Traffic and Transportation Analysis Techniques

This chapter deals with traffic and transportation analysis techniques. The chapter shows how to use space-time diagrams in order to represent the movements of an object (car, vessel, aircraft, crews, or passengers) through space and time. The chapter covers transportation networks basics, and especially algorithms for discovering optimal paths in transportation networks. Many real-life traffic and transportation problems can be formulated relatively easily in words. After such a formulation of the problem, in the next step, engineers usually translate the verbal description of the problem into a mathematical description. The chapter includes mathematical modeling issues and involves Linear Programming and Integer Programming in traffic and transportation. The numerous independent random factors affect various traffic phenomena. The chapter contains discussion about probability theory and traffic phenomena, probability theory basics, random variables and probability distributions, queueing in transportation systems, and simulation. Government, industry, and/or traffic authorities frequently have to evaluate sets of transportation projects. The ranking of the alternative projects is usually done according to a number of criteria that, as a rule, are mutually conflicting. The chapter includes basics of the multiattribute decision making methods. Modern intelligent systems are based on computer techniques capable of counting with words (Fuzzy Logic), learning and adapting (Artificial Neural Networks), and performing, in a systematic way, stochastic search and optimization (Genetic Algorithms). A set of these techniques, inspired by nature, is known as computational intelligence. The chapter also contains the basics of computational intelligence.