# Interdisciplinary Urban Tunnel Control within Smart Cities
## 3 New Road Tunnel Traffic Control Algorithm
### New Road Urban Tunnel Traffic Control  
As mentioned above, nowadays the most common \"control\" approach in an urban tunnel is a closure of a tunnel. [PERSON] et al. [16] calls closure a disruptive traffic management strategy and further claims that the resulting strategy shall depend on the cause and type of congestions. Tunnel closure solves the problems of hygienic (environmental) limits in the tunnel. However, it means a big intervention in the quality of traffic in the whole city, which is contradicting the idea of smart cities. At the same time, due to the distributed nature of traffic spread in the city and implicit hysteresis of the traffic stream characteristics (as explained above), the recovery phase is very slow. In this chapter we propose a new traffic control management system based on the properties of traffic flow described above. A flow chart (using the SW modelling tool Enterprise Architect) for the proposed control algorithm is provided in Figure 12 and explained within this section. It aims to prevents traffic flow transiting into the jam phase. Similar principle of stabilizing main traffic stream is known from highways as ramp metering [25; 26]. In ramp metering there is a main traffic flow going on the motorway, and a traffic signalization allows only a limited number of vehicles entering from a side on-ramp. In the case of a tunnel, we are using a similar principle, with the difference, that we are restraining vehicles entering the tunnel (see Figure 13). It is not carried out in a binary manner (tunnel open/closed), but using variable length of a red signal, \(T_{R}\). The objective is to keep the density below the value of \(\kappa_{KO}\) (See Figure 11) and thus avoid falling into the jam traffic. Let us consider a fixed cycle length of 120 s. The length of the red-light signal then varies between 0 s and 120 s (which in a 120-second-long cycle means that the tunnel is closed) depending on two factors. The first parameter is the traffic density in the tunnel (see explanation in previous section and Figure 11). Since it is difficult to measure directly density, we use a parameter called occupancy, \(o_{i}\) (%) (the index \(i\) denotes \(i\)-th sensor). Occupancy is measured directly by point traffic sensors and under certain assumptions (mainly a uniform vehicle length \(L\)) it can be used to calculate density [27]:  
\[o_{i}=(L+d).\ \kappa_{i}=c_{K}.\kappa_{i} \tag{8}\]  
where \(L\) is the uniform vehicle length and \(d\) is the length of the sensor (e.g., inductive loop). While computing density, in order to allow better link to the theoretical section of  
Figure 11: The hysteresis within transition phase between fluid and jam traffic. this paper, we refer to the corresponding value of computed density in all calculations and figures. To be able to react to the expected worsening of the situation in advance a prediction module is used to have a prediction of the density (for example, 15 min in advance, \(\kappa_{i}\left(t+15\right)\)). There are several existing algorithms to predict traffic stream characteristics. [PERSON] and [PERSON] [28] are using spatial-temporal data fusion using traffic volume, road geometry, or even weather conditions. [PERSON] et al. [29] use random forest regressors on traffic flow time series. [PERSON] et al. in [30] predict traffic flow using a deep learning approach and [PERSON] et al. in [31] use statistical hybrid Gaussian process regression. Altogether, the various research methods show rather strong prediction capabilities. Figure 12: Flow chart for the proposed new road urban tunnel traffic control algorithm. Please note that in our approach we do not wait until the density reaches value \(\kappa_{KO}\) (in Figure 11). We start limiting the vehicles already by \(\kappa_{go,\ min}\). Clearly, we want to prevent the transition into the jam phase, so the algorithm starts earlier. This shall be sufficient as it is linked to the predicted value of occupancy.