The Analysis of Angle Resolution of Stress Vector Sensor Based on Optical Fiber Sensing Cable for High Speed Railway Traffic

With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress vector information is the important index to make more reasonable judgments about railway safety. However, information sensed by lots of commercial optical sensors is scalar. According to the stress filed distribution of rail, this paper proposes a new type of stress vector sensor based on optical fiber sensing cable (OFSC) with a symmetrical seven optical fibers structure and analyzes the relations between angle resolution and distance between adjacent of optical fibers through finite-element software (ANSYS) simulation. Through reasonable distance configuration, the angle resolution of the OFSC can be improved, and thus stress vector information, including the stress magnitude and the angle of stress, can be more accurately obtained. The simulation results are helpful to configure OFSC for angle resolution improvement in actual practice, and increase the safety factor in high speed railway structure health monitoring.     

Decision Model for Market of Performing Arts with Factorization Machine

Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However, they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine (FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent (ASGD) and Markov chain Monte Carlo (MCMC) are both explored to estimate the model parameters of FM. FM with ASGD (FM-ASGD) and FM with MCMC (FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models’ repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.     

Research on Equipment Support Activity Process Simulation Based on Monte Carlo Method

The influencing factors of the equipment support activity process have the characteristics of nonlinearity, high dimension, many constraints, random uncertainty and fuzzy uncertainty. Monte Carlo method can solve the above problems commendably. This paper analyzes the main equipment support activity process and establishes the sampling plan and simulation model of the medium maintenance process based on Monte Carlo method, and the simulation result verifies a fact that the medium maintenance time can be effectively reduced when parallel operation on some procedures is used. It has a practical value and can give good advice to achieve the capability of equipment supportability.     

Damage Detection for Simply Supported Bridge with Bending Fuzzy Stiffness Consideration

The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines (RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature (DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.     

Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function

Advanced Traveler Information Systems (ATIS) provide travelers with real time traffic information to optimize their travel choices. The objective of this paper is to model drivers' diversion from their normal routes in the provision of ATIS. Five different scenarios of traffic information are used. Generalized Estimating Equations (GEE) framework with repeated observations and binomial probit link function is introduced and implemented. GEE with four different correlation structures including the independent case are developed and compared with each other and with regular Maximum Likelihood Estimation (MLE). A travel simulator was used. Sixty-five subjects have traveled 10 simulated trial days each on a 40-link realistic network with real historical congestion levels. The results showed that providing traffic information increases the probability of drivers' diversion from their normal routes. Adding advice to the pre-trip and/or en-route information encourages drivers to divert. Providing en-route in addition to the pre-trip information with or without advice increases the diversion probability. High travel time on the normal route and less travel time on the diverted route increase the probability of diversion. High-educated drivers are less likely to divert. Expressway users are more likely to divert from their normal routes under ATIS. Drivers' familiarity with the device that provides the information and high number of traffic signals on the normal route increase the diversion probability.     

Airport management: a strategic approach

In this paper a Dynamic Balanced Scorecard (DBSC) is used for the main purpose of indicating strategy implementation avenues to managers so as to equip them with more efficient decision-making tools. To that end, Fuzzy Multicriteria Decision-Making methodology is used as a source of technical support in formulating a cause and effect system and fuzzy strategic indicators. This methodological instrument brings a strategic vision to performance analysis and is designed to furnish a tool for evaluating the impacts of management action on the BSC fuzzy indicators. The proposed analytical methodology is applied to Brazil’s seven main international airports.     

The effect of location,mobility and socio-demographic factors on task and time allocation of households

This paper investigates the role of location factors in task and time allocation at the household level. It is hypothesized that, if time constraints are less binding as a result of living in an urban area or owning more cars, spouses engage more often and longer in out-of-home activities and schedule their activities more independently. The hypotheses are tested with logistic and Cox regression models of activity participation and time allocation on a data set collected in the Amsterdam–Utrecht region in the Netherlands. Results suggest that the hypotheses are supported with respect to specific household activity scheduling decisions.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.     

Real Time Detection and Classification of Arrow Markings in Urban Streets

For highly automated driving in urban regions it is essential to know the precise position of the car. Furthermore it is important to understand the surrounding context in complex situations, e.g. multilane crossings and turn lanes. To understand those situations there is not only the task to detect the lane border, but to detect the painted information inside the lane. The paper is facing and evaluating two methods to classify this additional lane information. Therefore the images from five cameras mounted around the car are used. Four of them with fisheye lenses. The methods have in common, that the input images are transformed into a bird view projection. First introduced method is to extract contours from the transformed images and collect geometrical features and Fourier coefficients. The second introduced way, is to calculate histograms of oriented gradients and use it as input for the classification step. Both classification approaches are implemented and evaluated as multiclass and single class detectors for each arrow type. Furthermore, the classification results from a support vector machine and random forest were faced for this classification problem. The results from the multiclass detectors are evaluated and presented in form of confusion matrices. With the introduced approaches a high detection confidence could be achieved, proofed with validation datasets and in practical use.     

Lateral Collision Avoidance Robust Control of Electric Vehicles Combining a Lane-Changing Model Based on Vehicle Edge Turning Trajectory and a Vehicle Semi-Uncertainty Dynamic Model

This paper presents a new control scheme for lateral collision avoidance (CA) systems to improve the safety of four-in-wheel-motor-driven electric vehicles (FIWMD-EVs). There are two major contributions in the design of lateral CA systems. The first contribution is a new lane-changing model based on vehicle edge turning trajectory (VETT) to make vehicle adapt to different driving roads and conform to drivers’ characteristic, in addition to ensure vehicle steering safety. The second contribution is vehicle semi-uncertainty dynamic model (SUDM), which is SISO model. The problem of stability performance without the information on sideslip angle is solved by the proposed SUDM. Based on the proposed VETT and SUDM, the lateral CA system can be designed with H_∞ robust controller to restrain the effect of uncertainties resulting from parameter perturbation and lateral wind disturbance. Single and mixed driving cycles simulation experiments are carried out with CarSim to demonstrate the effectiveness in control scheme, simplicity in structure for lateral CA system based on the proposed VETT and SUDM.