# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
###### Abstract  
This paper mainly studies the deployment of hybrid sensors on highways. By constructing the deployment location constraint model, the overall accuracy of sensor deployment can be maximized. In addition, in order to meet the needs of intelligent transportation, the consideration of traffic data communication is added to the work. The highway under study is first divided into several units, and the combination type of sensors is used to represent the possible layout of two adjacent sensors. Then, a 0-1 optimization model reflecting the interaction between the sensor position and the server position is established. Then, a two-step search algorithm is proposed to find the optimal solution of the model and determine the deployment scheme with maximum accuracy. Finally, an example is given to verify the method. The results show that there are significant differences between uniform unit-deployment schemes and non-uniform unit-deployment schemes. Through the sensitivity analysis of each factor, the influence of budget and communication radius on the deployment plan is proven. In addition, the ramp length can also have a negative impact on the target value. hybrid sensors; highway; deployment; communication +
Footnote †: journal: _Sci._  
0000-0001-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-00000-0000-00000-00000-00000-00000  
monitor traffic, such as loop detectors, RSUs (Road Side Units), cameras, laser sensors, and ultra-acoustic sensors. Due to differences in sensor attributes (e.g., cost, service scope, data type), they have their own advantages in application. Traffic flow observation [4; 5], travel time estimation [6; 7] and path reconstruction [8] are universal goals to conduct the work of optimizing sensor locations. To improve the evaluation accuracy, mixed types of sensors may also be taken into consideration to enrich traffic information [9]. These assumptions are usually used in the background of freeways or urban road networks to discuss the sensor location problem. [PERSON] and [PERSON] [10] focused on the allocation of loop detectors along freeway corridors. [PERSON] et al. [11] investigated travel time estimation by optimizing the placement and density of point sensors in a freeway. [PERSON] et al. [12] studied the roadside sensor placement problem to monitor the state of a traffic network. [PERSON] et al. [13] tried to reach full link-flow observability to optimize sensor configuration in a traffic network. [PERSON] and [PERSON] [14] discussed the problem of optimally locating sensors on a traffic network. In most studies, the location problem of mixed types of sensors on the highway has received less attention. This problem has been discussed well for traffic networks [9; 15; 16]. These works provide valuable directions to predict traffic flow or OD (Origin Destination) matrices. However, in addition to traffic state prediction, road managers also need to pay attention to real-time information broadcasting in the future intelligent transportation system. The extraction of some traffic information might be time-consuming, which is expected to be executed in local servers instead of transmitting data to the control center. Against this background, the idea of edge computing is widely introduced to save computing time and reduce communication delay. Thus, the deployment of edge computing infrastructure should be considered when planning the location of sensors. In recent years, numerous works on the problem of server placement have been proposed. Among them, the constraint of workload and computing capacity are mainly considered in the optimization model [17; 18; 19; 20]. And the objective for the problem mainly includes energy consumption [21], workload balancing [22], and the total cost [23; 24]. To avoid the effect of traffic data storage and processing on the sensor deployment problem, this paper intends to investigate it by integrating the problem of server placement for highways.