# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 4 Case Study
### Deployment Plans of the Nonhomogeneous Highway  
In addition to straight shapes, curves and slopes are also found along highways, which will decrease the monitored region of a camera and the accuracy rate of traffic prediction. For any combination type, the accuracy rate between two cells not only depends on the distance but is also determined by road shapes. To simulate the researched highway with slopes, the road cells should be nonhomogeneous. Under this circumstance, the evaluation method of accuracy rates still follows the formulation of Equation (17), which is modified by multiplying a coefficient for slope roads. The researched highway is cut into 100 cells, where cells 20 to 35 represent slope roads. The sensor deployment plan for this case is depicted in Figure 10.  
The total numbers of used sensors and servers are 23 and 6, respectively. For slope roads, five cameras are installed to monitor traffic. Compared with what is shown in Figure 7, the number of sensors in this region is increased. This is mainly because the distance between two sensors needs to be shortened to maintain the total accuracy rate at a high level. Thus, more sensors should be placed within this zone. It can be seen that the number of required sensors will rise under complicated road conditions. Therefore, the total accuracy rate of the case with sloped roads is a little lower than that of the case  
Figure 10: The sensor deployment plan for nonhomogeneous road cells.  
Figure 9: The number of servers and sensors varying with the factors of budget and communication radius. (**a**) Budget. (**b**) Communication radius.  
without slope roads. To further analyze the effect of sloped roads on the objective value, a comparison test is conducted for various road lengths (see Figure 11).  
In Figure 11, the x-axis represents the ratio of the length of sloped roads to the length of the entire road, where 0 denotes that the entire road is straight without steep slopes and is selected as the benchmark. The left y-axis denotes the total accuracy rate, and the right y-axis denotes the coverage rate. It can be seen that sloped roads would bring a negative influence on the total accuracy rate, especially when the ratio is larger than 0.1. But the accuracy rate did not continuously decline after 0.2 but fluctuated between 18 and 18.5. The coverage rate also showed a downward trend as the ratio increased. It should be noted that a turning point appears at 0.3. Since this test intends to keep the accuracy rate of sloped roads that do not change sharply, the number of sensors within these cells will increase. It might be beneficial for it to improve when the length of slope roads is extended. Table 3 lists the number of required sensors and servers under various ratios. The number of cameras changed from 23 to 25, and the number of RSUs decreased to 0. This is mainly because the prediction accuracy of cameras is supposed to be higher than that of RSUs. More cameras are needed to monitor the traffic on sloped roads. The number of servers varied between five and six. When it was equal to five, the coverage rate could remain unchanged or be increased. This indicates that the distance between two sensors is shortened and that more sensors are clustered by a server.