The Indian code of practice for road bridges, IRC:6-2017, presents standard vehicular live load models with a certain minimum nose-to-tail spacing. In the case of congested traffic, the spacing between vehicles reduces, leading to an increase in the stress resultants. The code does not prescribe a load model for congested traffic in particular, but for bridges located in areas close to ports and industrial clusters that frequently see congested traffic, it prescribes scaling up the stress resultants using the values of congestion factors that have been provided for a range of spans. These values of congestion factors were arrived at based on a deterministic approach involving critical loading scenarios only for simply supported bridge spans. Vehicles of Gross Vehicle Weights 49 tonnes and 40.2 tonnes, which are representative of 6-axle and 5-axle trucks respectively, were used for the purpose.

The present study extends the deterministic approach for estimating congestion factors for vehicles with different Gross Vehicle Weights and axle configurations which are mentioned in IRC:SP:37-2010. Congestion factors have been estimated for simply supported bridges of spans 30m to 50m and for two six-span continuous balanced cantilever bridges.

Further, the study takes a relook into congestion factors using a probabilistic approach. The procedure involves simulation of random congested traffic streams reflective of the vehicle proportions observed on site, and estimation of stress resultants. Probabilistic models of axle loads and other traffic parameters were developed based on the Weigh-In-Motion data and axle load survey data collected from three different locations with varying vehicle proportions and different levels of loading. Linear correlation coefficient was used to model the dependence structure between the axle loads. A MATLAB script was developed for carrying out the computer simulations. A set of guidelines is presented for the estimation of congestion factors for continuous bridges. The study also includes the effects of parameters such as bridge spans, number of lanes, and percentage of multi-axle vehicles on congestion factors