# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Base Model Development
#### 3.5.3 Model Calibration  
The calibration of traffic-simulation models such as VISSIM is essential to ensure accurate predictions of traffic flow and safety [39]. This process involves adjusting the model's parameters to match observed traffic data. Before calibration, model verification strategies should be conducted to increase the effectiveness of the verification process in micro-simulation analysis. These strategies include observing vehicle behavior at low demand levels, reviewing MOEs, following a single vehicle through the network, and performing visual inspections to ensure the model replicates field observations. The latest software version should be used, and simple test problems should be coded for manual comparison. Color codes should also be used to identify links, and intersection attributes should be reviewed. The verification process was conducted for the VISSIM model. These strategies were instrumental in ensuring the model's accuracy and served as a crucial first step in the calibration process.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Source** \\ \hline Simulation Duration & 3600 s & Scenario Definition \\ Simulation Resolution & 1 s & VISSIM Default \\ Number of Replications & 20 & Recommended for statistical significance \\ Traffic Flow & 5200 veh/h & Manual Counts \\ Heavy Vehicle Percentage & 28\% & Manual Counts \\ Car-Following Model & Wiedemann 99 & VISSIM Default \\ Minimum Time Headway & 1 s & Calibrated value \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation configuration in VISSIM.  
The model calibration process involves determining which parameters require calibration, selecting suitable measures of effectiveness (MOEs) and data for model validation, calculating the number of simulation runs needed to achieve a 95% confidence level with a 5% margin of error for all MOEs, identifying validation criteria and targets, and adjusting the selected parameters iteratively until the model outputs accurately reflect the observed field traffic conditions or meet the validation targets. Figure 7 shows a general model calibration procedure that transportation professionals can use to calibrate simulation models accurately.  
For the VISSIM model used in this study, the main calibration focus was parameters related to driver behavior and heavy vehicle characteristics. The calibration process involved adjusting commonly used driver behavior parameters, such as [PERSON]'s 1974 car-following model, waiting time before diffusion, and minimum headway in the lane-changing algorithm.  
Vehicle parameters, including traffic composition, vehicle length, desired speed, desired acceleration/deceleration, maximum acceleration/deceleration, and other attributes associated with each modeled heavy vehicle type, were also examined. Additionally, signal-control parameters and the impact of simulation resolution on the response to traffic controls were considered.  
The validation stage involved comparing simulated MOEs to their corresponding observed values, and three MOEs were selected for validation purposes: traffic movements, queue length, and link speed. These MOEs were used to ensure that the calibrated model accurately reflected real-world traffic conditions and produced reliable results.