流水数据中饱和车头时距指标的收费车道通行能力计算方法,并计算得出各类型收费车道的通行能力及相应车辆折算系数取值。

基于多维时间序列的ETC短时交通流量预测模型

ETC短时交通流量预测是高速公路ETC管理的基础,准确的交通流量预测为交通枢纽管理方案设计与ETC车道的建设规划等提供指导.目前关于短时交通流量的研究很多,但多数是以数学表达式的形式进行模型表示,很难进行准确的趋势描述.本文基于多维时间序列的ETC短时交通流量预测模型,考虑了法定节假日、高速公路免费和天气等外界因素对ETC交通流量的影响,并结合某地尾号限行的特殊性,考虑"周几"因素,以某高速公路ETC车道交通流量数据为基础,进行预测.预测结果显示,该模型预测结果总体平均绝对相对误差为8.10%,表明该模型具有较强的实用性.

高速公路串行收费站通行能力仿真研究

针对基于车流到达服从标准泊松分布的并行收费站排队服务模型在交通流的时空复杂性、成本、可靠性等方面存在的不足,提出基于元胞自动机建模与仿真的串行收费站布设方式的通行能力、延误及布设个数的分析方法。研究结论表明:串行收费站的通行能力随布设个数的增加而增大,但增大幅度缓慢降低;串行收费站的车均延误以80%通行能力的流量水平为分界点,随着流量继续增大,车均延误急剧上升。给出了不同的流量水平所对应的串行收费站最佳布设个数。

北方公路收费站空气污染特征及健康影响研究——以大连市为例

选取大连市2个公路收费站作为研究地点,对收费亭内外一氧化碳、可吸入颗粒物、二氧化氮和总烃的浓度进行了监测,发现收费站室内外大部分污染物浓度超过我国标准规定值。利用二元相关和偏相关分析方法研究了收费亭内外空气污染特征及影响因素,影响因素有车流量、地理位置、外界污染源及扩散特性等。通过对收费站工作人员和普通市民身体健康的问卷调查,探讨了公路收费站室内外空气污染对工作人员健康的影响,提出了改善建议。

基于多特征GBDT模型的收费站短时交通流量预测

为了准确地预测高速公路收费站的短时交通流量,以便收费站根据不同时段的交通流量科学合理地制定人员配置方案来缓解收费站交通拥堵,文中提出了一种基于多特征GBDT模型的预测方法。引入一种新的机器学习算法GBDT,并通过数据分析,挖掘出时段、星期与天气3种有效的新特征,对广州机场高速机场收费站短时交通流量进行预测。结果表明,将挖掘的新特征应用于传统的BP神经网络模型建立多特征BP神经网络模型可以将预测误差降低4.67%,而文中提出的模型相对于多特征BP神经网络模型可以将预测误差降低0.91%,从而证明了该模型的有效性和可行性。

关于互通立交收费站拓建设计研究

随着我国城镇化进程的不断深入,汽车时代的提前到来,加之我国假日期间出行高速公路的免费制度,使得出入高速公路高峰流量剧增。由于早期高速公路互通立交收费设施规模对假日出行高峰小时交通量考虑不足,因此在节日期间,尤其在长假期间带来极大的不适应性,需要对这一类收费站进行拓建改造。以安徽省宣广高速公路广德东互通立交收费站改建设计为案例,总结这类收费站改建设计的关键点,并提出相关的设计思路与建议。

基于时空多图卷积网络的交通站点流量预测

交通流量预测是智能交通系统中的重要组成部分,但由于交通流量受交通状况、地理位置、时间等多种因素影响,使其具有高度非线性与复杂性,实现精准预测的难度较大。针对交通站点的出入流量预测问题,提出一种基于上下文门控的时空多图卷积网络(CG-STMGCN)模型。根据站点间的相邻关系与流通流量关系构造邻居图与流通流量图表示站点流量之间的邻近相关性与流量依赖性,在两图上分别建立基于上下文门控的时空卷积模块捕获站点流量的时空特征,并使用哈达玛乘积融合两图的输出作为最终预测结果。在真实交通站点数据集上的实验结果表明,CG-STMGCN模型的预测准确性优于同类预测方法,且稳定性更强。

基于XGBoost算法的大型活动期间轨道进出站量预测

大型活动期间周边轨道站点进出量的精准预测是城市轨道交通管理与运营部门制定运输组织计划的重要依据,也是实现活动期间交通保障的关键.有别于传统的中长期预测方法,文中针对分钟级别的站点客流展开预测研究.在分析大型活动开展前后城市轨道站点客流集散特征的基础上,提出包含日期、时段、天气、规模、活动时间等因素的多维度影响因素集;以海量刷卡数据和丰富的活动数据为基础,构建了基于极端梯度推进决策树的轨道站点预测模型;应用最优模型进行实例验证.结果表明:模型预测精度可达91%以上.

基于交通流量预测的高速公路收费道口运营策略研究

以宁沪高速公路南京主线收费站数据为样本,使用现代时间序列模型预测收费道口交通流量,提出了基于交通流量预测的收费道口运营策略制定方法,并给出了应用实例。

基于元胞自动机的收费通道通过能力仿真

在对称双车道元胞自动机模型的基础上建立收费通道模型,使用Matlab软件对使ETC车辆比例和大车比例对收费通道通过能力的影响情况进行研究.针对ETC车辆比例对通过能力的影响,研究了四种ETC车辆比例的改进方案下,收费站通过流量与到达概率之间的关系,分析了ETC车辆比例对饱和车流量的影响;针对大车比例对通过能力的影响,研究了大车比例与流量之间的关系,模拟了在大车比例较小和较大的情况下各方案对上游主干道路的影响.对三车道收费站在使用ETC时ETC车辆比例和大车比例的合理范围提出建议.