一种基于序贯Monte Carlo算法的交通事件重构方法

为了能准确地重构出当前道路场景中的交通流事件,需要合适的模型与方法以及能够代表交通流状态的实时数据。基于交通流非线性非高斯的特点,提出了一种基于序贯Monte Carlo方法的交通流堵塞事件重构模型。提出的模型能够不断的同化真实道路上实时的传感器数据使仿真中的交通流状态与真实路况不断接近。通过分析仿真数据推测出当前真实道路上的堵塞事件及其相关属性,并据此在仿真环境中模拟堵塞,进而实现对真实道路上堵塞事件的重构。理论研究和实验结果表明该模型能够根据重构结果评估当前的道路状况,合理推测引起拥堵的位置与堵塞范围。     

Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information

In just-in-time (JIT) manufacturing environments, on-time delivery is a key performance measure for dispatching and routing of freight vehicles. Growing travel time delays and variability, attributable to increasing congestion in transportation networks, are greatly impacting the efficiency of JIT logistics operations. Recurrent and non-recurrent congestion are the two primary reasons for delivery delay and variability. Over 50% of all travel time delays are attributable to non-recurrent congestion sources such as incidents. Despite its importance, state-of-the-art dynamic routing algorithms assume away the effect of these incidents on travel time. In this study, we propose a stochastic dynamic programming formulation for dynamic routing of vehicles in non-stationary stochastic networks subject to both recurrent and non-recurrent congestion. We also propose alternative models to estimate incident induced delays that can be integrated with dynamic routing algorithms. Proposed dynamic routing models exploit real-time traffic information regarding speeds and incidents from Intelligent Transportation System (ITS) sources to improve delivery performance. Results are very promising when the algorithms are tested in a simulated network of South-East Michigan freeways using historical data from the MITS Center and Traffic.com.     

基于序贯蒙特卡洛算法的交通流事件重构

针对交通数据重构应用性差、缺乏对交通事件重构的研究等问题,结合交通流非线性非高斯的特点,提出一个基于序贯蒙特卡洛方法的交通流堵塞事件重构模型。该模型不断同化道路上的传感器数据,使仿真中的交通状态不断逼近真实路况,通过分析仿真数据以探测真实路网中存在的堵塞事件。模型能够对探测到的堵塞进行多粒子模拟来实现对真实道路上堵塞事件的重构。实验结果表明,该模型能够推测并重构出道路上的堵塞事件,对堵塞起始位置重构的平均误差为17m,对堵塞范围重构的平均覆盖率为82%。     

Traffic modeling, prediction, and congestion control for high-speednetworks: a fuzzy AR approach

In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on the adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual Ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control     

Ping-pong flow control for ATM ABR traffic

In this paper, we propose an improved technique for congestion control, named as ping-pong flow control (PPFC), for asynchronous transfer mode (ATM) available bit rate (ABR) traffic. This is a rate-based flow control scheme, in which the rate regulation is achieved by directly adjusting the transmission rate in the source end station. The proposed algorithm uses a bipolar feedback strategy, which employs positive and negative feedbacks to control the transmission rate for different switch states. These states are determined using the traditional threshold-based method. We also introduce state early detection (SED), which enables the PPFC to control traffic flows more precisely and accurately at critical moments. The simulation results show that the proposed algorithm provides a higher throughput and lower cell loss ratio when compared to the well-known backward explicit congestion notification (BECN). Furthermore, these results also show that PPFC is robust against feedback losses.     

A V2I communication-based pipeline model for adaptive urban traffic light scheduling

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%–78% and average stop frequency by 12%–34% in the context of traffic management. The GTLM reduces travel time by 22%–44% and 30%–55% under two types of traffic conditions and achieves optimal performance in congested scenarios.     

基于集成学习的交通拥堵预测模型

The current navigation software has obvious inaccurate speed assessment when facing some serious traffic congestion, and cannot accurately predict the duration of the traffic congestion. Therefore, we propose a traffic congestion prediction model to accu- rately predict the congestion time in the face of most congestion situations through the prediction of speed. Regarding the speed pre- diction model, we select high-similarity samples based on the KNN algorithm. The prediction speed model is divided into two main models, KNN-VA and KNN-RBF, and we use an integrated learning method to fuse these two models to obtain more accurate aver- age speed prediction. Then, the congestion time can be predicted. In order to determine the congestion time, we use the RBF speed prediction method and the sampling method in a fixed area to verify. The results show that the model has high reliability for conges- tion time prediction.     

应用图像处理方法自动检测路口车辆排队长度

在智能交通处理系统中，路口车流参数——车辆排队长度，占空比等可以为很多情况提供必要的信息，如交通阻塞及交通事故的监控、交通信号灯的控制等。其中交通路口车流的长度是车流参数中最重要的一个。本文提出了一种基干图像局部特征的路口车辆排队长度的检测方法，通过融合图像的点特征(角点)和线特征(边缘)，完成车流长度的检测，包括停止车流的长度以及在可视范围内整体车流的长度。实验结果表明，这种方法实现简单、应用效果良好，具有较好的应用前景。     

Urban traffic congestion estimation and prediction based on floating car trajectory data

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.     

窗口流量控制的干线动态协调控制方法

城市交通干线在高峰时期经常处于饱和状态,排队车辆溢出至上游交叉口的"死锁"现象时有发生.为避免干线阻塞、提高饱和条件下干线交通流的运行效率,首先提出一种有效带宽评价方法对已有干线绿波协调控制系统的运行状态及控制效果进行监控;其次借鉴TCP/IP窗口流量控制思想,设计一种窗口流量控制的干线动态协调控制方法,控制城市干线拥挤交通流.模拟试验及对比结果表明,通过窗口流量通告的方式,下游交叉口可以向上游交叉口实时告知路段的有效容量,便于各交叉口信号机根据当前的交通需求及路段的有效容量重新分配各股车流的绿灯时间.     

基于YOLO的道路车辆拥堵分析模型

针对当前交通运行出现的拥堵问题，提出一种新型的道路状态判断模型。首先，模型基于YOLOv3目标检测算法，然后结合图片对应的特征值矩阵，通过相邻帧之间的特征矩阵作差并将差值逐项求和得到的结果与预设值进行比较来判断当前道路是处于拥堵状态还是正常通行状态，其次再将当前计算出的道路状态与前两次计算出的道路状态进行比较，最后运用模型里的状态统计法来统计道路某状态（拥堵或通畅）的持续时间。该模型能够同时对一条道路的三个车道进行状态统计分析，经过实验，模型对单条车道状态判断的平均准确率能达到80%以上，并且白天与夜晚的道路均适用。     

An improved long short-term memory networks with Takagi-Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short-term memory (FOLSTM) neural network for long-term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K-means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell-shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi-Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state-of-the-art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).     

基于小波分析的交通流量异常数据检测*

针对交通拥堵成为制约城市经济和社会发展的这一“瓶颈”,从提高交通控制系统性能的角度来解决城市交通拥堵问题。首先采用小波分析,交通流量数据的高频与低频分量分离;其次,求得原始信号与重构信号的差值;最后,结合最小二乘法找出数据中的异常点。通过对实际道路的交通流量进行实验,验证了该方法能准确地检测出交通流量中的异常数据,使误判率和漏判率都得到极大的降低,为实际的交通信号控制系统提供可靠的数据。     

Fuzzy-based Adaptive Routing Algorithm for Networks-on-Chip

In this paper, we propose two adaptive routing algorithms to alleviate congestion in the network. In the first algorithm, the routing decision is assisted by the number of occupied buffer slots at the corresponding input buffer of the next router and the congestion level of that router. Although this algorithm performs better than the conventional method, DyXY, in some cases the proposed algorithm leads to non-optimal decisions. Fuzzy controllers compensate for ambiguities in the data by giving a level of confidence rather than declaring the data simply true or false. To make a better routing decision, we propose an adaptive routing algorithm based on fuzzy logic for Networks-on-chip where the routing path is determined based on the current condition of the network. The proposed algorithm avoids congestion by distributing traffic over the routers that are less congested or have a spare capacity. The output of the fuzzy controller is the congestion level, so that at each router, the neighboring router with the lowest congestion value is chosen for routing a packet. To evaluate the proposed routing method, we use two multimedia applications and two synthetic traffic profiles. The experimental results show that the fuzzy-based routing scheme improves the performance over the DyXY routing algorithm by up to 25% with a negligible hardware overhead.     

Design of Regulatory Traffic Light Control Systems with Synchronized Timed Petri Nets

Fixing the phases is one of the common methods to control an urban traffic network. Once a road is filled with a high traffic flow approaching its capacity, the conventional traffic light controller is not able to handle this traffic congestion phenomenon well. In this paper, we propose a novel regulatory traffic light control system to handle such traffic congestion by using synchronized timed Petri nets (STPNs). Three kinds of intersections in an urban traffic network are defined and employed to demonstrate our new regulatory traffic light control system models. Finally, the liveness and reversibility of the proposed STPN models are proven through the reachability graph analysis method. To our knowledge, this is the first work that solves a traffic congestion problem with a regulatory traffic light control technique that is effective in preventing vehicles from entering traffic congestion zones.     

Exploring traffic congestion correlation from multiple data sources

Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to explore the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers through experiments. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. In addition, we found that traffic congestion correlation has obvious directionality and transmissibility. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.     

基于视频检测技术的交通拥挤判别模型*

针对日益严重的城市道路交通拥挤问题,提出基于视频检测技术直接判断道路交通拥挤程度的方法。以道路占有率、占有率方差、占有率变化量绝对值为交通特征参数,研究了其与道路拥挤事件发生的关系,在此基础上利用模糊C-均值算法给出了一种交通状态划分方法,最后建立了一种新的交通拥挤判别模型。应用实际采集的视频数据,分别通过该模型及人为判断进行实验验证。实验结果表明该模型是有效可行的。     

Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment

With the new generation of information technology development and the promotion of the Internet, local governments turn their attention to the construction of intelligent transportation systems. More and more cities began building intelligent transportation which has been widely used to monitor urban traffic. Experts can evaluate urban traffic congestion based on the information collected from the big data of intelligent transportation. In recent two years, double hierarchy hesitant fuzzy linguistic term set has been widely used to depict explicit evaluation information, which is straightforward and broad-spectrum. When evaluating traffic congestion in a city, decision makers can utilize double hierarchy hesitant fuzzy linguistic term sets to express vague information. Moreover, the ORESTE method is an applicative method which can select a reliable alternative by subdividing alternatives and reduce the loss of information in the conversion process. In this paper, we propose a double hierarchy hesitant fuzzy linguistic ORESTE method and a new score function of double hierarchy hesitant fuzzy linguistic term set. The method raises a new perspective to reduce the error from other methods and the new score function derives a robust decision-making result. Then, we apply the double hierarchy hesitant fuzzy linguistic ORESTE method to solve a practical case involving choosing the congested city by evaluating the 5S traffic congestion model. Finally, we compare the double hierarchy hesitant fuzzy linguistic ORESTE method with other methods such as the classical ORESTE method and the double hierarchy hesitant fuzzy linguistic MULTIMOORA to illustrate the advantages of our method.     

张量表达下的多模态交通缺失数据补全算法

来自多源感知设备所采集的多模态交通数据,由于探测设备、网络、数据传输等错误往往存在丢失.交通数据的缺失对交通网络智能规划、避免拥堵等会产生重大的负面影响.同时,来自于不同平台数据的编码方式、标识存在差异,很大程度上影响了交通数据的利用.基于此,本文针对交通监控视频与车流量探测数据,结合张量理论,建立了用以描述多模态交通数据的张量模型,并提出了基于Tucker-Crossover的多模态数据补全算法(Tucker-Crossover based Multimodal Data Imputation Algorithm,TCM D-IA),用于多模态交通缺失数据的补全.该方法利用Tucker分解后不同阶的因子矩阵和核矩阵进行相关性融合,从而提高缺失值估计效果.在真实交通数据集上的实验表明,TCMD-IA的多模态交通缺失数据补全效果优于其他方法,且具有较好的鲁棒性.     

Development of an intelligent technique for traffic network incident detection

Automated incident detection and alternative path planning form important activities within a modern expressway traffic management system which aims to ensure a smooth flow of traffic along expressways. This is done by adopting efficient technologies and processes that can be directly applied for the automated detection of non-recurrent congestion, the formulation of response strategies, and the use of management techniques to suggest alternative routes to the road-users, resulting in significant overall reductions in both congestion and inconvenience to motorists. A delicate balance has to be struck here between the incident detection rate and the false-alarm rate. This paper presents the development of a hybrid artificial intelligence technique for automatically detecting incidents on a traffic network. The overall framework, algorithm development, implementation and evaluation of this hybrid fuzzy-logic genetic-algorithm technique are discussed in the paper. A cascaded framework of 11 fuzzy controllers takes in traffic indices such as occupancy and volume, to detect incidents along an expressway in California. The flexible and robust nature of the developed fuzzy controller allows it to model functions of arbitrary complexity, while at the same time being inherently highly tolerant of imprecise data. The maximizing capabilities of genetic algorithms, on the other hand, enable the fuzzy design parameters to be optimized to achieve optimal performance. The results obtained for the traffic network give a high detection rate of 70.0%, while giving a low false-alarm rate of 0.83%. A comparison between this approach and four other incident-detection algorithms demonstrates the superiority of this approach.