Computational Intelligence and Neuroscience The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm Mon, 26 Jan 2015 13:05:15 +0000 Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. Zhehuang Huang and Yidong Chen Copyright © 2015 Zhehuang Huang and Yidong Chen. All rights reserved. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network Mon, 26 Jan 2015 09:37:31 +0000 In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. Zhijia Chen, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng Copyright © 2015 Zhijia Chen et al. All rights reserved. An Extended Affinity Propagation Clustering Method Based on Different Data Density Types Wed, 21 Jan 2015 14:20:59 +0000 Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself. XiuLi Zhao and WeiXiang Xu Copyright © 2015 XiuLi Zhao and WeiXiang Xu. All rights reserved. Traffic Signal Synchronization in the Saturated High-Density Grid Road Network Tue, 13 Jan 2015 06:33:15 +0000 Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN. Xiaojian Hu, Jian Lu, Wei Wang, and Ye Zhirui Copyright © 2015 Xiaojian Hu et al. All rights reserved. Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial Tue, 13 Jan 2015 05:52:29 +0000 Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. Zhen Yang, Wei Wang, Shuyan Chen, Haoyang Ding, and Xiaowei Li Copyright © 2015 Zhen Yang et al. All rights reserved. Study Partners Recommendation for xMOOCs Learners Mon, 12 Jan 2015 13:08:56 +0000 Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners’ behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners’ course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information. Bin Xu and Dan Yang Copyright © 2015 Bin Xu and Dan Yang. All rights reserved. Analysis of the Seismic Performance of Isolated Buildings according to Life-Cycle Cost Mon, 05 Jan 2015 13:31:24 +0000 This paper proposes an indicator of seismic performance based on life-cycle cost of a building. It is expressed as a ratio of lifetime damage loss to life-cycle cost and determines the seismic performance of isolated buildings. Major factors are considered, including uncertainty in hazard demand and structural capacity, initial costs, and expected loss during earthquakes. Thus, a high indicator value indicates poor building seismic performance. Moreover, random vibration analysis is conducted to measure structural reliability and evaluate the expected loss and life-cycle cost of isolated buildings. The expected loss of an actual, seven-story isolated hospital building is only 37% of that of a fixed-base building. Furthermore, the indicator of the structural seismic performance of the isolated building is much lower in value than that of the structural seismic performance of the fixed-base building. Therefore, isolated buildings are safer and less risky than fixed-base buildings. The indicator based on life-cycle cost assists owners and engineers in making investment decisions in consideration of structural design, construction, and expected loss. It also helps optimize the balance between building reliability and building investment. Yu Dang, Jian-ping Han, and Yong-tao Li Copyright © 2015 Yu Dang et al. All rights reserved. AITSO: A Tool for Spatial Optimization Based on Artificial Immune Systems Mon, 05 Jan 2015 08:42:39 +0000 A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis. Xiang Zhao, Yaolin Liu, Dianfeng Liu, and Xiaoya Ma Copyright © 2015 Xiang Zhao et al. All rights reserved. New Algorithms for Computing the Time-to-Collision in Freeway Traffic Simulation Models Wed, 31 Dec 2014 13:22:59 +0000 Ways to estimate the time-to-collision are explored. In the context of traffic simulation models, classical lane-based notions of vehicle location are relaxed and new, fast, and efficient algorithms are examined. With trajectory conflicts being the main focus, computational procedures are explored which use a two-dimensional coordinate system to track the vehicle trajectories and assess conflicts. Vector-based kinematic variables are used to support the calculations. Algorithms based on boxes, circles, and ellipses are considered. Their performance is evaluated in the context of computational complexity and solution time. Results from these analyses suggest promise for effective and efficient analyses. A combined computation process is found to be very effective. Jia Hou, George F. List, and Xiucheng Guo Copyright © 2014 Jia Hou et al. All rights reserved. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables Wed, 31 Dec 2014 08:10:05 +0000 In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. Feng Zhong-xiang, Lu Shi-sheng, Zhang Wei-hua, and Zhang Nan-nan Copyright © 2014 Feng Zhong-xiang et al. All rights reserved. Context Transfer in Reinforcement Learning Using Action-Value Functions Wed, 31 Dec 2014 00:10:41 +0000 This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents’ MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is -learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The -values learned during the learning process of the source tasks are mapped to the sets of -values for the target task. These transferred -values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task. Amin Mousavi, Babak Nadjar Araabi, and Majid Nili Ahmadabadi Copyright © 2014 Amin Mousavi et al. All rights reserved. A Study of Driver’s Route Choice Behavior Based on Evolutionary Game Theory Mon, 29 Dec 2014 11:30:50 +0000 This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers’ route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver’s route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver’s route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent. Xiaowei Jiang, Yanjie Ji, Muqing Du, and Wei Deng Copyright © 2014 Xiaowei Jiang et al. All rights reserved. Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine Mon, 29 Dec 2014 10:32:43 +0000 This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. Jingyu Zhou, Shulin Tian, Chenglin Yang, and Xuelong Ren Copyright © 2014 Jingyu Zhou et al. All rights reserved. Image Superresolution Reconstruction via Granular Computing Clustering Sun, 28 Dec 2014 13:29:22 +0000 The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso. Hongbing Liu, Fan Zhang, Chang-an Wu, and Jun Huang Copyright © 2014 Hongbing Liu et al. All rights reserved. A Study on Urban Road Traffic Safety Based on Matter Element Analysis Mon, 22 Dec 2014 07:11:18 +0000 This paper examines a new evaluation of urban road traffic safety based on a matter element analysis, avoiding the difficulties found in other traffic safety evaluations. The issue of urban road traffic safety has been investigated through the matter element analysis theory. The chief aim of the present work is to investigate the features of urban road traffic safety. Emphasis was placed on the construction of a criterion function by which traffic safety achieved a hierarchical system of objectives to be evaluated. The matter element analysis theory was used to create the comprehensive appraisal model of urban road traffic safety. The technique was used to employ a newly developed and versatile matter element analysis algorithm. The matter element matrix solves the uncertainty and incompatibility of the evaluated factors used to assess urban road traffic safety. The application results showed the superiority of the evaluation model and a didactic example was included to illustrate the computational procedure. Qizhou Hu, Zhuping Zhou, and Xu Sun Copyright © 2014 Qizhou Hu et al. All rights reserved. Application of Wireless Intelligent Control System for HPS Lamps and LEDs Combined Illumination in Road Tunnel Sun, 21 Dec 2014 00:10:18 +0000 Because of the particularity of the environment in the tunnel, the rational tunnel illumination system should be developed, so as to optimize the tunnel environment. Considering the high cost of traditional tunnel illumination system with high-pressure sodium (HPS) lamps as well as the effect of a single light source on tunnel entrance, the energy-saving illumination system with HPS lamps and LEDs combined illumination in road tunnel, which could make full use of these two kinds of lamps, was proposed. The wireless intelligent control system based on HPS lamps and LEDs combined illumination and microcontrol unit (MCU) Si1000 wireless communication technology was designed. And the remote monitoring, wireless communication, and PWM dimming module of this system were designed emphatically. Intensity detector and vehicle flow detector can be configured in wireless intelligent control system, which gather the information to the master control unit, and then the information is sent to the monitoring center through the Ethernet. The control strategies are got by the monitoring center according to the calculated results, and the control unit wirelessly sends parameters to lamps, which adjust the luminance of each segment of the tunnel and realize the wireless intelligent control of combined illumination in road tunnel. Jinxing Lai, Junling Qiu, Jianxun Chen, Yaqiong Wang, and Haobo Fan Copyright © 2014 Jinxing Lai et al. All rights reserved. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis Thu, 18 Dec 2014 09:23:55 +0000 Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA. Lili A. Wulandhari, Antoni Wibowo, and Mohammad I. Desa Copyright © 2014 Lili A. Wulandhari et al. All rights reserved. Freeway Travel Speed Calculation Model Based on ETC Transaction Data Mon, 15 Dec 2014 00:10:25 +0000 Real-time traffic flow operation condition of freeway gradually becomes the critical information for the freeway users and managers. In fact, electronic toll collection (ETC) transaction data effectively records operational information of vehicles on freeway, which provides a new method to estimate the travel speed of freeway. First, the paper analyzed the structure of ETC transaction data and presented the data preprocess procedure. Then, a dual-level travel speed calculation model was established under different levels of sample sizes. In order to ensure a sufficient sample size, ETC data of different enter-leave toll plazas pairs which contain more than one road segment were used to calculate the travel speed of every road segment. The reduction coefficient α and reliable weight θ for sample vehicle speed were introduced in the model. Finally, the model was verified by the special designed field experiments which were conducted on several freeways in Beijing at different time periods. The experiments results demonstrated that the average relative error was about 6.5% which means that the freeway travel speed could be estimated by the proposed model accurately. The proposed model is helpful to promote the level of the freeway operation monitoring and the freeway management, as well as to provide useful information for the freeway travelers. Jiancheng Weng, Rongliang Yuan, Ru Wang, and Chang Wang Copyright © 2014 Jiancheng Weng et al. All rights reserved. Game Theory Model of Traffic Participants within Amber Time at Signalized Intersection Mon, 15 Dec 2014 00:10:22 +0000 The traffic light scheme is composed of red, green, and amber lights, and it has been defined clearly for the traffic access of red and green lights; however, the definition of that for the amber light is indistinct, which leads to the appearance of uncertainty factors and serious traffic conflicts during the amber light. At present, the traffic administrations are faced with the decision of whether to forbid passing or not during the amber light in the cities of China. On one hand, it will go against the purpose of setting amber lights if forbidding passing; on the other hand, it may lead to a mess of traffic flow running if not. And meanwhile the drivers are faced with the decision of passing the intersection or stopping during the amber light as well. So the decision-making behavior of traffic administrations and drivers can be converted into a double game model. And through quantification of their earnings in different choice conditions, the optimum decision-making plan under specific conditions could be solved via the Nash equilibrium solution concept. Thus the results will provide a basis for the formulation of the traffic management strategy. Weiwei Qi, Huiying Wen, Chuanyun Fu, and Mo Song Copyright © 2014 Weiwei Qi et al. All rights reserved. Risk Evaluation of Bogie System Based on Extension Theory and Entropy Weight Method Wed, 10 Dec 2014 00:10:40 +0000 A bogie system is the key equipment of railway vehicles. Rigorous practical evaluation of bogies is still a challenge. Presently, there is overreliance on part-specific experiments in practice. In the present work, a risk evaluation index system of a bogie system has been established based on the inspection data and experts’ evaluation. Then, considering quantitative and qualitative aspects, the risk state of a bogie system has been evaluated using an extension theory and an entropy weight method. Finally, the method has been used to assess the bogie system of four different samples. Results show that this method can assess the risk state of a bogie system exactly. Yanping Du, Yuan Zhang, Xiaogang Zhao, and Xiaohui Wang Copyright © 2014 Yanping Du et al. All rights reserved. Label Propagation with -Degree Neighborhood Impact for Network Community Detection Wed, 26 Nov 2014 14:25:34 +0000 Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with -degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its -degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The -degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods. Heli Sun, Jianbin Huang, Xiang Zhong, Ke Liu, Jianhua Zou, and Qinbao Song Copyright © 2014 Heli Sun et al. All rights reserved. A Novel Adjustment Method for Shearer Traction Speed through Integration of T-S Cloud Inference Network and Improved PSO Sun, 23 Nov 2014 13:18:25 +0000 In order to efficiently and accurately adjust the shearer traction speed, a novel approach based on Takagi-Sugeno (T-S) cloud inference network (CIN) and improved particle swarm optimization (IPSO) is proposed. The T-S CIN is built through the combination of cloud model and T-S fuzzy neural network. Moreover, the IPSO algorithm employs parameter automation adjustment strategy and velocity resetting to significantly improve the performance of basic PSO algorithm in global search and fine-tuning of the solutions, and the flowchart of proposed approach is designed. Furthermore, some simulation examples are carried out and comparison results indicate that the proposed method is feasible, efficient, and is outperforming others. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system. Lei Si, Zhongbin Wang, Xinhua Liu, Yinwei Yang, and Lin Zhang Copyright © 2014 Lei Si et al. All rights reserved. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO Wed, 12 Nov 2014 00:00:00 +0000 To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy -means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect. Jian Zhang and Ling Shen Copyright © 2014 Jian Zhang and Ling Shen. All rights reserved. Numerical Simulation of Nonperiodic Rail Operation Diagram Characteristics Tue, 11 Nov 2014 06:24:49 +0000 This paper succeeded in utilizing cellular automata (CA) model to simulate the process of the train operation under the four-aspect color light system and getting the nonperiodic diagram of the mixed passenger and freight tracks. Generally speaking, the concerned models could simulate well the situation of wagon in preventing trains from colliding when parking and restarting and of the real-time changes the situation of train speeds and displacement and get hold of the current train states in their departures and arrivals. Finally the model gets the train diagram that simulates the train operation in different ratios of the van and analyzes some parameter characters in the process of train running, such as time, speed, through capacity, interval departing time, and departing numbers. Yongsheng Qian, Bingbing Wang, Junwei Zeng, and Xin Wang Copyright © 2014 Yongsheng Qian et al. All rights reserved. High Speed Railway Environment Safety Evaluation Based on Measurement Attribute Recognition Model Sun, 09 Nov 2014 13:29:55 +0000 In order to rationally evaluate the high speed railway operation safety level, the environmental safety evaluation index system of high speed railway should be well established by means of analyzing the impact mechanism of severe weather such as raining, thundering, lightning, earthquake, winding, and snowing. In addition to that, the attribute recognition will be identified to determine the similarity between samples and their corresponding attribute classes on the multidimensional space, which is on the basis of the Mahalanobis distance measurement function in terms of Mahalanobis distance with the characteristics of noncorrelation and nondimensionless influence. On top of the assumption, the high speed railway of China environment safety situation will be well elaborated by the suggested methods. The results from the detailed analysis show that the evaluation is basically matched up with the actual situation and could lay a scientific foundation for the high speed railway operation safety. Qizhou Hu, Ningbo Gao, and Bing Zhang Copyright © 2014 Qizhou Hu et al. All rights reserved. Estimation of Critical Gap Based on Raff’s Definition Sun, 09 Nov 2014 06:38:37 +0000 Critical gap is an important parameter used to calculate the capacity and delay of minor road in gap acceptance theory of unsignalized intersections. At an unsignalized intersection with two one-way traffic flows, it is assumed that two events are independent between vehicles’ arrival of major stream and vehicles’ arrival of minor stream. The headways of major stream follow M3 distribution. Based on Raff’s definition of critical gap, two calculation models are derived, which are named M3 definition model and revised Raff’s model. Both models use total rejected coefficient. Different calculation models are compared by simulation and new models are found to be valid. The conclusion reveals that M3 definition model is simple and valid. Revised Raff’s model strictly obeys the definition of Raff’s critical gap and its application field is more extensive than Raff’s model. It can get a more accurate result than the former Raff’s model. The M3 definition model and revised Raff’s model can derive accordant result. Rui-jun Guo, Xiao-jing Wang, and Wan-xiang Wang Copyright © 2014 Rui-jun Guo et al. All rights reserved. Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony Algorithm Thu, 06 Nov 2014 13:42:00 +0000 Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness. Tinggui Chen and Renbin Xiao Copyright © 2014 Tinggui Chen and Renbin Xiao. All rights reserved. Modeling Mode Choice Behavior Incorporating Household and Individual Sociodemographics and Travel Attributes Based on Rough Sets Theory Thu, 06 Nov 2014 13:36:09 +0000 Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling. Long Cheng, Xuewu Chen, Ming Wei, Jingxian Wu, and Xianyao Hou Copyright © 2014 Long Cheng et al. All rights reserved. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model Wed, 05 Nov 2014 11:31:10 +0000 Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. Fei Dou, Limin Jia, Li Wang, Jie Xu, and Yakun Huang Copyright © 2014 Fei Dou et al. All rights reserved. Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps Wed, 05 Nov 2014 00:00:00 +0000 A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower’s velocity, relative velocity, and gap) while the output signals represented the response (the follower’s acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity. Jie Yang, Ruey Long Cheu, Xiucheng Guo, and Alicia Romo Copyright © 2014 Jie Yang et al. All rights reserved.