ABSTRACT

This chapter discusses the data analytics and signal processing challenges for indirect monitoring of transport infrastructure in smart cities. In indirect methods, the condition of the infrastructure is monitored using the vibrations on an instrumented truck or train as it passes by. These methods can be used for the monitoring of road pavements, highway and railway bridges and railway tracks. A major advantage of these methods is that no sensors or data acquisition system needs to be installed on the structure. A common challenge in most indirect methods is how to transform large quantities of measured data into helpful information. In this chapter, a framework is proposed for implementing the concept of Internet of Things in the context of indirect monitoring of transport infrastructure. The main data science challenges in the context of indirect monitoring of transport infrastructure are also discussed and recommendations are provided for future development. A decision making process is adapted which addresses big data complexities involved in indirect monitoring. The proposed framework would improve the safety and accessibility of smart cities.