The high reliability and safety of the critical on-board equipment of high-speed trains play a crucial role in the safety of train operation. So, online safety monitoring and maintenance strategy adjustment are key technologies to realize the safe operation of the advanced train control system. To reduce the difficulty of formal verification of Continuous-time Markov Chains (CTMC) model with uncertain parameters, online quantitative safety monitoring and maintenance strategy adjustment methods are realized by combining probabilistic model checking approach with machine learning algorithm. To begin with, for improving the credibility of the training data used for the machine learning algorithm, probabilistic model checking approach is used to verify the CTMC models transformed from dynamic fault tree (DFT) models. Then, for the defined continuous stochastic logic (CSL) property, the relationship between the maximum reachability probability and the failure/repair rates parameters are obtained by using least square support vector machine (LSSVM) in this paper, which can avoid the shortcoming that the traditional formal method can’t converge in the limited verification time. Finally, for improving the real-time performance of the proposed method, Quantitative Safety Boundary Computation Algorithm is designed to compute the quantitative safety boundaries (QSBs) for monitoring quantitative safety level (QSL) and adjusting the maintenance policy.

In this article, a fixed-time deep reinforcement learning (DRL) formation hunting control problem is investigated for a multi-marine surface vehicle (MSV) system. First, considering the lack of dynamic adaptability caused by the conventional deep neural network (DNN) framework, an online adaptive DNN method is proposed for the high-dimensional multi-MSV system. Second, a novel DRL framework is developed for designing fixed-time formation hunting controllers, which integrates the online adaptive DNNs method with the actor–critic-based reinforcement learning (RL) algorithm. Finally, a nonsmooth fixed-time stability analysis is established for the nonsmooth closed-loop system induced by the DRL-based structure, which rigorously demonstrates that all signals converge within a fixed-time interval independent of initial states. The simulation example demonstrates the practical viability of the presented scheme.

The traditional A ∗ algorithm has problems such as low search speed and huge expansion nodes, resulting in low algorithm efficiency. This article proposes a circular arc distance calculation method in the heuristic function, which combines the Euclidean distance and the Manhattan distance as radius, uses a deviation distance as the correction, and assignes dynamic weights to the combined distance to make the overall heuristic function cost close to reality. Furthermore, the repulsive potential field function and turning cost are introduced into the heuristic function, to consider the relative position of obstacles while minimizing turns in the path. In order to reduce the comparison of nodes with similar cost values, the bounded suboptimal method is used, and the idea of simulated annealing is introduced to overcome the local optima trapped by node expansion. Simulation experiments show that the average running time of the improved algorithm has decreased by about 70%, the number of extended nodes has decreased by 92%, and the path has also been shortened, proving the effectiveness of the algorithm improvement.

Considering that the nature of the stego signal caused by spatial domain steganography and joint photographic experts group (JPEG) domain steganography is different, existing deep-learning steganalysis networks typically cannot work well in both spatial and JPEG domains. We propose a unified steganalysis network named ESNet to effectively preserve and identify the stego signal from spatial and JPEG domains. Specifically, dual-branch preprocessing extracts noise residuals by using fixed SRM kernels (branch 1) and randomly initialized kernels (branch 2), fuses the features from two branches and exchanges the fused complementary information through two carefully designed bidirectional fusion blocks, thereby effectively enhancing the signal-to-noise ratio. During feature extraction, considering that low-level features, such as texture and edge, are indispensable for steganalysis, we gather multi-level feature maps at different layers of the network to provide richer feature representations and merge them by using a multi-level feature fusion module, which learns the weight of different features in single-level feature map to enhance the expression of steganographic features. During classification, the multi-scale attention pooling module is employed to extract multi-scale features by designing convolution kernels of different sizes. After concatenating features of different scales, gated channel transformation is exploited to weight the importance of each channel to further strengthen the representations of steganographic features. Finally, stylepooling in combination with global standard deviation pooling and global average pooling, is used to compress channels and preserve the representation ability of channels as much as possible for classification. The experimental results show that the proposed ESNet exhibits state-of-the-art detection performance in both spatial and JPEG domains, and achieves satisfactory robustness against the cover source mismatch.

The system requirement specifications (SRSs) of the train control system (TCS) are the starting point and foundation of system design and development. Defects in the SRSs will bring great risk to the success of railway engineering projects. Therefore, formal modeling and verification methods are introduced to ensure the correctness of TCS. However, there is a huge gap between the formal computer executable model and the SRSs of TCS described in natural language. To solve this problem, a complete conversion process of ‘TCS requirement specification$\rightarrow $semi-formal models (UML/SysML)$\rightarrow $formal models (safety verification model and reliability evaluation model)’ should be created to ensure full coverage and consistency of semi-formal models and formal models to the SRSs of TCS. With the continuous development of wireless communication, artificial intelligence, and control technology, the future advanced TCS is developing towards a more intelligent and autonomous direction. Online safety monitoring and operational state-based maintenance approaches are critical technologies for developing the future advanced TCS. However, the traditional model-checking approach is time-consuming and susceptible to state space explosion problems. To reduce the difficulty of online safety monitoring and reliability evaluation, machine learning algorithms should be combined with the traditional model checking approaches to improve the verification efficiency during train operation. In this paper, we discussed various formal modeling and safety verification methods for the SRSs of TCS and pointed out the above development directions for the advanced TCS.

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness of the proposed algorithm.

For the prediction unit partition modes (PUPM)-based steganography, a mainstream branch of high efficiency video coding (HEVC) video steganography, striking a balance between embedding performance and security is very challenging. Including the$2\mathcal {N} \times 2\mathcal {N}$PUPMs having the maximum number of PUPMs into data embedding is indeed an effective way of enlarging the embedding capacity, but it necessarily causes a significant decline in security. Therefore, a multi-factor-involved cost function (MFICF) is proposed in this paper to evaluate the embedding cost for modifying each PUPM by comprehensively considering four different aspects affecting the embedding performance and security. With the assistance of MFICF, the 7-ary notational system is combined to use all the 7 types of PUPMs containing$2\mathcal {N} \times 2\mathcal {N}$for data embedding, thus enlarging the embedding capacity as well as enhancing the embedding efficiency. The syndrome-trellis code driven by MFICF, named CFSTC, is designed to preferentially select PUPMs with low embedding costs for data embedding, so that the embedding efficiency is largely enhanced. The security is effectively guaranteed by allocating a large embedding cost for modifying$2\mathcal {N} \times 2\mathcal {N}$to another type of PUPM. Finally, a lightweight convolutional neural network in combination with gated channel transformation, called GSCNet, is proposed to replace the in-loop filter in HEVC, further optimizing the visual distortion and bitrate increase caused by data embedding. Combining these components above, we design a PUPM-based steganography algorithm, GSAPM. Experimental results show that GSAPM effectively enhances the embedding performance while maintaining high security.

Online safety monitoring is the key technology to realize the safe operation of the automatic train protection (ATP) system. So, based on the probabilistic model checking and least square support vector machine (LSSVM) algorithms, an intelligent quantitative safety monitoring method is proposed to monitor the operational safety of ATP online. To begin with, the dynamic fault tree (DFT) model and continuous-time Markov Chains (CTMC) model of ATP are established based on the fault-tolerant structure of ATP. Then, the reliability and safety performance of DFT are evaluated by the hierarchical iterative evaluation method when considering the imperfect fault characteristics of the critical sub-equipment. Furthermore, continuous stochastic logic (CSL) is introduced to represent the temporal quantitative safety property. For the defined CSL property, the CTMC model will be verified by probabilistic model checking off-line, and the verification data set will be obtained. The distribution regularities of maximum reachable probability about the failure rate parameters of sub-equipment will be achieved by training the obtained verification data set with the LSSVM model. Finally, the quantitative safety boundaries (QSBs) of the corresponding quantitative safety levels are computed by the designed algorithm. The obtained QSBs can be used for monitoring the operational status of ATP online.

Improving the performance of machine learning algorithms to overcome the curse of dimensionality while maintaining interpretability is still a challenging issue for researchers in artificial intelligence. Patch learning (PL), based on the improved adaptive network-based fuzzy inference system (ANFIS) and continuous local optimization for the input domain, is characterized by high accuracy. However, PL can only handle low-dimensional data set regression. Based on the parallel and serial ensembles, two deep patch learning algorithms with embedded adaptive fuzzy systems (DPLFSs) are proposed in this paper. First, using the maximum information coefficient (MIC) and Pearson's correlation coefficients for feature selection, the variables with the least relationship (linear or nonlinear) are excluded. Second, principal component analysis is used to reduce the complexity further of DPLFSs. Meanwhile, fuzzy C-means clustering is used to enhance the interpretability of DPLFSs. Then, an improved PL method is put forward for the training of each sub-fuzzy system in a fashion of bottom-up layer-by-layer, and finally, the structure optimization is performed to significantly improve the interpretability of DPLFSs. Experiments on several benchmark data sets show the advantages of a DPLFS: (1) it can handle medium-scale data sets; (2) it can overcome the curse of dimensionality faced by PL; (3) its precision and generalization are greatly improved; and (4) it can overcome the poor interpretability of deep learning networks. Compared with shallow and deep learning algorithms, DPLFSs have the advantages of interpretability, self-learning, and high precision. DPLFS1 is superior for medium-scale data; DPLFS2 is more efficient and effective for high-dimensional problems, has a faster convergence, and is more interpretable.

Deep convolutional neural networks have achieved state-of-the-art performance for the removal of atmospheric obscuration. However, most relevant studies have focused on eliminating the effects of atmospheric obscuration but not on the glare in images caused by reflected sunlight. On the basis of a glare image formation model, we propose a deep trident decomposition network with a large-scale sun glare image dataset for glare removal from single images. Specifically, the proposed network is designed and implemented with a trident decomposition module for decomposing an input glare image into occlusion, foreground, and coarse glare-free images by exploring background features from spatial locations. Moreover, a residual refinement module is adopted to refine the coarse glare-free image into fine glare-free image by learning the residuals from features of multiscale receptive field. The experimental results indicated that the proposed network significantly outperforms state-of-the-art atmospheric obscuration removal networks on the built dataset.

Deep convolutional neural networks have been investigated for atmospheric particle removal and accomplished the state-of-the-art performance. Most of the previous studies however focus on removing the effects of atmospheric particles but not on glares caused by direct or reflected sunlight on images. In this paper, we propose a decompose-refine network for single image glare removal. Specifically, our network is composed of a glare detection subnetwork and a glare removal subnetwork, which are respectively in charge of glare detection and removal. Experimental results show that our network outperforms the state-of-the-art network baselines on testing dataset.

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

For high-speed train (HST), high-precision of train positioning is important to guarantee train safety and operational efficiency. For improving train positioning accuracy, we develop a mathematical positioning model by analyzing the wireless position report created by HST. To begin with, k-means algorithm is integrated with the least square support vector machine (LSSVM) to differentiate the position data and establish the corresponding prediction model for each position data class. Then, the ant colony optimization (ACO) algorithm is introduced to adaptively optimize the clustering number of position data and solve the over-fitting problem of the single k-means algorithm. So, a better classification of position data can be obtained by ACO-k-means than the single k-means algorithm. Furthermore, the online learning algorithms are designed for improving the adaptability and real-time performance of established positioning model. Finally, the field data of Beijing-Shanghai high-speed railway (BS_HSR) is used to test the performance of the established positioning models. Experiments on real-world positioning data sets from BS_HSR illustrate that the proposed methods can enhance the real-time performance in online updating process on the premise of reducing the positioning error.

In intelligent transportation systems, there are many tasks that rely on the detection of road congestion, such as traffic signal scheduling and traffic accident detection. As traditional methods for traffic congestion detection are difficult to use, expensive, and may cause damage to the road surface, this paper presents a method for road congestion detection that is based on multidimensional visual features and a convolutional neural network (CNN). This method first detects the density of foreground objects by using a gray-level co-occurrence matrix; second, the speed of moving objects is detected by using the Lucas-Kanade optical flow with pyramid implementation. Third, a Gaussian mixture model is used to model the background, and the CNN is then used to accurately detect the final foreground from the candidate foregrounds. Finally, the proposed method performs road congestion detection in terms of a multidimensional feature space, including traffic density, traffic velocity, road occupancy, and traffic flow. Furthermore, we propose an information entropy method using a histogram of optical flow to enhance the accuracy and reliability of road congestion detection. Simulation results via quantitative and qualitative assessment indicate that the proposed method is able to significantly outperform the state-of-the-art road-traffic congestion detection methods due to the fusion of multidimensional features using the CNN.

This paper puts forward a systems approach for the parallel control and management of the high-speed maglev system (HMS). An artificial HMS is first established by using a multiagent-based technique, and we demonstrate its consistence with the actual HMS. We then conduct some computational experiments and summarize some operational rules for the artificial HMS. Finally, the parallel control and management for the HMS are achieved by parallel execution of the artificial and actual HMSs with parallel interactions between them. We evaluate our approach overall by ensuring the safety and reliability of the HMS through parallel control and management. The solutions and recommendations for the safety control and effective management of the HMS can be provided by the proposed approach.

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L0-norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L0-norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.

The majority of existing studies in subway train operations focus on timetable optimization and vehicle tracking methods, which may be infeasible with disturbances in actual operations. To deal with uncertain passenger demands and realize real-time train operations (RTOs) satisfying multiobjectives, including overspeed protection, punctuality, riding comfort, and energy consumption, this paper proposes two RTO algorithms via expert knowledge and an online learning approach. The first RTO algorithm is developed by a knowledge-based system to ensure the multiple objectives with a constant timetable. Then, by considering uncertain passenger demand at each station and random running time errors, we convert the train operation problem into a Markov decision process with nondeterministic state transition probabilities in which the aim is to minimize the reward for both the total time delay and energy consumption in a subway line. After designing policy, reward, and transition probability, we develop an integrated train operation (ITO) algorithm based on Q-learning to realize RTOs with online adjusting the timetable. Finally, we present some numerical examples to test the proposed algorithms with real detected data in the Yizhuang Line of Beijing Subway. The results indicate that, taking the multiple objectives into account, the RTO algorithm outperforms both manual driving and automatic train operations. In addition, the ITO algorithm is capable of dealing with uncertain disturbances, keeping the total time delay within 2 s and reducing the energy consumption.

The safe operation of Chinese Train Control System Level 3 (CTCS-3) is of great significance, particularly with respect to the increasing operational speed and expanding railway networks of the Chinese high-speed railway system, which has drawn deep concern from both customers and strategic makers. A hazard and operability (HAZOP) study has been recognized as an effective systematic examination to identify any potential problems existing in various industrial processes or operations. This paper presents a process of applying a HAZOP study to identify the hazards of a CTCS-3 onboard system for the first time, which is composed of two major parts: system models and hazard identification on the examination session. To better reflect the structure and functions of a CTCS-3 onboard system, the following models are developed, i.e., a reference model, a function hierarchical model, a state diagram, and a sequence diagram. To demonstrate the effectiveness of the HAZOP study, hazard identification on the basis of the functions of the CTCS-3 onboard system and a scenario of temporary speed restriction is considered and employed. The results indicate that existing hazards can be dug out at express speed, which allows the relevant actions to be proposed and implemented to prevent the hazards from spreading to a wide range in the whole CTCS-3 onboard system.

In this paper, we are trying to find the developmental tendencies and study hotspots of intelligent transportation systems technologies by theme classification and analysis of core articles from all papers published in IEEE Transactions on Intelligent Transportation Systems during 2010-2013. First, we classify theme categories by co-word analysis with different research domains and obtain 12 themes that include vehicle control technology, modeling and simulation, image processing, etc. Second, we find research focuses and directions of these themes by analyzing the trends of the article numbers published in each year of the TOP 5 themes. Finally, we identify TOP 5 core articles of these 12 themes and obtain their specific study hotspots by sorting the citations without self-citations of the articles in the Web of Science.

Current research in automatic train operation concentrates on optimizing an energy-efficient speed profile and designing control algorithms to track the speed profile, which may reduce the comfort of passengers and impair the intelligence of train operation. Different from previous studies, this paper presents two intelligent train operation (ITO) algorithms without using precise train model information and offline optimized speed profiles. The first algorithm, i.e., ITOe, is based on an expert system that contains expert rules and a heuristic expert inference method. Then, in order to minimize the energy consumption of train operation online, an ITOr algorithm based on reinforcement learning (RL) is developed via designing an RL policy, reward, and value function. In addition, from the field data in the Yizhuang Line of the Beijing Subway, we choose the manual driving data with the best performance as ITOm. Finally, we present some numerical examples to test the ITO algorithms on the simulation platform established with actual data. The results indicate that, compared with ITOm, both ITOe and ITOr can improve punctuality and reduce energy consumption on the basis of ensuring passenger comfort. Moreover, ITOr can save about 10% energy consumption more than ITOe. In addition, ITOr is capable of adjusting the trip time dynamically, even in the case of accidents.

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of ±0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.

The determination of principal curves relies on the arc-length as a global index to describe the middle of the data distribution. With a non-constant data distribution, however, curves that are constructed by the approach introduced in reference may not reflect the middle of data distribution, as demonstrated in this article. This is particularly so for curve segments that have a large curvature and a high data density. To overcome this problem, the paper revisits the projection of the samples onto the curve by incorporating Riemannian distances. This analysis suggests estimating the density value of each sample relative to its neighbors and utilize this value to compute the projection index for the curve. The use of density values, in turn, allows penalizing distances between samples along with the arc-length. In a similar fashion to conventional principal curves algorithms, for example proposed by Hastie and Stuetzle and Tibshirani, the incorporation of Riemannian distances gives rise to an iterative algorithm that includes a projection and a self-consistent step. Application studies to simulated and experimental data sets shows that the proposed modification has the potential to outperform existing algorithms in areas of high curvature under an non-constant data distribution.

Satellites are currently being used to track the positions of trains. Positioning systems using satellites can help reduce the cost of installing and maintaining trackside equipment. This paper develops a nonlinear combinatorial data reduction model for a large amount of railway Global Positioning System (GPS) data to decrease the memory space and, thus, speed up train positioning. Three algorithms are proposed by employing the concept of looking ahead, using the dichotomy idea, or adopting the breadth-first strategy after changing the problem into a shortest path problem to obtain an optimal solution. Two techniques are developed to substantially cut down the computing time for the optimal algorithm. The surveyed GPS data of the Qinghai–Tibet railway (QTR) are used to compare the performance of the algorithms. Results show that the algorithms can extract a few data points from the large amount of GPS data points, thus enabling a simpler representation of the train tracks. Furthermore, these proposed algorithms show a tradeoff between the solution quality and computation time of the algorithms.

This paper revisits the construction of principal curves. Although they have a solid theoretical foundation as a nonlinear extension to principal components, this paper shows that they are difficult to implement in practice if the data distribution is sparse and uneven or if the data contain outliers. These issues may hamper the application of principal curves to an intelligent transportation system. To address these problems, this paper introduces an adaptive constraint K-segment principal curve (ACKPC) algorithm that can be applied in the presence of uneven and sparse distributions, as well as outliers. The benefits of the ACKPC algorithm are as follows: (1) It utilizes predefined endpoints of the curve to reduce the computational effort, and (2) it shows to be less sensitive to parameter settings and outliers. These benefits are demonstrated using two benchmark studies and experimental data from a freeway traffic stream system as well as recorded data from a Global Positioning System (GPS) data from a low-precision GPS receiver.

We have proposed to use the method of principal curves to describe and analyze the interaction among freeway traffic-stream variables and their joint behaviors without utilizing conventional assumptions made on the functional forms of interactions, as in previous studies. As a nonparameter modeling approach, the performance of the proposed method depends only on the data used and involves no assumed knowledge regarding the relationship among the traffic-stream variables. First, we discuss the basic algorithm for data analysis using principal curves and the corresponding data filter algorithm for determining principal curves for application in traffic-steam analysis. Second, a case study is used to compare the performance of the proposed method to that of the classical model proposed by Greenshields; results indicate that the proposed model is better than the classical one in both data accuracy and curve shape. Finally, the traffic-stream models generated with principal curves at different locations and lanes are compared with each others and the three-dimensional traffic-stream models developed from principal curves are discussed. Clearly, our results have demonstrated the feasibility and advantages of applying principal curves in freeway traffic-stream modeling and analysis.

According to the salving-principle of self-organization theory, the primary parameter in traffic flow is found. By simulation, a criterion for traffic light setting at an intersection based on the primary parameter is obtained. Then, by analyzing the important effect of the primary parameter on this criterion, the practicability of using self-organization theory in traffic flow research is confirmed.

This work investigates the relationship among speed, flow and density based on the actual traffic data collected from Beijing No.3 loop highway. Principal findings related to the shapes of the three curves are presented. Through observation of the traffic flow, a new modeling approach is proposed in this paper.

Based on the self-organization theory and the feature that a primary parameter is a slow parameter and slaves other parameters, this paper deals with a traffic flow real time control loop model that includes a main loop and sa elf-adaptive loop. This model cannot only assure the real time feature for traffic flow control, but it also assures the precise of control.