Statistical Analysis with Missing Data

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data

A bayesian network approach to traffic flow forecasting

This paper presents a fuzzy logic adaptive traffic signal controller for an isolated four-approaches intersection with through and lest-turning movements. The controller has the ability to make adjustments to signal timing in response to observed changes. The "urgency degree" term, which can describe the different user's demand for green time is used in fuzzy logic decision-making in our algorithm. Using three levels model of fuzzy controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. This paper describes a flexible form of fuzzy logic signal control whose performance can be tuned off-line using a set of parameters which define the fuzzy set membership functions for the input variables. Our work also demonstrates the feasibility of using a multiobjective genetic algorithm (MOGA) to find a set of optimal parameters for a fuzzy controller with a range of possibly conflicting performance measures. Simulation results show that our proposed controller produces lower vehicle delays and percentage of stopped vehicles than the traffic-actuated controller.

Traffic signal control using fuzzy logic and MOGA

BRoPH: An efficient and compact binary descriptor for 3D point clouds

Short-term traffic flow forecasting plays a very important role in urban traffic management and control. In this paper, According to the chaotic property of urban traffic flow, we compute the parameters of phrase space reconstruction for traffic flow system. Meanwhile, a local-forecasting method is introduced to predict urban road short-term traffic flow based on the theory of phrase space reconstruction. Self-organizing Map (SOM) network is introduced to seek the near neighbor. Case study using real traffic flow data from UTC-SCOOT system proves the validity of the method. The research in this paper is a significant attempt to forecast traffic flow from the viewpoint of non-linear time series.

An applicable short-term traffic flow forecasting method based on chaotic theory

This paper surveys the utilization of the wall following concept for path planning in mobile robotics and proposes an improved wall following method for escaping from local minimum encountered by the artificial potential field (APF) method used in real-time path planning. In the new algorithm, more reliable switching conditions are designed to guarantee the success of escape. Moreover, memory information of key points is incorporated in to enhance the robot's capability of making decision. It can be applied effectively in some more complicated unknown environments in which some previous wall following methods are proved to be inefficient or invalid. Simulation studies have been carried out for analyzing the memory information and verify the validity of the proposed method.

An improved wall following method for escaping from local minimum in artificial potential field based path planning

http://www.jhuapl.edu/spsa/PDF-SPSA/Xu%20_Article.pdf

Jingyan Song

Papers

Traffic signal control using fuzzy logic and MOGA

BRoPH: An efficient and compact binary descriptor for 3D point clouds

An applicable short-term traffic flow forecasting method based on chaotic theory

An improved wall following method for escaping from local minimum in artificial potential field based path planning

Performance evaluation and optimization of human control strategy