Case Study: Analysis and Study of Different Approaches for Road Network Maintenance

The protection and convenience of smooth traffic by the road network are governed to a large extent by the superiority of maintenance. By early identification of problem, the rapid deterioration of the roadways can be prevented. The primary intention of maintenance is to allow the movement of traffic at a desired speed, safety and not as much of cost. Road network can be preserved and prolonged if sufficient maintenance measures are undertaken at proper time. Potholes, cracks, patches etc., are some types of road surface distresses mostly used to perform manually. In the current field practices, road distress data assessment is reported to be done through distress data collection and processing of the collected raw data. This process is a labor-intensive and time consuming process and can also slows down the road maintenance management. By considering necessity for automation at present, distress data collection is increasingly being shifted towards atomization. In this paper, we analyzed, different solution which has used concept of neural network, artificial intelligence, fuzzy logic, computer vision, data-driven methods, for automation of the process. Sensor based technique and GPS based approach for monitoring road and traffic conditions to detect road distress has been analyzed.