The book "Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods" by Yosef Sheffi explores the flow patterns in urban transportation networks, which are influenced by two competing mechanisms: user travel decisions and congestion. Users aim to minimize disutility, such as travel time, while congestion affects travel times due to increased traffic flow. The author models these mechanisms using an analytical approach that draws parallels with supply and demand in the marketplace. The analysis focuses on transportation level of service and flows, resulting in equilibrium flow patterns. The book covers various aspects of travel choice, including trip initiation, mode selection, destination distribution, and route choice, all analyzed within a unified framework that combines graphical and network representations. The core methodology involves formulating the problem as a nonlinear optimization, which is explained without requiring advanced mathematical programming or graph theory knowledge. Intuitive arguments and network structures are used to illustrate the concepts graphically.The book "Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods" by Yosef Sheffi explores the flow patterns in urban transportation networks, which are influenced by two competing mechanisms: user travel decisions and congestion. Users aim to minimize disutility, such as travel time, while congestion affects travel times due to increased traffic flow. The author models these mechanisms using an analytical approach that draws parallels with supply and demand in the marketplace. The analysis focuses on transportation level of service and flows, resulting in equilibrium flow patterns. The book covers various aspects of travel choice, including trip initiation, mode selection, destination distribution, and route choice, all analyzed within a unified framework that combines graphical and network representations. The core methodology involves formulating the problem as a nonlinear optimization, which is explained without requiring advanced mathematical programming or graph theory knowledge. Intuitive arguments and network structures are used to illustrate the concepts graphically.