# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 5 Results
### Case Study 3  
The third case study assessed an incident that occurred on 7 July 2018, and closed all lanes on I-75 Northbound at mile marker 91. Incident information was collected from the FDOT SunGuide report. The roadway characteristics and simulation variables used in the model and the details of the incident characteristics are shown in Table 5. Additionally, Figure 10 maps the incident location of the third case study. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Data Details** \\ \hline Date and time & 2018/12/25 02:00:33 \\ Location & I-75 Northbound, ramp to Big Bend Rd \\ Severity & 2 \\ Number of blocked lanes & Exit ramp left lane blocked \\ First responder arrival time & 2018/12/25 02:08:06 \\ Incident clearance duration (minutes) & 92.4 \\ County & Hillsborough \\ Notifier agency & FHP \\ Incident type & Crash \\ Maximum speed & 70 \\ AADT & 89,000 \\ Truck \% & 10.50\% \\ \hline \end{tabular}
\end{table} 
Table 4: Detailed information related to case study 2. Figure 9: Incident location—case study 2. The three case studies were simulated using the developed algorithm. The appropriate height restriction was identified as follows: descriptor attributes specified the height limit for each road, and a restriction attribute stored the vehicle height parameter. Following this, a script evaluator was created so that selection of a street was prohibited when the actual vehicle height exceeded the maximum vertical clearance. By applying height restrictions, the developed model diverted trucks to avoid low vertical clearances. Additionally, a scaled factor was used as a polygon barrier to avoid school zones. To compare travel times and estimate benefits from the model application, two simulation scenarios were performed for each case study: a base scenario and an optimized alternative route scenario. The base scenario route was estimated without consideration  
Figure 10: Incident location—case study 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Data Details** \\ \hline Date and time & 2018/07/011 15:00:27 \\ Location & I-75 Northbound, at mile marker 91 \\ Severity & 3 \\ Number of blocked lanes & Road closed \\ First responder arrival time & 2018/11/15 04:23:15 \\ Incident clearance duration (minutes) & 65.68 \\ County & Collier \\ Notifier agency & FHP \\ Incident type & Vehicle fire \\ Maximum speed & 70 \\ AADT & 41,500 \\ Truck \% & 11.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed information related to case study 3. The resulting benefits were estimated, and the two scenarios were compared to validate the developed framework. Table 6 presents a summary of benefit estimation from the three case studies. To validate the proposed diversion decision-making framework, the model benefits were estimated with the following procedure:  
1. Calculation of the travel time on the shortest route (without diversion) and the travel time on the optimized truck route (with diversion). 2. Estimation of the difference in total travel time between the two scenarios. 3. Calculation of the reduction in delays due to the implementation of the diversion operation. 4. Quantification of the reduction in fuel consumption due to the implementation of the diversion strategy using conversion factors. The following conversion factors were obtained from the National Highway Traffic Safety Administration Commercial Medium- and Heavy-Duty Truck Fuel Efficiency Technology Study (June 2015):
* For idle, a value of 6.515 miles per gallon;