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Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

Taxi demand prediction is valuable for the decision-making of online taxi-hailing platforms. Data-driven deep learning approaches have been widely utilized in this area, and many complex spatiotemporal characteristics of taxi demand have been studied. However, the heterogeneity of demand patterns among different taxi zones has not been taken into account. To this end, this paper explores zone clustering and how to utilize the inter-zone heterogeneity to improve the prediction. First, based on the pairwise clustering theory, a taxi zone clustering algorithm is designed by considering the correlations among different taxi zones. Then, both the cluster-level and the global-level prediction modules are developed to extract intra- and inter-cluster characteristics, respectively. Finally, a Multi-Level Recurrent Neural Networks (MLRNN) model is proposed by combining the two modules. Experiments on two taxi trip records datasets from New York City demonstrate that our model improves the prediction accuracy compared with other state-of-the-art methods.

This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers' waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands' co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.

Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects. In this paper, we propose a novel anchor-free method, called FII-CenterNet, which introduces the foreground information to eliminate the interference of the complex background information in traffic scenes. The foreground region proposal network segments the foreground based on boxes-induced segmentation annotation, and midground is proposed to provide rich edge information of the objects. In addition to foreground location, scale information is also introduced to improve the regression performance. Extensive experimental results on two public datasets verify the benefits of the introduction of the foreground information, and demonstrate that our FII-CenterNet achieves the state-of-the-art performance in both accuracy and efficiency.

IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance “intelligence” of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.

Traffic data analysis and mining are elemental functions of Intelligent Transportation Systems. In recent year, tremendous sensors are deployed in order to collect big data, and equipment maintenance costs a lot. With the development of deep learning, especially especially Generative Adversarial Networks, we can generate realistic big artificial traffic flow data and use small real traffic data and synthesized traffic data in traffic data mining tasks. In this paper, we focus on discovering the semantics embedded in latent codes which are fed into Generative Adversarial Networks, and propose to use the interpolation of semantic latent code to generate semantic manipulation of traffic flow. We evaluate our approach using the publicly available data from Caltrans Performance Measurements Systems (PeMS), and experimental results show the the effectiveness of the proposed method.

Online Ride-hailing System improves the efficiency of vehicle utilization and the urban transportation. However, the imbalance between supply and demand is still a problem. To solve this problem and improve resource utilization efficiency, a Decentralized Deep Reinforcement Learning Method (DDRL) for vehicle repositioning is proposed. In DDRL, each vehicle is modeled as an independent agent and dispatched according to its own state to rebalance its local supply and demand. Thus, the global rebalance problem is divided into many small local rebalance problems. First, a new reward evaluation method is proposed and the long-term global reward in traditional reinforcement learning is transformed into many short-term local rewards. Second, a unified algorithm is designed by learning all the decentralized agents' sample data. Finally, the weight matrix of the state is introduced to magnify the differences between the states of adjacent vehicles. Experiments are carried out and the effectiveness of DDRL is verified.

Visible images captured by maritime video surveillance system under hazy environment often suffer from low contrast and information loss, etc. These negative factors will significantly limit the development and progression of target recognition, detection, tracking, and other vision-based technologies in maritime applications. However, existing dehazing methods are mainly designed for massive inland images, resulting in inaccurate estimation of haze-free versions under the maritime scene. In this work, we propose a heterogeneous twin dehazing network (termed HTDNet) for enhancing visually-degraded maritime images. The HTDNet is mainly composed of two modules, i.e., twin feature extraction module (T-FEM) and feature fusion module (FFM). In particular, T-FEM is employed to collect coarse haze features from two views, and FFM is designed for feature integration and enhancement. To further improve the visual quality, the dataset containing massive maritime images is constructed to train our network. Both synthetic and real-world experiments have illustrated our superior performance compared with several state-of-the-art methods. There is thus a great potential to successfully extend our HTDNet to enable maritime intelligent transportation system.

Limited by insufficient illumination, the images collected by maritime imaging devices often suffer from low brightness, low contrast, low signal-to-noise ratio, severe information loss, and so on. The above problems restrict the development of maritime- related work such as intelligent supervision, collision warning, accident investigation, etc. To improve the imaging quality of maritime video images, we propose an efficient and effective multi-deep neural network (termed EEMNN) for low-visibility enhancement. In particular, we fuse the multi-scale information extracted from the encoder-decoder module using the dense blocks (DBs) and attention blocks (ABs). It is capable of enhancing the fused information leading to preserving the edges, textures, and other fine details. To prevent the overexposure of enhanced images, we fuse and reconstruct the output features of DBs and ABs with the raw low-light image to get the final enhanced image through two residual blocks (RBs). The mixing of multiple network modules can effectively improve the generalization ability and robustness of our network. Through extensive experiments, EEMNN has higher objective evaluation indicators, more efficient enhancement, more natural maritime scenes, and stronger detail-preservation capabilities compared with other enhancement methods.

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

Traffic prediction is an elemental function of Intelligent Transportation Systems (ITS), and accurate and timely prediction is significant for both proactive traffic control and providing traveler information. In this paper, we focus on investigating ensemble leaning that benefits from different base models, and propose a traffic-condition-awareness ensemble approach. We apply graph convolution on the network of traffic detectors to capture the spatial patterns embedded in traffic flow. Then, the extracted features are used to formulate a weight matrix to ensemble the predictions of base models according to their performances under a certain condition. We performed a series of experiments on a real dataset to compare the proposed methods with several competitive models, including ensemble methods: Weight Regression model and Gradient Boosting Regression Tree model, and single model approach: Support Vector Regression (SVR), Long Short-term Memory (LSTM) model and Historical Average Model model. Experimental results demonstrate that our method can significantly improve the performances of traffic flow prediction.

Cyber-Physical-Social Systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

Intelligent traffic signal control is necessary for improving traffic efficiency. These fast changing and challenging traffic scenarios and demands are generally handled by professional traffic engineers. However, it may take years of time and thousands of practices to train such an engineer. This paper proposes a deep behavioral cloning method to learn how to control the traffic signal effectively and efficiently from virtual expert demonstration. The method imitates promising working behavior of optimized offline solutions, and applies it to solve online traffic signal control problems of the similar scenario. Different traffic demand patterns are generated through a combination of different kinds of components. Then the virtual demonstration is constructed by getting an exclusive and optimized solution for each generated virtual traffic demand pattern through a heuristic random search method. After that, a deep neural network-based behavioral cloning method is employed to learn from the virtual demonstration and finish on-line traffic signal control task. The experimental results show that compared with other methods, the proposed method significantly reduces the waiting time and time loss in different situations. And the average traffic speed of the road network at different saturation levels can be improved by 1.58% to 11.54%.

Asset Administration Shell (AAS) is the core concept for modeling and designing manufacturing components in Digital Twins applications. AAS describes the properties of assets such as products, machines, devices and factories, and communicates with other AAS through the network. Therefore, AAS enhances the interoperability and communications among device and software components. To further advance the interoperability and generality among components of a production line, this paper proposes a general platform for designing a production line based on AAS. The platform enables to automatically build a production line so that it flexibly meets different needs for various scenarios.

How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly consists of three blocks, including the local polar representation block, the dual-distance attentive pooling block, and the global contextual feature block. For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud. The proposed module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture, called SCF-Net in this work. Extensive experimental results on two public datasets demonstrate that the proposed SCF-Net performs better than several state-of-the-art methods in most cases.

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

Traffic signal control plays an essential role in the Intelligent Transportation Systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has to rely on human engineers to cope with the complicated and challenging traffic control and operation problem, which cannot be handled well by the traditional methods alone. Thus, imitating the good working experience of engineers to solve traffic signal control problems remains a practical, smart, and cost effective approach. In this paper, we construct a modelling framework to imitate how engineers cope with complex scenarios through learning from the historical record of manipulations by traffic operators. To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, average time loss of vehicles, and vehicle throughput by 6.6%, 7.2%, and 6.85%, respectively.

The accuracy of traffic flow prediction significantly impacts the operation of Intelligent Transportation Systems (ITS). In this paper, we propose a Differential Time-variant (DT) Traffic Flow Prediction method, which can remarkably improve the accuracy and reduce the variance of traffic flow forecast based on deep learning models. To extract the temporal trend of the traffic flow at different locations, we apply data difference to preprocess the raw traffic data. This method can better eliminate the uncertainties of traffic flow series like volatility and anomaly. Then, time information is introduced in the form of One-Hot Encoding to effectively model the temporal patterns of traffic flow. Necessary analysis is presented to demonstrate the rationality. Three popular deep neural networks are applied to test our method, and experimental results on PeMS data sets indicate that it can make more accurate prediction compared with the same model.

Road detection is fundamental for many applications, especially vision-based autonomous driving systems. To improve the accuracy of the detection results, most of previous research focus on designing feature encoders and classifiers. In this paper, a road detection method is proposed based on ensemble learning and key samples focusing. A road detection network is designed, which integrates classification results based on different feature combinations by weighted voting. The outputs of the network are further processed by morphological transformation. To focus on key samples, a novel loss function is proposed. The loss function can attach importance to hard samples and pay different attention to missed detection and false detection. The method is evaluated on KITTI dataset, and its effectiveness is verified.

The distribution static compensator (D-STATCOM) is a power quality compensator, which can be utilized for improving the power quality of the distribution power grid by managing the flow of reactive power and unbalanced caused by variable and unbalanced loads. This paper develops the concept of regulating the D-STATCOM scheme to improve the dynamic proficiency of the power distribution network during electrical network defects, such as voltage drops and voltage swells. Furthermore, the methodology adopted in this research paper is established to utilize the sliding mode control (SMC) for the proper working of D-STATCOM. The simulation of the suggested SMC methodology has been carried out by employing the MATALB/Simulink software and compared the results with traditional proportional integrator regulator. The results show that the suggested approach controlling the D-STATCOM achieves sinusoidal and symmetrical grid current with less harmonics; moreover, it can efficiently eliminate the oscillations produced in active and reactive power. Hence, the proposed strategy is helpful in mitigating the voltage flicker and improving the overall efficiency of the distribution grid.

This paper is to discuss the state, trend, and frontiers of development of cyber-physical-social systems (CPSSs) in China. The demand for developing CPSS is discussed in detail, followed by the Artificial societies, Computational experiments, Parallel execution (ACP) approach for CPSS and knowledge automation. The development of ACP based on CPSS in transportation, energy, information, Internet of Things, and Internet of Minds (IoM) is discussed to demonstrate the cutting-edge applications in CPSS. Finally, the blockchainized IoM technology and the concepts of parallel society are described. This paper will contribute to the transition from the current social construct to a futuristic intelligent society.

In this modern era, technology has advanced to such an extent to make our daily life more proficient and convenient. This paper presents the design and implementation of vending machine (VM) for students, which operates on radio frequency identification technology (RFID). Hence, it turns the VM into smart shopping booth (SSB). The SSB gives the access only through RFID card and vend different products according to the desire of student. The SSB is equipped with RFID reader and student identity card that holds the RFID tag. When the student identity card is scanned for verification and then after selecting the required products, will be dispensed off if a student has available balance in his/her identity card and SMS will be sent to students about remaining balance in their cards by GSM technology. The programming for controller is done by using proton IDE language. The SSB is the customized idea of vending machine and it attempts to resolve various issues that appeared in coin/note based VM. The student identity card RFID based technology is a breakthrough towards a paperless and cashless environment. Moreover, it is very beneficial in saving the precious time of students due to fast paced life and also user friendly. RFID based SSB also improved the communication and sensing capabilities of VM. Hence, our suggested approach outperforms in all aspects.

Intelligent transportation systems (ITS) have developed for a long time. The rise of deep learning has brought new vitality of the ITS. However, traffic flow data is usually time-correlated and highly randomized. The data distribution will also change dynamically. To actualize the forecasting of traffic flow accurately, we use the historical traffic information to predict the messages of the traffic flow at any time interval. This paper proposes an efficient traffic flow forecast architecture based on deep learning. The method combines the gated recurrent unit (GRU, a type of recurrent neural network) layers and one-dimension convolution layers. Since the performance of these models has a strong dependence on hyper-parameters, this paper conducts a large-scale search of the hyper-parameter space. At the same time, experiments on flow data show that the method proposed in this paper can achieve a better prediction accuracy. Experiments also show lower test errors compared with the existing approaches.

Personal identification has been used more and more widely in information society. Iris recognition, as one of numerous identification methods, has already been applied in many situations, because the iris patterns have stable, unique and non-invasive features. We analyze the general process of iris recognition in this paper, including image preprocessing, feature extraction, classification and recognition. We also study the progress in iris recognition brought by deep learning method. It is our belief that traditional approaches play a very significant role in iris recognition field, while deep learning methods will make a great difference to iris recognition.

Traffic flow prediction is an important functional component of Intelligent Transportation Systems (ITS). In this paper, we propose a hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA), to efficiently capture the spatial-temporal features in traffic flow. Firstly, we apply graph convolutional network (GCN) to mine the spatial relationships of traffic flow over multiple observation stations, in which the adjacent matrix is determined by a data-driven approach. Then, we feed the output of the GCN model to the long short-term memory (LSTM) model which extracts temporal features embedded in traffic flow. Further, we implement a soft attention mechanism on the extracted spatial-temporal traffic features to make final prediction. We test the proposed method over the PeMS data sets. Experimental results show that the proposed model performs better than the competing methods.

With the advent of awareness toward quality of life by people has kindled a widespread investment and concern in science community for a better biomedical product and new technologies. Biomedical health is no longer limited to pharmaceutical drugs but monitoring of daily body vitals for prevention and improved diagnostics is getting a lot of attentions. In this paper, we are going to use wireless body area network technology (WBANT) for monitoring a patient's condition in a given system. With emerging practical use of WBANT, scientists have proposed many innovative ways for health and body vitals monitoring such as channel state information (CSI) and receiving signal strength indication (RSSI). CSI can characterize the multipath propagation of signal to some extent in comparison to RSSI method. So, we propose a system design for identification of narcolepsy disease combining wireless communication technology and computer science analytics. This system setup continuously transmits a particular frequency signal and the receiver obtains the reflected signal containing the patient's information in a given environment with changing body postures in real time. The various path gain data collected is used to extract and analyze characteristics of the patient position characterizing the disease of narcolepsy.

The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework.

The public transportation system is an essential part of the life of the citizens and it’s the basis of intelligent transportation system(ITS). This paper tries to predict shortterm bus passenger flow by using deep learning approach that called SAE model and DBN model. The model training and evaluation were carried out using the credit card records of the Suzhou bus IC card. The experimental results show that the SAE and DBN models can reduce the prediction error by 9.51% and 10.48%, respectively, compared with the traditional method. The methods of deep learning show a good application prospect in the short-term bus passenger flow forecasting.

In this paper, we introduce the combination of thermostat control strategy (TCS) and DC-link voltage control strategy to improve fuel economy for series hybrid electric vehicles (HEVs). The main method is tuning essential parameters, including a parameter under DC-link voltage control and a selected constant as an important judgement condition of charging operation modes under TCS. The minimum mass points of equivalent fuel consumption (EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure (WLTP). By analysis and verification, the fuel economy of series HEVs with WLTP performs better with the combination control schemes than individual control scheme, so this scheme proves very effective for series HEVs driving in an urban environment.

The rapid development of the Cloud Computing Technologies (CCTs) has amended the conventional design of resource-constrained Network Control System (NCS) to the powerful and flexible design of Cloud-Based Networked Control System (CB-NCS) by relocating the processing part to the cloud server. This arrangement has produced many internets based exquisite applications. However, this new arrangement has also raised many network security challenges for the cloud-based control system related to cyber-physical part of the system. In the absence of robust verification methodology, an attacker can launch the modification attack in order to destabilize or take control of NCS. It is desirable that there shall be a solution authentication methodology used to verify whether the incoming solutions are coming from the cloud or not. This paper proposes a methodology used for the verification of the receiving solution to the local control system from the cloud using Karush-Kuhn-Tucker (KKT) conditions, which is then applied to actuator after verification and thus ensure the stability in case of modification attack.

Forest fires are serious disasters that endanger the safety and property of residents. To deal with the arson-caused forest fires, we use security game theory to generate optimal patrol strategy under limited patrolling resources. The existing security game algorithms focus on a single behavior model to prevent attacks, while our work is to incorporate the secondary damage caused by the attack behavior into the optimization object. For forest fires, the security game algorithm only expects to catch arsonists in the forest, which is unreasonable in our opinion. Our algorithm incorporates the spread of fires into patrolling path planning so that defenders avoid being swallowed by fire via capturing the spread of it.

Software simulation and real environment running parallel execution is a lately proposed method, which also provides full coverage and convenience to accomplish autonomous driving education purpose. This paper introduces a new high-school iSTEM program of autonomous vehicles education. This program uses a Scaled RC-Car platform with several sensors and Raspberry Pi embedded platform, to build an autonomous driving car in scaled indoor simulation environment. The RC-Car is capable of safely autonomous driving. Many existing algorithms are put together to provide the necessary functions of autonomous driving, such lane detection, obstacle detection, lane following, vehicle control etc. In this paper, we provide the details of this program, hardware and software components of the RC-Car, Deep learning end-to-end approaching of algorithm deployment, and future works.

For personalized design it is important to be able to collect, measure and evaluate individual properties of human beings. This paper proposes registration for the point clouds during foot 3D scanning. In the experiment, we get the point clouds of the human foot from the Artec 3D scanner and complete the registration of the point clouds from different visual angles. Dealing with the customized footwear, we choose a novel algorithm, which combines the NARF key point detector and the FPFH descriptor, to improve the efficiency of the initial iteration and reduce the computation burden of matching process.

A new combinatorial algorithm based on the characteristics of wavelet transform, particle swarm optimization algorithm and BP neural network for short-term traffic flow forecasting was presented in this paper. Firstly, using wavelet transform, we do multi-resolution decomposition of traffic flow data and single branch reconstruction on its original scale, then apply the particle swarm optimized BP neural network to forecast on each reconstruction sequence, at last, add all the forecasting results to the final short-term traffic flow forecasting results. The experimental results show that, comparing to the traditional particle swarm optimized BP neural network and traditional BP neural network, the new algorithm significantly improves the forecasting accuracy of short-term traffic flow, has a broad application prospect and is more suitable for forecasting short-time traffic flow data which contains much more noise or has a serrated signal curve.

With the rapid development of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications (V2X communications), reliable and low-complexity cooperative communication techniques are becoming more and more important due to the requirements of signal reliability and transmission delay for vehicles. Therefore, in this paper, an estimated relaying scheme based on soft information is proposed for cooperative vehicular networks, named by estimate-and-forward (EF) strategy. The proposed EF relay forwards the unconstrained minimum mean square error (MMSE) estimate of the received signal at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which outperforms amplify-and-forward (AF) in terms of performance, and achieves lower complexity than decode-and-forward (DF). Therefore, the proposed EF relay achieves a better trade-off between performance and complexity compared to the conventional AF and DF. Simulation results confirm the advantages of the proposed EF for cooperative vehicular networks.

Nowadays, with the fast growth of Internet of Vehicles (IoV), wireless communication techniques for IoV is becoming more and more important for vehicular networks. To achieve more reliable information and reduce the computational complexity, receiver detection is one of the most significant techniques. In this paper, several modified K-best detection algorithms are developed to take advantages of the list decoder, which provide a flexible performance and complexity tradeoff. Different from the existing algorithms, the proposed K-best algorithms could make use of the a priori probability to generate the list. Simulation results demonstrate that the proposed low complexity algorithms can achieve a significant performance gain over existing ones, especially for the networks with high order constellations.

Intelligent video surveillance technology has changed the traditional passive reception mode. It can real-time, automatically and intelligently analyze video data to improve its efficiency and reliability. This paper presents a set of efficient expression and deep analysis platform for massive traffic video, which can connect the surveillance cameras from urban transport systems, and perceive the state of people, vehicles, roads and other elements using intelligent visual perception technology, and extract relevant semantic structure information from the non-structured video content and display it in structured text language. Then, its main contents and key technologies are analyzed. Finally, an application case in Guangzhou, China is given. The system can be used for intelligent expression, deep analysis, detection, identification, archiving and management of massive traffic video data, to provide a complete information services for the managers and users.

Autonomous vehicle testing is important. Testing tools are developed to carry out simulation or real field autonomous driving testing, which are both common used methods. However, real field on-road testing is with low efficiency and limited ability to test all critical scenarios due to safety and costs involved. How to accelerate the testing and achieve the full coverage is the goal of this research. Therefore, use the simulation methods to achieve the full coverage test, especial the corner cases, use the real field test methods to do the importance sampling verification, obtain the parameters estimate through iteration of testing data, and then the functional testing of Autonomous vehicle can be accomplished during the develop stage. This paper discusses the autonomous driving reliability accelerated testing in parallel paradigm and proves the importance of this method, which is an appropriate reference for autonomous vehicle testing.

A streamlined parallel traffic management system (PtMS) is outlined that works alongside a redesigned intelligent transportation system in Qingdao, China. The PtMS's structure provides enhanced control and management support, with increased versatility for use in real-world scenarios.

The Artificial societies, Computational experiments, and Parallel execution (ACP) approach provides us an opportunity to look into new methods that address transportation problems from new perspectives. In this paper, we present our work and results of applying the ACP approach on modeling and analyzing transportation systems, particularly carrying out computational experiments based on artificial transportation systems (ATSs). Two aspects in the modeling process are analyzed. The first is growing an ATS from the bottom up using agent-based technologies. The second is modeling environmental impacts under the principle of “simple is consistent.” Finally, three computational experiments are carried out on one specific ATS, i.e., Jinan-ATS, and numerical results are presented to illustrate the applications of our method.

Artificial Transportation Systems (ATS) can support a variety of computational experiments for different purposes, and have become important tools for transportation research. However, when the road network modeled in ATS is large or there are a lot of vehicles, ATS will run very slow or even cannot be started due to the memory limit of a standalone computer. With the acceleration of urbanization process and rapid increase of car ownership, it is necessary to find a high-performance computing method for running large-scale ATS. Therefore, the paper presents an Erlang-based approach to realize concurrent and distributed computing of ATS. To verify the feasibility, a prototype is built, and a performance test is conducted. The results show the method can achieve the efficiency utilization of computing resources.

Short message is one of the most common communication media for mobile subscribers, so major mobile operators are devoted to improve their Short Message Service (SMS). However, the annoying and undesired messages, also named message spam or simply spam, not only worsen the users' experience, but also cause their complaints on SMS. In this paper, we present a novel Chinese SMS spam filtering framework based on AdaBoost algorithm to provide accurate and effective short messages classification. Three content-based weak filters are introduced to boost the performance of final classification decision. Results from Receiver Operating Characteristics (ROC) analysis prove the proposed method has such advantages as higher efficiency and fewer parameters over those established SMS spam filtering methods. The application of the proposed method is expected to block the most spam for mobile subscribers and improve the service quality of SMS. With simple data processing and few training parameters, the proposed method can be applied into the practice of short text classification.

To overcome public transportation problems during the 16th Asian Games held in Guanhzhou China, a PtMS (Parallel Transportation Management System), a novel application of Intelligent Transportation Systems, was introduced for effective and convenient traffic management. Results show that PtMS has successfully enhanced public traffic management, raising it from experience-based policy formulation plus manual implementation to scientific computing-based policy generation plus implementation with intelligent systems.

One of the major challenges facing the 2010 Asian Games is the transportation problem. Because the games will use 58 existing game facilities and 12 new sports stadiums, which are located across the Guangzhou metropolitan areas (see Figure 1), safe and effective traffic control and transportation management will be essential to their success (see http://www.gz2010.cn/en/). The city already has a big issue with road congestion, caused mainly by the inadequate supply of transportation infrastructures. For example, between 2001 and 2008, the number of private passenger vehicles in Guangzhou increased 529 percent to 783 thousand, an annual increase of 26.9 percent. However, only 193.1 km are scheduled to be added to the total length of metropolitan road networks between 2007 and 2010, which is an increase of 5,528 km, a mere 3.6 percent. By 2010, the number of private passenger vehicles will increase to an estimated 1.261 million, but the space for new roads and transportation infrastructures is limited.1 Under such time and infrastructure constraints, Guangzhou has chosen intelligent transportation systems (ITS) to enhance and improve its traffic safety and efficiency for a better 2010 Asian Games.

Reasonable timing design for traffic light can induce and maintain the transportation systems in good order. How to allocate the time are the keys. In the paper, the theory of time-varying universe is used to describe the circle time, and corresponding fuzzy sets on the universe are also discussed to modeling the situation of traffic flow, then the parallel traffic management and control methods which are dynamic with the time change are presented. A simulation example are provided to analyze the linguistic dynamic evolution of timing design of traffic light when the traffic flow is change with time-varying for an intersection.

The ACP (Artificial Societies, Computational Experiments, and Parallel Execution) approach has provided us with an opportunity to look into new methods in addressing transportation problems from new perspectives. In this chapter we present our works and results of applying the ACP approach in modeling and analyzing transportation systems, especially carrying out computational experiments based on artificial transportation systems. Two aspects in the modeling process are analyzed. The first is growing an artificial transportation system from bottom up using agent-based technologies. The second is modeling environmental impacts on a simple-is-consistent principle. Then the implementation architecture based on cloud computing is introduced. Finally, three computational experiments are carried out on one specific ATS, Jinan ATS, and numerical results are presented to illustrate the applications of our method.

Based on psychological experiments and psychophysical mechanisms, it is possible to acquire manufacturing workshop operator's behavior and cognitive situations. A comprehensive optimization model for complex industrial processes is proposed by integrating expert operator's behavior and electrophysiological characters. By using the proposed multiple layer modeling method includes process control, manufacturing and management factors, a human-in-the-loop control system can be created including the situation awareness of manufacturing process and workshop operator cognition.