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While decision-making under uncertainty is a universal problem, its implications in the field of transportation systems are especially consequential; where the benefits of right decisions can be substantial, the effects of wrong ones can be disastrous. Development decisions involving highways must incorporate traffic demand and land price. In the literature, these and other uncertainties are almost universally modeled using Geometric Brownian Motion (GBM). The book challenges this blanket assumption. Its vivid analysis of many aspects of transportation decision-making reveals novel findings including the new concept of Opportunity Cost of Wrong Decisions. Through empirical tests, the author invalidates one real-option application of the GBM model while corroborating the applicability of several others from the class of Lévy jump models. Despite similarity of model end results, the improvements in model precision further the quest for optimality and much more. Comprehensive and thorough, the book appeals to a wide range of readers including decision-makers, engineers, planners, academicians, researchers, politicians and others curious about any of the above topics.

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The author holds MMath in Statistics & Civil Eng., Certificate in Structural Eng. & Option in Management Sci. from the University of Waterloo, Canada. His unconventional Master´´s combines areas of Quantitative Finance, Construction Management & Microeconomics. Mayar has work experience in various fields of civil eng. in private & public sectors.

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