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A creative and universal technique, magnetism and vortex current coupling growth (MVC), has been developed for the super-rapid mass-production of the layered MAX (Ti2AlC) phase. The book-like MAX grows from the bottom to the top layer by layer, separated by stacking faults to benefit the removal of the "A" layer from MAX.

Taxis are the most important components for public transit systems. The model of taxis is more flexible and convenient for passengers compared to other transit model like buses or railway. However, the dynamic of taxi locations will have influence on transit efficiency. Therefore, the hotspot selecting patterns when unoccupied with regards to different income level drivers were investigated in this study. Two month GPS data of 7200 taxis in Wuhan was used as the data samples. The Wuhan City was firstly Mapped Meshing and divided into 4623 grids. After preprocessing the taxi GPS data and taxi drivers' income level classification (top, ordinary and bottom), the pickup points were filtered and matched with all these grids. Heat and grid probability for passenger demand hotspot were proposed and analyzed in this study. The results showed that the correlation value between top drivers and heat are not always higher than ordinary taxi drivers but the correlation value between grid probability and top taxi drivers are always high at all time slots. The finding of this study reveals that the high income taxis drivers have the high ability for cruising to the closed hotspot having high grid probability compared with middle or low income drivers. It therefore recommended that, if such experience could be represented and learned by other taxi drivers, the efficiency of taxi transit systems will be improved. Furthermore, if the information of real time heat and grid probability of hotspot could be broadcasted to taxi drivers, it will be beneficial for taxi transit systems as well.

Ship motions are usually employed to analyze water transportation system. Interpolation of ship motions can be used for estimating the lost ship movement information, which is important for analyzing water traffic. Although some methods are proposed to interpolate ship trajectory, these methods are needed to improve interpolation accuracy. In addition, the course and speed of ship are not interpolated in previous studies. This article proposes a novel estimation algorithm for interpolating ship motions. First, the bilateral filtering is used to smooth the navigation trajectories, then a novel interpolation approach forecasts the vessel positions during the interpolation time period by taking into account the dynamic information of Automatic Identification System (AIS), and then forms the interpolation track of the moving vessel. In order to validate the proposed method, the AIS data of Yangtze River is collected from the AIS stations and the proposed restoration methods are verified by MATLAB under simulation. The results show that the proposed method can effectively remove the inaccurate ship motion data and interpolate the lost ship motion data.

In order to improve the accuracy of lateral and vertical target recognition in a car-following situation, the box plot is used to analyze and filter discrete points to overcome the LIDAR point data error; application of the modified adaptive K-means clustering algorithm which is based on the clustering evaluation index is applied to process the LIDAR point from LUX4; the candidate targets are output by clustering results. The test results show that the obstacle detection algorithm is more robust and reliable in the car-following situation.

With a high frequency of traffic incidents on the highways and urban freeways, it is urgent to make a response and dispatch the emergency vehicles as quickly as possible. The solution about how to isolate and protect the incident scenario becomes an issue. It means fast layout and recovery of road cones in a traffic incident scenario becomes an urgent problem. A research and design for a road cones vehicle is conducted then, it is used to handle the traffic incidents on site. The entire console was based on the pickup-car, the character of mobility for vehicle device is qualified and the console is installed into the carriage of the rear, and an electro-magnet mechanism is used to deliver road cones. At last, it is simulated by SolidWorks, and the console has received good recognition. The design of a road cones vehicle is targeted to be simple, efficient and reliable. It is able to be isolated quickly from the incident scenes to avoid harsh traffic jams, decrease traffic injuries, and it has played a key role that prevents the traffic situation from getting worse.

Waterway transportation accidents may cause great economic losses, casualties and environmental pollution. Traditional comprehensive index method is used to conduct safety assessment with fixed values, and can't reflect the randomness and uncertainty of risk. This paper analyzes several typical factors influencing the Yangtze River waterway transportation safety to identify four indexes for waterway transportation safety evaluation, and then the waterway transportation safety evaluation model is established through the method of fuzzy logic to analyze the Yangtze River waterway transportation accident data from 2002 to 2011. With the accurate evaluation results, this model is reliable. This study can be used to improve the management of Maritime Safety Administration.

To ensure traffic safety of highway off-ramp area, it should leave enough reaction time for drivers taking corresponding actions after traffic signs. And the recognition time for traffic signs depends on information volume. In this study, traffic sign recognition process in highway off-ramp were analyzed and visual recognition time model was set up. Then, the information volume contained in traffic signs was calculated using the information theory, and the information was divided it into four grades. In order to analyze the recognition time for different information grades, a simulation driving experiment was implemented. 20 participants took part in this experiment and the time for four grades was obtained. An evaluation approached was proposed, which identifies the safety conditions of highway off-ramp into four levels according to the time ranges after recognition. The proposed traffic safety evaluation method may provide a new way to evaluate the safety for highway off-ramp for its intuitive, easy operation and reasonable, especially for setting traffic signs information volume.

It is always a hot spot of the brain-computer field to achieve various functions by utilizing electroencephalogram (EEG) to control external equipment. This paper describes a design of an intelligent vehicle control platform based on brain-computer interface (BCI), which obtains EEG through BCI, then simulation model is established by Matlab /Simulink to extract and classify EEG as control command, which will be sent to intelligent vehicle by wireless serial port communication, so as to realize the control of intelligent vehicle on roads of the sandbox. By imaging four status: left hand, right hand, right leg and resting, the platform can accomplish the real-time control of intelligent vehicle on road of the sandbox, including turning left, turning right, going forward and stopping. The findings of that experiment show that the control system has good feasibility and stability, which lays the foundation for the practical application of the control of intelligent vehicle via EEG signal. The accomplishment of this platform provides a new approach to expanding and improving human capability to control external equipment.

This study presents an interval bus departure frequency programming model for traffic system pollution control through odd-even restrictions on private vehicles policy under uncertainties. The restriction policy results in enormous cost of the public transportation system especially in peak hours. The developed model can effectively quantify the relationship among system cost, execution time of the restriction policy and traffic environment by considering the uncertainties of traffic system. The uncertainties presented as interval numbers in the public transportation system can be quantified with the interval programming. The developed model is applied to Wuhan city, China. The results indicate that the efficiency of the program is significantly influenced by automobile ownership, the execution time of the policy and the level of emission. Besides, the program can easily be extended to all bus routes for vehicle emissions management.

In this paper, a method for vehicle driver’s intention recognition is investigated through an optimized hidden Markov model (HMM). As uncertainties exist between modeling and application of intention recognition with HMM, the Baum-Welch (B-W) algorithm is introduced to optimize and update the parameters of the established HMM. The major steps within the optimization process, such as acquisitions of forward probabilities, backward probabilities, maximum expected probabilities ratio, elements updates of matrixes π, A and B, and setups of recursion terminated condition, are fully described in the paper. An elaborate example presented at the end of the paper shows the credibility and effectiveness of the proposed algorithm.

With the development of computer, sensors, artificial intelligence technology, network, advanced automotive technology, the vehicle road test by a automatic evaluation system from the traditional manual evaluation has become an irresistible trend. Currently, the research of intelligent test system for vehicle road test in China has just started. Automatic evaluation module is core component of the system. In this study, according to the theory and technology of artificial neural network and the characteristics and requirements of the vehicle road test system, we introduce the one-way transmission of multi-layer neural network structure to the automatic evaluation module, discuss the principle, structure, transfer function and specific implementation of the application. Finally the proposed model was applicated in the vehicle road test system.

A new recognition method for driver's intention is proposed in this study. Genetic algorithm (GA) has strong adaptability, robustness and quick global searching ability. It has such disadvantages as premature convergence, low convergence speed and so on. Ant colony optimization (ACO) converges on the optimization path through pheromone accumulation and renewal. It has the ability of parallel processing and global searching and the characteristic of positive feedback. But the convergence speed of ACO is lower at the beginning for there is only little pheromone difference on the path at that time. The hybrid algorithm of genetic algorithm and ant colony optimization adopts genetic algorithm to give pheromone to distribute. And then it makes use of ant colony optimization to give the precision of the solution. It develops enough advantage of the two algorithms. The comparative analysis on optimal performance is made by using the Camel function. Finally, the method is used for the optimized the decision tree of driver's intention recognition. The experimental result shows that the recognition method and the hybrid algorithm are feasible and effective.

This paper proposes a novel algorithm, the Kalman Filter Embedded Trust Region (KFETR), for target tracking. Kalman filter and trust region are two successful methods for object tracking. The presented KFETR algorithm integrates the advantages of the two approaches. The new algorithm makes full use of the target's moving information and predicts the target's approximate position firstly. Because of these properties, the algorithm overcomes the problem that trust region converges to a local minimum which is not of interest caused by improper initial position. Promising experimental results on several image sequences demonstrate the robustness and effectiveness of KFETR.

The multi-agent system, which combines the cellular automata model, is of benefit to the simulations of large-scale events. Cars' movements were simulated using cellular automata model and their variability was introduced by superimposing agent. There were two submodels which based on the combined model. The first submodel simulated cars' movement in a park. The other submodel was proposed to simulate the congestion of mass cars on emergency channels around the park. Besides, the negotiation model was set to solve the problem of cars' position conflict. Moreover, drivers' attribution was introduced to this model. By setting these submodels, traffic flow time space dissipation models were analyzed. Compared with the investigation data, the results show that these submodels can simulate the evacuation process and have small errors.