Everybody knows that digital technology has revolutionized our economy and our lifestyles. But how many of us really understand the drivers behind the technology – the significance of going digital; the miniaturization of electronic devices; the role of venture capital in financing the revolution; the importance of research and development? How many of us understand what it takes to make money from innovative technologies? Should we worry about manufacturing going offshore? What is the role of India and China in the digital economy? Drawing on a lifetime’s experience in the industry, as an engineer, a senior manager, and as a partner in a global venture capital firm, Henry Kressel offers an expert personalized answer to all these questions. He explains how the technology works, why it matters, how it is financed, and what the key lessons are for public policy.

Competing for the Future

Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes - the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.

http://ceprofs.tamu.edu/dlord/Papers/Lord-Mannering_Review.pdf

The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

https://www.ce.utexas.edu/prof/bhat/ABSTRACTS/AMAR_review_article.pdf

Analytic methods in accident research: Methodological frontier and future directions

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

Influence of connected and autonomous vehicles on traffic flow stability and throughput

In this article, the authors explore the bagging and boosting classification techniques. They apply the two techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn. This higher predictive performance could ultimately lead to incremental profits for companies that use these methods. Furthermore, the results recommend the use of a balanced sampling scheme when predicting a rare event from large data sets, but this requires an appropriate bias correction.

/pdf/bagging-and-boosting-classification-trees-to-predict-churn-boq2zc7149.pdf

Bagging and Boosting Classification Trees to Predict Churn

Accident prediction models for roads with minor junctions

This paper investigates the stability of the classical car-following model (for example, Chandler et al., Operations Research, 6, 165–184, 1958; Herman et al., Operations Research, 7, 86–106, 1959; Wilhelm and Schmidt, Transportation Engineering Journal (ASCE) 99, 923–933, 1973). Conditions for local and asymptotic stability as defined in the references cited are established for the linear model. These differ from those in the literature in two ways. First, it will be shown that, in the autonomous model when the product of the coefficient of proportionality α and the reaction time τ is less than or equal to 1/e, there exist oscillatory solutions with higher frequencies than 2π, although there are none with lower frequencies. Secondly, asymptotic stability is considered along with local stability. The derived condition for asymptotic stability is both necessary and sufficient. In addition, the condition depends on the frequency of the forcing term, with the sufficient condition ατ 1 2 for the asymptotic stability found in the literature being included as a special case. The nonlinear model is considered by linearization and numerical integrations. Some practical values of parameters are tested for the stability of the model. The analyses in this paper are extended to consider different values of α and τ for different drivers in the line.

Stability analysis of the classical car-following model

The perceived importance of five technical service qualities (Gronroos 1984) or features (i.e. national and local policies, leaders, values and candidates), and voters’ ratings of the Labour and Conservative Parties’ competence on each of these parameters, were investigated during the 2001 British General Election using an a priori segmentation method and the classification tree statistical technique for data analysis. Voter ratings of the technical service features were found to be indicators of intention to vote. A product differentiation approach is most likely to influence voting intention, because the technical service features are more readily manipulated through marketing programmes than demographic and customer characteristics (Bucklin and Gupta 1992). Ratings of technical service features are stronger indicators of voting intention than voter demographics and characteristics. A product differentiation approach, based around technical service features, would be the most effective focus for strateg...

Market Segmentation and Product Differentiation in Political Campaigns: A Technical Feature Perspective

Dose-response functions for the soiling of heritage materials due to air pollution exposure

Political parties have long since targeted the marginal constituency and floating voters using demographic segmentation approaches and the use of market segmentation techniques in general election campaigns is now well-documented (see Johnson 1971; Ahmed and Jackson 1979; Yorke and Meehan 1986; Baines et al. 2003). The actual practice of segmentation as undertaken by political parties and its relation to theory is less well-considered. This paper represents a serious attempt to outline how political parties targeted a priori segments of the electorate including gender, age and lifecycle in the 2005 British General Election when they should have been adopting a product attributed-based approach. Selected MORI surveys from April 2005 were analysed, using logistic regression to indicate the most important factors in determining how Britons vote. Principal components analysis provides an indication of how the three main British political parties are perceived. The paper discusses, using resource-advantage the...

David Jarrett

Papers

Accident prediction models for roads with minor junctions

Stability analysis of the classical car-following model

Market Segmentation and Product Differentiation in Political Campaigns: A Technical Feature Perspective

Dose-response functions for the soiling of heritage materials due to air pollution exposure

Product Attribute-Based Voter Segmentation and Resource Advantage Theory