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.

城市轨道交通车站高峰时段与高峰客流预测模型

现有轨道交通车站高峰客流预测方法简化了车站高峰形成过程,基于默认假设,即车站高峰小时与所属线路高峰小时一致进行预测,忽略了车站与线路间存在的高峰偏差现象,造成部分车站高峰客流量被低估,导致车站能力设计不足,站台拥挤风险增加。从车站高峰形成机理出发,基于用地发生率模型,考虑不同目的出行行为的差异化,对客流属性进行划分,引入不同目的的出行时间概率分布函数,建立站点尺度的高峰小时与高峰客流预测模型框架。该模型真实反映了车站高峰与高峰偏差现象形成的这一复杂过程,可解释性强、符合实际,且能适用于建成环境、车站特征和轨道交通网络服务等变化情形下的车站高峰客流预测。验证结果显示:1)提出模型较传统模型提升了43%~47%的车站预测精度(高峰客流相应的MAPE值下降了5.7%~6.38%,高峰时间相应的APE值下降了23~50 min),具有更广泛的适用性和更稳定、更准确的预测结果,能为车站设计和运营方案制定提供更可靠的决策依据;2)各类出行目的的峰值和峰尺度存在差异,按不同比例叠加后,会产生不同的叠加曲线。揭示了车站高峰客流形成机理为不同用地产生的不同出行目的客流时间分布叠加曲线的高峰。

Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network

The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.

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.

商务区网约车交通组织实践——以上海杨浦滨江南段为例

上海杨浦滨江南段是城市发展重点区域,美团、哔哩哔哩等互联网企业已明确入驻商务区,这将导致网约车夜高峰的产生,同时区域用地紧凑,路网和公交条件一般,网约车交通组织有着很大挑战。该文基于需求预测明确区域网约车交通组织和上落客设施布局原则,从设施挖潜、路网影响、系统配套管理方案三个方面提出改善措施,以此提升网约车服务供给,也为国内相关类似地区的交通组织改造提升提供借鉴。

MCA-TFP Model:A Short-Term Traffic Flow Prediction Model Based on Multi-characteristic Analysis

With the urbanization,urban transportation has become a key factor restricting the development of a city.In a big city,it is important to improve the efficiency of urban transportation.The key to realize short-term traffic flow prediction is to learn its complex spatial correlation,temporal correlation and randomness of traffic flow.In this paper,the convolution neural network(CNN)is proposed to deal with spatial correlation among different regions,considering that the large urban areas leads to a relatively deep Network layer.First three gated recurrent unit(GRU)were used to deal with recent time dependence,daily period dependence and weekly period dependence.Considering that each historical period data to forecast the influence degree of the time period is different,three attention mechanism was taken into GRU.Second,a twolayer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data.Besides,the prediction model was established by combining these three modules.Furthermore,in order to verify the influence of spatial correlation on prediction model,an urban functional area identification model was introduced to identify different functional regions.Finally,the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data.The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.

公交线路资源配置与高峰客流协调评价研究

公交线路资源配置与高峰客流协调发展是公交系统可持续发展的重要保障.对二者协调程度进行评价,通过对公交系统规划与运营的深入研究,运用数据包络分析法,以公交车辆数、站点数、沿线客流强度为投入指标,以高峰客流量、高峰满载率适宜度为产出指标组成评价指标体系,并建立二者之间协调评价模型.以哈尔滨市主城区86条公交线路为评价单元,利用数据包络分析法的CCR模型,得到了每条线路的相对效率值,对每条线路进行协调度评价.结果表明,40条处于强协调状态、34条处于弱协调状态、9条处于轻度失调状态、3条处于严重失调状态,公交线路总体上处于弱协调状态.提出的评价方法在实际运营中具有较强的实用性,可为公交规划与运营管理提供理论依据.

自动预测通勤高峰期交通流量的方法

通勤高峰期的交通拥堵在中国很多城市越来越严重。通勤高峰期城市道路交通流量的准确预测是缓解交通拥堵和建设智能交通的关键基础问题之一。针对现有道路交通流量主要依靠ORIGIN-DESTINATION调查法,成本高、效率低且结果准确性有限等难题,本文提出一种低成本高效率的方法。综合利用全市域范围内的交通、社保、参保、人口等数字城市数据,依据交通工具的服务半径和出行距离,将居民的出行方式归结为步行、公汽、轨道交通和自驾4种类型,利用轨道交通优先原则和最短路径算法分析统计私家车出行数量和轨道交通人流量,并在此基础上预测通勤高峰期内的道路交通流量。以武汉市典型数据为例,证明了该方法的有效性和可靠性。

Vissim在地下交通空间工程中的应用与分析

该文利用软件Vissim对天津开发区现代服务产业区地下交通空间的交通进行模拟建模,并通过vissim得出的一系列路网性能指标,如路网平均车速、路网延误总和、停车率、排队长度等,并与定性分析进行综合比较,讨论Vissim在实际工程利用中的方法与意义。

可接受空中交通流不均衡度模型构建

为优化空中交通流的管控策略,研究了空中交通流分布特性。根据厦门1号扇区实测数据,确定了空中交通流的高峰时段,并计算其累积超容比。引进泰尔指数,作为评价空中交通流不均衡度的评价指标。采用多项式回归分析法建立了空中交通流累积超容比-泰尔指数(CT)模型,并进行显著性检验,构建了可接受空中交通流不均衡度(AATFID)模型。研究结果表明,该模型能较好反映空中交通流高峰分布态势,对空中交通管理策略具有重要指导意义。

电信网网间互联与因特网网间互联成本及费用产生的比较分析

通过对电信网网间互联与因特网网间互联成本及费用的产生进行比较,从互联成本、费用的结构,互联费用结算等方面入手剖析了两者之间的异同,提出了因特网互联应考虑的若干问题。

模糊c均值聚类在交通流高峰期确定中的应用

交通流高峰期是交通规划、交通控制中一个非常重要的概念.目前高峰期一般是凭经验人为确定的.文章利用模糊c均值聚类方法对交通流高峰期的确定问题进行研究.首先对模糊c均值聚类算法进行简要介绍,然后利用该算法对某城间高速公路交通流数据进行聚类分析,分别确定了该高速公路正、反向交通流的高峰期.结果表明,该算法聚类结果与经验交通流高峰期基本一致。

高峰期网络流量高精准度预测模型研究

针对当前网络流量预测法是通过监测网络流量历史数据进行预测,存在预测精准度低和流量信息参数自适性差的问题,提出基于多元线性回归分析的高峰期网络流量预测模型。通过BP神经网络法,确定网络流量信息权值,采用滑动窗口算法得到流量序列中对应信息数据,构成新的网络流量序列,得到多元线性回归初始模型;引入最小二乘法对流量信息参数进行估算,得到流量信息的样本回归函数,使用可决系数F检验及统计样本回归函数,完成高峰期网络流量预测模型的构建。实验结果表明,使用该模型可降低误差、提高拟合度、增加能量利用率,为高峰期网络流量预测提供了基础保障。

城市信号交叉口左转车流车头时距分布特征研究

针对长沙市典型信号交叉口芙蓉中路-人民中路,分别在饱和状态和复合状态下对高峰与非高峰两个时段左转车流的车头时距分布进行特性分析。交叉口实测数据验算表明,我国现行规范CJJ37和美国规范HCM2010等常用方法对左转车流饱和车头时距预测结果均偏大较多,采用4种传统的车头时距分布模型和SPSS中的9大基本曲线模型的拟合效果均不理想。采用分段函数分别建立了饱和状态和复合状态下的统一模型,通过调整模型中的标定系数和增长系数,即可分别得到高峰时段和非高峰时段的车头时距分布特征。实测数据检验分析表明:饱和状态和复合状态的车头时距分布都存在2.000～2.500 s内集中程度最高且超过27%,集中在1.500～2.500 s内超过50%的现象;非高峰时段车头时距分布集中程度低于高峰时段,且倾向于集中在较小的车头时距上;目前常用方法对车头时距平均值的预测误差通常都超过16%,文中模型误差则低于4%,与传统模型相比能较好地拟合车头时距的实际分布形态,拟合效果更为理想。

城市轨道交通高峰线路客流协同控制方法

在线路客流控制中,需同时考虑各个车站控流方案的可执行性与协同性.采用Fisher最优分割法确定合理客流控制时段,基于此建立以乘客总等待时间最少和旅客周转量最大为目标的线路客流协同控制线性规划模型.基于成都地铁2号线AFC数据进行实验,针对协同控流与非协同控流方案,以及不同客流控制时段划分方案下的协同控流方案进行对比实验.算例中:协同控流方案在旅客周转量下降约1.0%的情况下,乘客总等待时间减少约56.7%;基于Fisher最优分割法确定的时段划分方案中协同控流方案在乘客总等待时间方面最优,并具有很好的可执行性.

城市道路交叉口高峰小时流量的灰色预测

利用原有的交叉口高峰小时流量的统计数据，吸收灰色理论中的累加生成及等维灰数递补的方法，采用灰色预测中GM（1，1）模型代替原有的增长系数法，使交叉口高峰小时流量的预测值具有较高的可信度。

基于密度聚类的多向行人流群集区域分布比较

为预防公共场所的行人安全事故,优化和改善人群安全管理,基于情景实验的数据,利用密度峰值算法和具有噪声的密度聚类算法,从不同时刻分布变化的角度,分别选取单走廊双向行人流、90°和120°交叉路口的行人流场景研究行人流群集区域的分布状态,并比较了两种算法的聚类效果和参数差异,得出场景实验数据中行人流群集区域的分布规律和变化特征。研究发现聚类簇在3个场景的行人移动过程中均是动态变化的,不会处在某个稳定的聚类状态。使用该方法识别密集人群的潜在群集区域及位置,可以观察场景内安全隐患区域,提前在这些区域放置引导疏散设施,同时做好全路段防护,提高行人群集疏散的效率及安全性。

An attention-based deep learning model for citywide traffic flow forecasting

Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management.However,the flows in different areas and feature channels(inflow/outflow)may correspond to different degrees of importance in forecasting flows.Many forecasting models inadequately consider this heterogeneity,resulting in decreased predictive accuracy.To overcome this problem,an attention-based hybrid spatiotemporal residual model assisted by spatial and channel information is proposed in this study.By assigning different weights(attention levels)to different regions,the spatial attention module selects relatively important locations from all inputs in the modeling process.Similarly,the channel attention module selects relatively important channels from the multichannel feature map in the modeling process by assigning different weights.The proposed model provides effective selection and attention results for key areas and channels,respectively,during the forecasting process,thereby decreasing the computational overhead and increasing the accuracy.In the case involving Beijing,the proposed model exhibits a 3.7%lower prediction error,and its runtime is 60.9%less the model without attention,indicating that the spatial and channel attention modules are instrumental in increasing the forecasting efficiency.Moreover,in the case involving Shanghai,the proposed model outperforms other models in terms of generalizability and practicality.