Blockchain For Intelligent Transport System

Intelligent Transportation System (ITS) is gaining attention but at the same time, road accidents, congestion, delays, etc. have also increased. Relative information about such events is vital. Such information can be presented in legal processes as digital proof. Availability of the information is not a problem as multidimensional data have been recorded all the time by ITS. Recording all the information in ITS arises the problem of fetching relevant information and removing other facts and figure that are not required to describe certain situations such as an accident. To address this issue, we analyze road accident data and reduce various dimensions with Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-negative Matrix Factorization (NMF). We conduct comparative analysis with three datasets where error rate for PCA is 32% with Dataset1. Likewise, error rate for LDA and NMF are 36% and 35%, receptively. While keeping in mind that such reduced data is helpful in many legal processes, we introduce Blockchain in the framework. Blockchain can make data immutable thus can be considered as digital proof. Blockchain also requires a smart contract in this situation between insurance companies to collect data in case of any uncertain situation. Such analysis can offer a different point of views and trends in data. Information can be more explainable to define the situation and helps to develop a friendly environment for day-to-day customers. The proposed framework provides dimensionality reduction of data that eventually reduce the data dimension to store in Blockchain.

Automated summary

Intelligent Transportation System (ITS) is generating vast amounts of data, which can be effectively reduced using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-negative Matrix Factorization (NMF). The integration of Blockchain technology enhances data integrity and the use of smart contracts between insurance companies can streamline data collection for uncertain situations.