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Heavy-haul trains have the characteristics of large volume, long formation, and complex line conditions, which increase the driving difficulty of drivers and can easily cause safety problems. In order to improve the safety and efficiency of heavy-haul railways, the train control mode urgently needs to be developed towards the direction of automatic driving. In this paper, we take the Shuohuang Railway as the research background and analyze the train operation data of SS4G locomotives. We find that the proportion of operation data under different working conditions is seriously out of balance. Aiming at this unbalanced characteristic, we introduce the classification method in the field of machine learning and design an intelligent driving algorithm for heavy-haul trains. Specifically, we extract the data by random forest algorithm and compare the classification performance of C4.5 and CART algorithms. We then select the CART algorithm as the base classifier of the AdaBoost algorithm to build the model of the automatic air brake. For the purpose of heightening the precision of the model, we optimize the AdaBoost algorithm by improving the generation of training subsets and the weight of voting. The numerical results certify the effectiveness of our proposed approach.
Siyu Wei; Li Zhu; Lijie Chen; Qingqing Lin. An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains. Actuators 2021, 10, 188 .
AMA StyleSiyu Wei, Li Zhu, Lijie Chen, Qingqing Lin. An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains. Actuators. 2021; 10 (8):188.
Chicago/Turabian StyleSiyu Wei; Li Zhu; Lijie Chen; Qingqing Lin. 2021. "An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains." Actuators 10, no. 8: 188.
The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep Q-learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count (ETX) as a metric to evaluate the quality of the communication link for the connected vehicles' communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.
Dajun Zhang; F. Richard Yu; Ruizhe Yang; Li Zhu. Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -15.
AMA StyleDajun Zhang, F. Richard Yu, Ruizhe Yang, Li Zhu. Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-15.
Chicago/Turabian StyleDajun Zhang; F. Richard Yu; Ruizhe Yang; Li Zhu. 2020. "Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-15.
Virtually coupled train sets (VCTS) have been proposed to increase the transportation capacity and the flexibility of railway organization. Due to the lack of reliable wireless communications and accurate perceptual information, the promotion of VCTS was challenged. With the development of industrial Internet of things (IIoT), an IIoT-based VCTS is built in the paper based on the popular communication-based train control (CBTC) architecture. Considering the dynamic and complex operation environment, it is difficult to achieve the efficient cooperative control of VCTS. The reason is that the traditional method is frequently trapped into a local optimization. To resolve the problem, we apply reinforcement learning (RL) to obtain an optimal policy for the IIoT-based VCTS, where the traditional artificial potential field (APF) is taken to develop the reward function. RL can thus search the global optimal policy, while APF can help RL to reduce the computation complexity. This can substantially increase the efficiency of the proposed approach. Simulation results confirmed that the proposed RL-based cooperative control approach would bring excellent performance in the IIoT-based VCTS.
Hongwei Wang; Qianqian Zhao; Siyu Lin; Dongliang Cui; Chengcheng Luo; Li Zhu; Xi Wang; Tao Tang. A Reinforcement Learning Empowered Cooperative Control Approach for IIoT-Based Virtually Coupled Train Sets. IEEE Transactions on Industrial Informatics 2020, 17, 4935 -4945.
AMA StyleHongwei Wang, Qianqian Zhao, Siyu Lin, Dongliang Cui, Chengcheng Luo, Li Zhu, Xi Wang, Tao Tang. A Reinforcement Learning Empowered Cooperative Control Approach for IIoT-Based Virtually Coupled Train Sets. IEEE Transactions on Industrial Informatics. 2020; 17 (7):4935-4945.
Chicago/Turabian StyleHongwei Wang; Qianqian Zhao; Siyu Lin; Dongliang Cui; Chengcheng Luo; Li Zhu; Xi Wang; Tao Tang. 2020. "A Reinforcement Learning Empowered Cooperative Control Approach for IIoT-Based Virtually Coupled Train Sets." IEEE Transactions on Industrial Informatics 17, no. 7: 4935-4945.
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federal learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federal learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.
Gaofeng Hua; Li Zhu; Jinsong Wu; Chunzi Shen; Linyan Zhou; Qingqing Lin. Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway. IEEE Access 2020, 8, 176830 -176839.
AMA StyleGaofeng Hua, Li Zhu, Jinsong Wu, Chunzi Shen, Linyan Zhou, Qingqing Lin. Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway. IEEE Access. 2020; 8 (99):176830-176839.
Chicago/Turabian StyleGaofeng Hua; Li Zhu; Jinsong Wu; Chunzi Shen; Linyan Zhou; Qingqing Lin. 2020. "Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway." IEEE Access 8, no. 99: 176830-176839.
Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks' impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.
Li Zhu; Yang Li; F. Richard Yu; Bin Ning; Tao Tang; Xiaoxuan Wang. Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems. IEEE Transactions on Intelligent Transportation Systems 2020, 1 -13.
AMA StyleLi Zhu, Yang Li, F. Richard Yu, Bin Ning, Tao Tang, Xiaoxuan Wang. Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems. IEEE Transactions on Intelligent Transportation Systems. 2020; (99):1-13.
Chicago/Turabian StyleLi Zhu; Yang Li; F. Richard Yu; Bin Ning; Tao Tang; Xiaoxuan Wang. 2020. "Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-13.
The application of network slicing to mobile edge computing (MEC) systems has aroused great interest from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes.
Jie Feng; Qingqi Pei; F. Richard Yu; Xiaoli Chu; Jianbo Du; Li Zhu. Dynamic Network Slicing and Resource Allocation in Mobile Edge Computing Systems. IEEE Transactions on Vehicular Technology 2020, 69, 7863 -7878.
AMA StyleJie Feng, Qingqi Pei, F. Richard Yu, Xiaoli Chu, Jianbo Du, Li Zhu. Dynamic Network Slicing and Resource Allocation in Mobile Edge Computing Systems. IEEE Transactions on Vehicular Technology. 2020; 69 (7):7863-7878.
Chicago/Turabian StyleJie Feng; Qingqi Pei; F. Richard Yu; Xiaoli Chu; Jianbo Du; Li Zhu. 2020. "Dynamic Network Slicing and Resource Allocation in Mobile Edge Computing Systems." IEEE Transactions on Vehicular Technology 69, no. 7: 7863-7878.
The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.
Jie Feng; F. Richard Yu; Qingqi Pei; Jianbo Du; Li Zhu. Joint Optimization of Radio and Computational Resources Allocation in Blockchain-Enabled Mobile Edge Computing Systems. IEEE Transactions on Wireless Communications 2020, 19, 4321 -4334.
AMA StyleJie Feng, F. Richard Yu, Qingqi Pei, Jianbo Du, Li Zhu. Joint Optimization of Radio and Computational Resources Allocation in Blockchain-Enabled Mobile Edge Computing Systems. IEEE Transactions on Wireless Communications. 2020; 19 (6):4321-4334.
Chicago/Turabian StyleJie Feng; F. Richard Yu; Qingqi Pei; Jianbo Du; Li Zhu. 2020. "Joint Optimization of Radio and Computational Resources Allocation in Blockchain-Enabled Mobile Edge Computing Systems." IEEE Transactions on Wireless Communications 19, no. 6: 4321-4334.
Security is crucial in cyber-physical systems (CPS). As a typical CPS, the communication-based train control (CBTC) system is facing increasingly serious cyber-attacks. Intrusion detection systems (IDSs) are vital to protect the system against cyber-attacks. The traditional IDS cannot distinguish between cyber-attacks and system faults. Furthermore, the design of the traditional IDS does not take the principles of CBTC systems into consideration. When deployed, it cannot effectively detect cyber-attacks against CBTC systems. In this paper, we propose a novel intrusion detection method that considers both the status of the networks and those of the equipment to identify if the abnormality is caused by cyber-attacks or by system faults. The proposed method is verified on a hardware-in-the-loop simulation platform of CBTC systems. Simulation results indicate that the proposed method has achieved 97.64% true positive rate, which can significantly improve the security protection level of CBTC systems.
Yajie Song; Bing Bu; Li Zhu. A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems. Electronics 2020, 9, 181 .
AMA StyleYajie Song, Bing Bu, Li Zhu. A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems. Electronics. 2020; 9 (1):181.
Chicago/Turabian StyleYajie Song; Bing Bu; Li Zhu. 2020. "A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems." Electronics 9, no. 1: 181.
This paper investigates the robust distributed cruise control problem of multiple high-speed trains under external disturbances. First, by modeling each train as a cascade of point masses connected by spring-like couplers, the longitudinal interaction between adjacent cars are represented by the connected topological graph. Then, under the framework of the communication-based train control technology, the interaction of desirable speed information among trains and the wayside control center is described by the directed topological graph. Next, a distributed cruise controller is designed by taking advantages of the graphic theory such that the multiple trains track different target speeds, and both the distance of neighboring cars and the headway of successive trains are kept in appropriate ranges. Finally, to eliminate the influence of external disturbances, we adopt the disturbance observer to approximate the perturbations, and present a sufficient condition for the existence of the distributed control strategy and the observer gain parameter in form of the linear matrix inequality (LMI). Numerical experiments illustrate that the composite control law is effective in inhibiting the external disturbances, and guaranteeing the safety, efficiency and comfort of high-speed trains' movement.
Xi Wang; Li Zhu; Hongwei Wang; Tao Tang; Kaicheng Li. Robust Distributed Cruise Control of Multiple High-Speed Trains Based on Disturbance Observer. IEEE Transactions on Intelligent Transportation Systems 2019, 22, 267 -279.
AMA StyleXi Wang, Li Zhu, Hongwei Wang, Tao Tang, Kaicheng Li. Robust Distributed Cruise Control of Multiple High-Speed Trains Based on Disturbance Observer. IEEE Transactions on Intelligent Transportation Systems. 2019; 22 (1):267-279.
Chicago/Turabian StyleXi Wang; Li Zhu; Hongwei Wang; Tao Tang; Kaicheng Li. 2019. "Robust Distributed Cruise Control of Multiple High-Speed Trains Based on Disturbance Observer." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 267-279.
Mobile edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues, 2) cooperative computation offloading, and 3) dynamic optimization. To address the security and privacy issues, we employ blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this paper, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multi-objective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an A3C-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. Simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.
Jie Feng; F. Richard Yu; Qingqi Pei; Xiaoli Chu; Jianbo Du; Li Zhu. Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach. IEEE Internet of Things Journal 2019, 7, 6214 -6228.
AMA StyleJie Feng, F. Richard Yu, Qingqi Pei, Xiaoli Chu, Jianbo Du, Li Zhu. Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach. IEEE Internet of Things Journal. 2019; 7 (7):6214-6228.
Chicago/Turabian StyleJie Feng; F. Richard Yu; Qingqi Pei; Xiaoli Chu; Jianbo Du; Li Zhu. 2019. "Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach." IEEE Internet of Things Journal 7, no. 7: 6214-6228.
Nowadays, most communication based train control systems (CBTC) use Wireless Local Networks (WLAN) technology for information transmission. However, because of the high running speed of the train in urban rail transit, WLAN technology cannot fully satisfy the information transmission under the condition of train operation at a high speed. Long term evolution (LTE) technology has a high transmission speed, which can better meet the real-time requirements of train and ground information transmission. Therefore, LTE technology has become an emphasis on the research of communication technology in today’s urban rail transit. Although there is a lot of research on the application of LTE technology in urban rail transit, there is little research on the reliability of LTE based train ground communication systems using experimental data. In this paper, the reliability of train ground communication is firstly defined. Then the train ground communication environment was established in the laboratory, and the performance parameters of train ground communication based on LTE technology were obtained in the test. Finally, the reliability is calculated and analyzed according to the experimental results, and a storage method of train data in actual operation is proposed, which can be used to analyze the reliability of train information transmission. The results show that the reliability of train ground communication based on LTE technology meets CBTC requirements.
Shuo Wang; Li Zhu; Kun Xu; Lin Zhang; Xuan Wang. Reliability Evaluation for LTE Based CBTC Train Ground Communication Systems. Journal of Advanced Transportation 2019, 2019, 1 -11.
AMA StyleShuo Wang, Li Zhu, Kun Xu, Lin Zhang, Xuan Wang. Reliability Evaluation for LTE Based CBTC Train Ground Communication Systems. Journal of Advanced Transportation. 2019; 2019 ():1-11.
Chicago/Turabian StyleShuo Wang; Li Zhu; Kun Xu; Lin Zhang; Xuan Wang. 2019. "Reliability Evaluation for LTE Based CBTC Train Ground Communication Systems." Journal of Advanced Transportation 2019, no. : 1-11.
Quality of service (QoS) guarantee is critical in urban rail transit. In this paper, the train-centric communication-based train control (CBTC) systems through train-to-train (T2T) wireless communication is introduced based on the modification of LTE vehicle-to-everything (LTE-V2X). To be specific, a novel train-centric CBTC systems is established based on T2T wireless communication where distributed sensing-based semi-persistent scheduling (DS-SPS) is served as the resource allocation scheme in the T2T scenario. The quantized age of information (AoI) is used as an integrated system QoS indicator of the CBTC wireless communication systems in urban rail transit. Machine learning techniques especially Q-learning is further utilized to improve system AoI performance. Simulation results show that the proposed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning can achieve improved system AoI and peak AoI performance compared with fixed SPS policy. Furthermore, the system performance of the designed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning is shown to be better than traditional LTE-M and WLAN based wireless communication systems.
Xiaoxuan Wang; Lingjia Liu; Li Zhu; Tao Tang. Train-Centric CBTC Meets Age of Information in Train-to-Train Communications. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 4072 -4085.
AMA StyleXiaoxuan Wang, Lingjia Liu, Li Zhu, Tao Tang. Train-Centric CBTC Meets Age of Information in Train-to-Train Communications. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (10):4072-4085.
Chicago/Turabian StyleXiaoxuan Wang; Lingjia Liu; Li Zhu; Tao Tang. 2019. "Train-Centric CBTC Meets Age of Information in Train-to-Train Communications." IEEE Transactions on Intelligent Transportation Systems 21, no. 10: 4072-4085.
The security and Quality of Service (QoS) provisioning are two critical themes in urban rail Communication-Based Train Control (CBTC) data communication systems, which can directly affect the train’s safe operation. In this paper, we design a novel train-centric CBTC systems using train-to-train (T2T) wireless communication with innovative the security check scheme. The local security certification and cooperative security check are proposed to detect and defense the Sybil attack based on CBTC T2T communications. The quantized Age of Information (AoI) is used as an integrated QoS and security indicator of the train-centric CBTC data communication systems. The proposed AoI indicator fully considers the impact of the packet drop and re-transmission, Sybil attack, and the cooperative security check on CBTC systems. Policy-based asynchronous reinforcement learning is utilized to improve the integrated AoI performance. Simulation results show that the proposed cooperative security check scheme with the optimization model can achieve improved integrated AoI performance, compared with the traditional security check scheme. Moreover, with the help of the cooperative security check scheme, we detect and defense the Sybil attack against the train-centric CBTC systems with much higher probability.
Xiaoxuan Wang; Lingjia Liu; Li Zhu; Tao Tang. Joint Security and QoS Provisioning in Train-Centric CBTC Systems Under Sybil Attacks. IEEE Access 2019, 7, 91169 -91182.
AMA StyleXiaoxuan Wang, Lingjia Liu, Li Zhu, Tao Tang. Joint Security and QoS Provisioning in Train-Centric CBTC Systems Under Sybil Attacks. IEEE Access. 2019; 7 (99):91169-91182.
Chicago/Turabian StyleXiaoxuan Wang; Lingjia Liu; Li Zhu; Tao Tang. 2019. "Joint Security and QoS Provisioning in Train-Centric CBTC Systems Under Sybil Attacks." IEEE Access 7, no. 99: 91169-91182.
Communication-based train control (CBTC) systems are automated train control systems using wireless communications. The normal operation of CBTC systems depends on a reliable data communication system. The existing CBTC data communication system are based on train-to-ground communication, which suffers from too many subsystem interfaces and makes ground equipment extremely complicated. In next generation CBTC systems, train-to-train communication is adopted with the advantages of higher efficiency and briefer system structure. Although train-to-train communication based CBTC systems has been implemented in urban rail systems. The data communication system reliability has not been studied before. In this paper, the next generation CBTC data communication system reliability is modeled with Deterministic and stochastic Petri nets (DSPNs). We design a next generation CBTC data communication system using LTE technologies, and set up laboratory test environment to test system performance. The performance data is converted as DSPN model parameters to evaluate system reliability. With the tested system performance measures, we evaluate the reliability of data communication scenarios, which includes the reconnection and handoff scenarios. Extensive simulation results are then given to illustrate the system reliability, which considers both the train to train communication link and train to ground communication link.
Li Zhu; Dingyi Yao; Hongli Zhao. Reliability Analysis of Next-Generation CBTC Data Communication Systems. IEEE Transactions on Vehicular Technology 2018, 68, 2024 -2034.
AMA StyleLi Zhu, Dingyi Yao, Hongli Zhao. Reliability Analysis of Next-Generation CBTC Data Communication Systems. IEEE Transactions on Vehicular Technology. 2018; 68 (3):2024-2034.
Chicago/Turabian StyleLi Zhu; Dingyi Yao; Hongli Zhao. 2018. "Reliability Analysis of Next-Generation CBTC Data Communication Systems." IEEE Transactions on Vehicular Technology 68, no. 3: 2024-2034.
Li Zhu; Fei Richard Yu; Victor C. M. Leung; Hongwei Wang; Cesar Briso-Rodríguez; Yan Zhang. Communications and Networking for Connected Vehicles. Wireless Communications and Mobile Computing 2018, 2018, 1 -4.
AMA StyleLi Zhu, Fei Richard Yu, Victor C. M. Leung, Hongwei Wang, Cesar Briso-Rodríguez, Yan Zhang. Communications and Networking for Connected Vehicles. Wireless Communications and Mobile Computing. 2018; 2018 ():1-4.
Chicago/Turabian StyleLi Zhu; Fei Richard Yu; Victor C. M. Leung; Hongwei Wang; Cesar Briso-Rodríguez; Yan Zhang. 2018. "Communications and Networking for Connected Vehicles." Wireless Communications and Mobile Computing 2018, no. : 1-4.
Urban rail transit plays an increasingly important role in urbanization processes. Communications-Based Train Control (CBTC) Systems, Passenger Information Systems (PIS), and Closed Circuit Television (CCTV) are key applications of urban rail transit to ensure its normal operation. In existing urban rail transit systems, different applications are deployed with independent train ground communication systems. When the train ground communication systems are built repeatedly, limited wireless spectrum will be wasted, and the maintenance work will also become complicated. In this paper, we design a network virtualization based integrated train ground communication system, in which all the applications in urban rail transit can share the same physical infrastructure. In order to better satisfy the Quality of Service (QoS) requirement of each application, this paper proposes a virtual resource allocation algorithm based on QoS guarantee, base station load balance, and application station fairness. Moreover, with the latest achievement of distributed convex optimization, we exploit a novel distributed optimization method based on alternating direction method of multipliers (ADMM) to solve the virtual resource allocation problem. Extensive simulation results indicate that the QoS of the designed integrated train ground communication system can be improved significantly using the proposed algorithm.
Li Zhu; Fei Wang; Hongli Zhao. QoS-Aware Resource Allocation for Network Virtualization in an Integrated Train Ground Communication System. Wireless Communications and Mobile Computing 2018, 2018, 1 -12.
AMA StyleLi Zhu, Fei Wang, Hongli Zhao. QoS-Aware Resource Allocation for Network Virtualization in an Integrated Train Ground Communication System. Wireless Communications and Mobile Computing. 2018; 2018 ():1-12.
Chicago/Turabian StyleLi Zhu; Fei Wang; Hongli Zhao. 2018. "QoS-Aware Resource Allocation for Network Virtualization in an Integrated Train Ground Communication System." Wireless Communications and Mobile Computing 2018, no. : 1-12.
Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
Li Zhu; Fei Richard Yu; Yige Wang; Bin Ning; Tao Tang. Big Data Analytics in Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 383 -398.
AMA StyleLi Zhu, Fei Richard Yu, Yige Wang, Bin Ning, Tao Tang. Big Data Analytics in Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (1):383-398.
Chicago/Turabian StyleLi Zhu; Fei Richard Yu; Yige Wang; Bin Ning; Tao Tang. 2018. "Big Data Analytics in Intelligent Transportation Systems: A Survey." IEEE Transactions on Intelligent Transportation Systems 20, no. 1: 383-398.
Cheng Wang; Li Zhu. Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory. Journal of the Franklin Institute 2015, 352, 4624 -4637.
AMA StyleCheng Wang, Li Zhu. Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory. Journal of the Franklin Institute. 2015; 352 (10):4624-4637.
Chicago/Turabian StyleCheng Wang; Li Zhu. 2015. "Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory." Journal of the Franklin Institute 352, no. 10: 4624-4637.