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The twenty-first century finds civilization heavily based in cities that have grown into large metropolitan areas. Many of these focal points of human activity face problems of economic inefficiency, environmental deterioration, and an unsatisfactory quality of life—problems that go far in determining whether a city is "livable." A large share of these problems stems from the inefficiencies and other impacts of urban transportation systems. The era of projects aimed at maximizing vehicular travel is being replaced by the broader goal of achieving livable cities: economically efficient, socially sound, and environmentally friendly. This book explores the complex relationship between transportation and the character of cities and metropolitan regions. Vukan Vuchic applies his experience in urban transportation systems and policies to present a systematic review of transportation modes and their characteristics. Transportation for Livable Cities dispels the myths and emotional advocacies for or against freeways, rail transit, bicycles,and other modes of transportation. The author discusses the consequences of excessive automobile dependence and shows that the most livable cities worldwide have intermodal systems that balance highway and public transit modes while providing for pedestrians, bicyclists, and paratransit. Vuchic defines the policies necessary for achieving livable cities: the effective implementation of integrated intermodal transportation systems.
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air line crew scheduling, corporate planning, computer-aided design and man ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi tion, linear programming relaxations are often the basis for many approxi mation algorithms for solving NP-hard problems (e.g. dual heuristics).
A series of conference proceedings on various transportation issues from the European Conference of Ministers of Transport, now known as the International Transport Forum.