# Interdisciplinary Urban Tunnel Control within Smart Cities
## 2 Systemic Approach to Urban Road Tunnel
### Case Study--Management of Urban Tunnel Blanka in Prague  
For the purpose of city tunnel control, these three functions \(Q_{1},Q_{2},Q_{3}\) can be expressed as monetary values. It is rather straightforward for energy management (thereis a given price of energy). It is more complicated for environmental parameters and transportation, but it was addressed for example by a project GLOMODO (global model of traffic) ([[https://starfos.tact.cz/cs/project/UH0370](https://starfos.tact.cz/cs/project/UH0370)]([https://starfos.tact.cz/cs/project/UH0370](https://starfos.tact.cz/cs/project/UH0370)), accessed on 1 November 2021). This project aims to add values (meaning) to the data from all strategic detectors and all section speed control systems in Prague. An illustrative overview of the sensors used in the project is provided in Figure 2.  
Project GLOMODO consists of several modules whose aim is to give a road operator or a city manager better understanding of the traffic situation. As depicted in Figure 3, apart from data preprocessing aiming mainly on identification and correction of missing data and outliers, there are three modules: Module FIS computing the cost of congestions; Module LOS providing a level of service indication based on so-called speed index [13]; and Module MEN aiming on identification normal behavior of traffic flow and warning the operators in case of any non-standard traffic patterns. For the purpose of this paper, we shall focus on the module FIS only as it assigns monetary value based on the level of congestions. The overall cost of congestion in all district \(Q\) is computed when the vehicle is forced to travel with a limited speed due to congestions and is based on the following formula with parameters adjusted for the conditions of the city district--it is expressed by symbol (. ):  
\[Q(.)=Q_{1}(.)+Q_{2}(.)+Q_{3}(.) \tag{5}\]  
Figure 2: Overview of strategic sensors located in Prague and used within the project GLOMODO. where \(Q_{1}(. )\) computes the value of lost time, \(Q_{2}(. )\) determines the cost of environmental impact (related to increased pullutions) of congestions and \(Q_{3}(. )\) denotes the increased fuel consumption. \(Q_{1}(. )\) is computed as:  
\[Q_{1}(.)=N_{act}.t_{act}.C_{T}.veh_{occ} \tag{6}\]  
where \(N_{act}\) denotes the 5-minute value of traffic flow, \(t_{act}\) denotes the time delay due to the congested traffic, \(C_{T}\) the time spent in traffic and \(veh_{occ}\) the average occupancy of a vehicle. \(Q_{2}(. )\) is computed as:  
\[Q_{2}(.)=(S_{Gas}.C_{Gas}+(S_{Diesel}+S_{HDV}).C_{Diesel}).P_{CO2} \tag{7}\]  
where \(S_{Gas}\) is the computed fuel consumption for petrol vehicles in the given time interval; \(S_{Diesel}\) denotes the computed fuel consumption for diesel vehicles; \(S_{HDV}\) similarly denotes the consumption of trucks andlorries; \(C_{Gas}\) denotes CO\({}_{2}\) emissions for 1 litre of petrol; \(C_{Diesel}\) denotes emissions CO\({}_{2}\) for 1 litre of diesel; and, finally, \(P_{CO2}\) is the price associated with the production of 1 ton CO\({}_{2}\) emissions. \(Q_{3}(. )\) is building upon the generally accepted models of fuel consumption for different speeds. Different tools use slightly different models, as depicted in Figure 4. For this reason, GLOMODO uses an adopted model that is based on experimental evaluations [14]. Figure 4: Comparison of fuel consumption (in liters per 100 km) according to models HERA COPERT and TRANSYT. Figure 3: Main modules developed within the project GLOMODO. All parameters in the equations are set based on national as well as international guidelines and experiments. This approach is rather general and can be easily adopted for example to changes in vehicle fleets. Examples of the results of the above-mentioned model are provided in Figure 5 where the cumulative cost of congestions on particular strategic sensors as well as the level of service (LOS) is shown. Figure 6 further demonstrates the impact of each of the factors. Clearly, \(Q_{1}(.