Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges.

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model's flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards. [ABSTRACT FROM AUTHOR]