# Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges
## 3 Congested Traffic Simulation Based on CA Method
It should be noted that the histograms in Figure 5a,b correspond to the GVW distribution for extreme response scenarios with different traffic volumes. The histogram of the GVW distribution of all the vehicles of the WIM data can be seen in Figure 5c. In comparing histograms in Figure 5b,c, it can be noticed that when the value of the daily extreme response is larger,  
Figure 3: Histogram of occurrence times of free-flow extreme response scenarios. Figure 2: Longitudinal layout of the investigated bridge (m). the level of the GVW distribution in the extreme response scenario is significantly higher compared to the GVW of the global WIM data. On the other hand, when the daily extreme response is obtained with a larger traffic volume, the level of GVW distribution in the extreme response scenario is significantly smaller compared to the GVW of the WIM data (compare Figure 5a and Figure 5c). Figure 4: The average hourly traffic and ratio of response to D60 for extreme response scenarios during daytime. Figure 5: GVW distribution of (**a**) high hourly traffic volume extreme response scenarios; (**b**) high response value extreme response scenarios; and (**c**) WIM data. According to this, the congestion-prone free-flow traffic is characterized by a high volume of traffic and low percentage of heavy vehicles. Therefore, in the event that traffic flow becomes congested due to excessive traffic volume, the percentage of heavy vehicles in the congested situation must be lower than the actual percentage in the original WIM data. In order to obtain enough extreme congestion response scenarios for the comparison with free-flow extreme response scenarios, a micro-simulation model can be used to produce the congested flow traffic suitable for critical long-span bridge-loading scenarios, as presented by [PERSON] et al. [39]. The CA method has been used in several fields. With the aid of the CA method, complex movements of vehicles, such as speed changes, lane changes, etc. [35], can be simulated successfully and the generated long-term traffic flow maximum effects can be obtained. Traffic micro-simulation based on CA is applied to obtain the detailed information associated with vehicle load, vehicle velocity, headway distance, and lane position [38]. After a vehicle is produced, its movement is controlled by the local rules that determine the driving modes (i.e., acceleration, cruising, coasting, and braking). The accuracy and efficiency of the CA method has been verified, as presented by [PERSON] et al. [40]. Thus, the congestion traffic data used in this study were obtained using the CA model. An input flow rate of 12,000 vehicles per hour was used for the six lanes of the bridge. A total of 24 h of congested traffic was generated. To implement the bottleneck, each vehicle's desired velocity was changed to 15-20 km/h. This arrangement lead to homogeneous congested traffic, which is characterized by a low average velocity. However, unlike fully jammed traffic, it still maintains a relatively high traffic flow [39]. The average speed histogram, traffic flow histogram for each hour, and GVW histogram of the generated congested traffic are shown in Figure 6. It can be seen that the level of vehicles with a GVW less than 10 tonnes is magnified in the simulated congested flow. Figure 6: The (**a**) average speed histogram; (**b**) traffic flow histogram for each hour; and (**c**) GVW histogram of the generated congested traffic.