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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.

This paper introduces an evolutionary algorithm which uses the concepts and principles of the quantum-inspired evolutionary approach and the hierarchical arrangement of the compartments of a P system. The P system framework is also used to formally specify this evolutionary algorithm. Extensive experiments are conducted on a well-known combinatorial optimization problem, the knapsack problem, to test the effectiveness of the approach. These experimental results show that this evolutionary algorithm performs better than quantum-inspired evolutionary algorithms, for certain arrangements of the compartments of the P system structure utilized.