THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Statistical Analysis With Missing Data (2nd ed.) (Book)

Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

The goal of the Imaging X-Ray Polarimetry Explorer (IXPE) Mission is to expand understanding of high-energy astrophysical processes and sources, in support of NASA's first science objective in Astrophysics: “Discover how the universe works.” Polarization uniquely probes astrophysical anisotropies — ordered magnetic fields, aspheric matter distributions, or general relativistic coupling to black-hole spin — that are not otherwise measurable. IXPE will conduct X-ray polarimetry for multiple categories of cosmic X-ray sources that are likely to be polarized such as neutron stars, stellar-mass black holes, supernova remnants and active galactic nuclei. The IXPE Observatory consists of Spacecraft and Payload modules built up in parallel to form the Observatory during system integration and test. The Payload includes three polarization-sensitive, X-ray detectors, each paired with its corresponding grazing incidence mirror module assembly (MMA). A deployable boom provides the correct separation (focal length) between the detector units (DU) and MMAs. These Payload elements are supported by the IXPE Spacecraft which is derived from the BCP-100 small Spacecraft architecture. This paper summarizes the IXPE mission science objectives, describes the Observatory implementation concept including the payload and spacecraft elements and summarizes the expected concept of operations.

Observatory design for the imaging X-Ray Polarimetry Explorer (IXPE) mission

Modern infrastructure systems, ranging from transportation to water and sewer systems, are becoming increasingly dependent on software. The role it plays in these systems is primarily to integrate and manage elements that support system operations. In other words, software has transformed what were previously considered to be largely static systems to active, dynamic systems. In general, infrastructure system software is characterized by an emphasis on the following core functions: (1) sensor management; (2) data analysis; (3) data management; and (4) equipment control interfaces. As the nature of infrastructure systems changes, the tools available to support their design and management must change as well. The construction engineering community has developed sound techniques and resources to support the estimation of costs for physical construction of infrastructure systems. However, such tools are not readily available to support the cost estimation of the software component of infrastructure system dev...

Software Development Cost Estimation for Infrastructure Systems

Conflicts in freeway merge areas contribute significantly to freeway congestion. With advances in vehicle-to-vehicle and vehicle-to-infrastructure communications, a new concept such as the Connected Vehicle environment, where all the vehicles and infrastructure components can communicate with each other, emerges and allows for new approaches to better manage freeway merge conflicts. Given this background, a prototype advanced freeway merging control algorithm was developed in this paper. This algorithm estimates the anticipated lead and lag gap availability, and selects an appropriate strategy for controlling mainline leading and lagging vehicles and a ramp vehicle to create a sufficient gap for a smoother merge. Evaluation was conducted using VISSIM microscopic traffic simulations of an actual freeway network in Virginia. The results show that the proposed freeway merging control algorithm can significantly improve the network performance. In an ideal situation, the proposed algorithm generates a 6.3% increase in vehicles miles traveled, a 42.2% increase in average speeds, a 25.9% reduction in total travel times, and a 54.6% reduction in total delay times. In addition, fuel consumption is reduced by 12.0% and CO2 emission by 12.7%. Finally, a sensitivity analysis indicates that at least a 60% advisory compliance rate and a 50% market penetration rate are necessary to generate meaningful benefits from the proposed algorithm. All these results support that there is significant potential in improving freeway operations when a connected vehicle environment is established.

A Prototype Freeway Merging Control Algorithm Under a Connected Vehicle Environment

The development of individual intelligent transportation systems (ITS) that operate well independently is not sufficient to meet the growing transportation needs of urban areas. To realize the true potential of ITS, systems such as freeway management systems, transit scheduling and dispatch systems, signal control systems, emergency computer-aided dispatch systems, and even construction scheduling systems must be integrated to allow for coordinated regional decision making. The formidable task of realizing integrated ITS (IITS) must often be accomplished by various municipal systems, most with little or no automation, and several organizational bodies that attempt to coordinate these efforts into a comprehensive regional network. Each of these organizations and systems has its own set of objectives, and these objectives may be noncommensurate and conflicting. Regional integration allows the systems to share information, allowing transportation officials to manage the entire regional transportation system....

Development of Integrated Intelligent Transportation Systems

Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The data provided by ITS is necessary for advanced forms of control, however basic forms of control, primarily time-of-day (TOD) which are prevalent in the United States do not directly rely on the data. Thus sensor data is typically unused and discarded in this country. The sensor data is in fact capable of providing abundant amounts of information that can aid in the development of improved TOD signal timing plans. Data mining tools are necessary to extract the information necessary from the data to improve on timing plan development and in turn would allow the timing plan development and monitoring process to be automated. This paper describes a research program that is investigating the application data mining tools including statistical clustering and classification techniques to aid in the development of traffic signal timing plans. Specifically, a case study was conducted that illustrated that the use of Hierarchical Cluster analysis can be used to identify temporal interval break points that support the design of a time-of-day (TOD) signal control system. The cluster analysis approach was able to utilize a high resolution system state definition that takes full advantage of the extensive set of sensors deployed in a traffic signal system. Finally the case study also demonstrated that a Classification and Regression Tree (CART) could be developed that can be used to automatically monitor the quality of TOD intervals as traffic conditions change through time. The results of this research indicate that advanced data mining techniques hold high potential to provide automated techniques that assist traffic engineers in signal control system design and operations

Brian L. Smith

Papers

Observatory design for the imaging X-Ray Polarimetry Explorer (IXPE) mission

Software Development Cost Estimation for Infrastructure Systems

A Prototype Freeway Merging Control Algorithm Under a Connected Vehicle Environment

Development of Integrated Intelligent Transportation Systems

Signal system data mining