An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

Road traffic states estimation algorithm based on matching of regional traffic attracters

To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.

Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.

A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.

Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics

The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.

A genetic resampling particle filter for freeway traffic-state estimation

On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.

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 prediction enabled dynamic access points switching for energy saving in dense networks

To meet the ever-increasing traffic demand and enhance the coverage of cellular networks,network densification is one of the crucial paradigms of 5G and beyond mobile networks,which can improve system capacity by deploying a large number of Access Points(APs)in the service area.However,since the energy consumption of APs generally accounts for a substantial part of the communication system,how to deal with the consequent energy issue is a challenging task for a mobile network with densely deployed APs.In this paper,we propose an intelligent AP switching on/off scheme to reduce the system energy consumption with the prerequisite of guaranteeing the quality of service,where the signaling overhead is also taken into consideration to ensure the stability of the network.First,based on historical traffic data,a long short-term memory method is introduced to predict the future traffic distribution,by which we can roughly determine when the AP switching operation should be triggered;second,we present an efficient three-step AP selection strategy to determine which of the APs would be switched on or off;third,an AP switching scheme with a threshold is proposed to adjust the switching frequency so as to improve the stability of the system.Experiment results indicate that our proposed traffic forecasting method performs well in practical scenarios,where the normalized root mean square error is within 10%.Furthermore,the achieved energy-saving is more than 28% on average with a reasonable outage probability and switching frequency for an area served by 40 APs in a commercial mobile network.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

Criterion for the Emergence of Meta-Stable States in Traffic Systems

The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just add a constraint to the classic model by introducing a velocity-dependent randomization. Two typical randomization strategies are adopted in this paper. It is shown that the Matthew effect is a necessary condition to induce traffic meta-stable states, thus shedding a light on the prerequisites for the emergence of hysteresis loop in the fundamental diagrams.

On the Time Series Forecasting of Road Traffic Accidents in Ondo State of Nigeria

This paper focuses on time series forecasting of monthly occurrence of fatal road accidents in Ondo State of Nigeria. Its aim, however, is to use time series analysis to analyze the data obtained from Federal Road Safety Corps （FRSC）, Ondo State Command; which was considered in two cases： the total cases reported （TCR） and the number of deaths resulted from accidents （NOD）. Various smoothing models for time series were used to analyze the two cases. Based on the models, predictions were made and the results show a steady increase as a result of long-term effects on road accidents for the two cases. It was found also that simple exponential smoothing model is the appropriate model for both TCR and NOD.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.

State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

Local-State Routing in Satellite Constellation Networks from the Perspective of Complex Network

Routing algorithms in satellite constellation networks usually make use of the local state information to adapt to the topology and traffic dynamics,since it’s difficult to obtain the global states in time due to the spatial large-scale feature of constellation networks.Furthermore,they use different range of local states and give these states distinct weights.However,the behind design criterion is ambiguous and often based on experience.This paper discusses the problem from the perspective of complex network.A universal local-state routing model with tunable parameters is presented to generalize the common characteristics of local-state routing algorithms for satellite constellation networks.Based on this,the impacts of localstate routing algorithms on performance and the correlation between routing and traffic dynamics are analyzed in detail.Among them,the tunable parameters,the congestion propagation process,the critical packet sending rate,and the network robustness are discussed respectively.Experimental results show that routing algorithms can achieve a satisfactory performance by maintaining a limited state awareness capability and obtaining the states in a range below the average path length.This provides a valuable design basis for routing algorithms in satellite constellation networks.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.

不同降雨量下基于宏观基本图的边界控制策略

在雨雪等不利天气条件下,城市交通拥堵加剧,北京、天津等大城市在降雨条件下经常发生多路段区域性交通拥堵。因此,根据天津市中心城区和市郊区域路网实际交通数据,以路网交通流宏观基本图模型为研究基础,对比不同降雨量以及不同路网的路网交通流时序和宏观基本图变化规律,分析不同降雨量对天津市中心城区和市郊区域路网交通状态的影响。基于不同降雨量下中心城区和市郊区域路网宏观交通流的变化规律,分别构建路网动态演化模型,并对模型的参数进行标定和有效性验证。针对降雨条件下路网发生的区域性拥堵问题,基于宏观基本图的边界控制分别设计了不同降雨量下中心城区和市郊区域路网控制策略,通过仿真实验分析验证了不同控制策略的效果,并给出了能够缓解中心城区和市郊区域路网拥堵的可行策略。结果表明:在小雨天气条件下,将从市郊区域向中心城区的交通流量的转移比例减小量控制在9%~50%范围之内时,中心城区与市郊区域交通状态更加均衡,路网调控效果更好;在大雨天气条件下,将从市郊区域向中心城区的交通流量的转移比例减小量控制在23%~50%范围之内时,中心城区与市郊区域交通状态更加均衡,路网调控效果更好。这表明该控制策略能够缓解中心城区和市郊区域路网的交通拥堵,保障路网交通系统的稳定运行。

多源数据融合驱动的城市快速路交通状态划分

为提升交通状态划分效果,本文提出一种基于负激励项的改进模糊C均值聚类(BNITFCM)交通状态划分模型。该模型在原有FCM(Fuzzy C-Means)模型基础上考虑了交通流样本点权重以及交通流参数权重对聚类效果的影响,并引入隶属度负激励项、交通流权重负激励项、交通流样本点权重负激励项使聚类结果呈现类内高耦合、类间低耦合的特性。在此基础上,对样本点进行加权处理,用加权欧氏距离描述样本点之间的关系。通过拉格朗日乘子法得出模型的迭代公式并通过该迭代公式对模型进行求解。针对大多交通状态划分方法参数特征维度低的问题,本文以经过多源数据融合获得的速度、速度标准差、流量、密度和道路满载度构建高维特征输入。以数值仿真实验检验了BNIT-FCM模型在分类准确性方面的表现,结果表明,BNIT-FCM模型较FCM模型和改进模糊隶属度FCM模型(IFMD-FCM)的ARI(Adjusted Rand Index)分别提升了4.17%和3.56%。以深圳市北环大道卡口和浮动车数据的交通流为研究对象,实验结果表明,BNIT-FCM模型对比FCM模型以及IFMD-FCM模型的轮廓系数分别提升了4.12%和4.07%;同时,BNIT-FCM模型采用多源融合数据的速度及其标准差比单独采用卡口数据和单独采用浮动车数据的速度及其标准差的轮廓系数分别提升了29.67%和54.13%。