Empirical inquiry into urban travel behavior thrived, integrating observational data, experiments, and scheduling information to reveal congestion responses and decision patterns in real-world networks. The rise of Integrated Land-Use–Transportation modeling, grounded in random utility theory and solved via nonlinear optimization, enabled joint representation of urban activity locations, housing, and travel, with extensions to logit-based mode choices and household mobility. Probabilistic and discrete-continuous econometric frameworks linked mode choice, route choice, and travel time uncertainty, advancing probabilistic travel times and robust estimation. Policy analysis focusing on subsidies, fares, technology choices, and policy variables’ effects on transit use shaped empirical understanding of modal shares. Transit system design and cost optimization methods matured, including optimization of system characteristics and analytic models for distance and travel costs. Historical Significance: Historically, this period established the unification of land-use and transportation analysis as a standard research paradigm, linking urban form with travel behavior and energy implications. The development of nonlinear optimization for joint modeling and the extension of random utility and logit frameworks laid the groundwork for more sophisticated disaggregated and policy-sensitive models. The introduction of probabilistic travel times and perceptions foreshadowed robust and stochastic assignment and uncertainty-aware network analysis. Foundational contributions to routing and scheduling, such as time-window column generation and period routing concepts, spurred subsequent advances in logistics and distribution planning. Theoretical bridges between equilibrium concepts (Nash, Cournot) and Wardrop-type traffic equilibria, and the generalization of traffic equilibrium to probabilistic travel times, influenced later congestion modeling and equilibrium analysis.