MAARG: Monitoring And Assessment of Road by Geo-citizens - A multi-sensor location aware system for road infrastructure management

Abstract

The idea behind building a road surface and condition monitoring system is that in a developing country like India, we would like to create a sustainable system that can, with the help of geo-citizens, monitor road quality. This task is crucial to the municipal and city authorities to conduct as there is not enough manpower to monitor large road networks of the growing metropolises in the country. This semi-automated location-aware system can augment and help provide critical inputs to infrastructure/road managers in their tasks and deliver results like no other system currently in use. A crucial aspect of urban infrastructure management in cities is the continuous monitoring and maintenance of roads. Specific departments employed by governing authorities track road deterioration and ensure timely repair and maintenance of roads. Traditional methods in this domain use high-power laser or radar sensors and thereby go on to become intricate and demanding. Apart from the methods in data collection, the process of gathering, assimilating, and post-processing of data is an expensive and time-consuming task with low coverage and often in need of assistance from experts. Additionally, frequent data collection is essential to generate efficient and accountable results. High-end systems are inefficient in covering large areas at frequent and/or regular intervals of time, which leads to complexity in activity planning. In previous work, we have established that smartphone sensors can be used to determine the surface type and condition of the road. In the current thesis, we use the same algorithm for classification and build over it to develop a system that can address these above-listed challenges in an ever-expanding urban environment. MAARG system consists of three subsystems: Data Collection, Data Processing, and Data Analytics and Visualization subsystems. The mobile application developed serves as a data collection medium for raw data collection and training data collection. Geo-citizens can upload the data collected to a central server. The uploaded data is verified offline and then pushed to a processing server. The processing server runs the classification algorithm and labels it. This data is fused together and divided into logical segments. A majority voting and amalgamation algorithm then consume this data and arrives at the final output that is saved in the database. MAARG system includes a WebGIS dashboard comprising a back-end Express server that analyses and aggregates information from multiple inputs and showcases it on a browser with the OpenStreetMap as a background layer (WMS). While the proposed system not only uses tracking information but also image information to give an optimized understanding of the road infrastructure. Hence, MAARG processes positional information and attaches an on-the-fly analysis of the data collected from different sensors. This is achieved by collating multiple users data efficiently. This acts as an input to the query engine and visualizer where the users can visualize different spatiotemporal queries. MAARG system is an innovative, collaborative system for road condition monitoring leveraging the existing technologies by information fusion approach. Derived from geo-citizenship, we envision an ecosystem operating through a mobile application on smartphones of geo-citizens for large data collection and executing centralized processing. Prime advantages of such a platform are the multiplicity of data points and the crowd-sourcing model. Data collection from numerous sources ensures statistical advantage and expandable coverage for the monitoring system. Crowd-sourcing model provides reliable coverage and eliminates the need for deploying specialized road monitoring tools. This system is also dynamic to meet the increasing demand and prioritizes the areas of high usage. Overall, the MAARG system can disrupt the existing traditional methods and serve as an alternate innovative solution. This thesis primarily presents the architecture and system design of the three subsystems of the road monitoring system.