DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT

Abstract

Road related accidents have always been a nuisance to drivers and pedestrians alike.Every year countless accidents and deaths occur due to potholes which could have been preventable if there had been a prior warning or if the civic authorities were able to repair these potholes in time. This paper proposes a machine learning based pothole detection system called DeepBus for real time identification of surface irregularities on roads using Internet of Things (IoT). DeepBus uses IoT sensors to detect potholes in real time while an end user is driving vehicles on the road. The location of these potholes would be available on a centrally hosted map which can be accessed by both end users and civic authorities. Thus, it would serve as a warning system to all users as well as a database of potholes with thier locations to the authorities for quick repair and action. We have compared the performance of various machine learning models (Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest and Ensemble Voting) based on different parameters (Accuracy, F-score, Precision and Recall) and identified that Random Forest is the best model for pothole detection.

An improved random forest algorithm and its application to wind pressure prediction

“…ML techniques 4–6 have developed rapidly in recent years and have yielded very good results in classification and regression tasks for solving many complex problems. These technologies have shown great potential in various engineering fields, such as civil engineering, 7–9 structural health monitoring, 10–12 and transportation engineering 13–15 . However, applications of ML for wind resistance in engineering structures are still very limited, Chen et al 16 to obtain the prediction of the mean wind pressure and pulsating wind pressure distribution for the low double‐slope roof structure, combined the test data measured by the authoritative wind tunnel test and analyzed and predicted using artificial neural network, and finally obtained more satisfactory results.…”

“…These technologies have shown great potential in various engineering fields, such as civil engineering, [7][8][9] structural health monitoring, [10][11][12] and transportation engineering. [13][14][15] However, applications of ML for wind resistance in engineering structures are still very limited, Chen et al 16 to obtain the prediction of the mean wind pressure and pulsating wind pressure distribution for the low double-slope roof structure, combined the test data measured by the authoritative wind tunnel test and analyzed and predicted using artificial neural network, and finally obtained more satisfactory results. Fu et al 17,18 used fuzzy neural network trick to analyze the wind tunnel test results of a large-span roof and predicted the wind pressure distribution characteristics of a large-span roof structure, which can comprehensively consider the influence of various factors and effectively deal with problems that are difficult to be solved by conventional approaches.…”

IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

“…Machine Learning aids to determine basic patterns and extract the required information from the large volume of data using computation and statistical process [58]. The use of machine learning models can be used for prediction of alcohol addiction to improve current EHIS [59] [60].…”

Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges

“…Besides simplifying the business processes and reducing errors in verification, its integration with Internet-of-things (IoT) has recently been discussed extensively among the research community. As one of the key enablers of Industry 4.0 and an emerging offshoot of IoT, the Industrial IoT (IIoT) networks are paving their way in various commercial and social sectors such as retailing, manufacturing, logistics, pervasive monitoring, security surveillance, healthcare, and home automation [4][5][6][7]. Moreover, with the recent developments in wireless communications and sensor network technologies, an increasing number of devices are being introduced in the IIoT space, where raw data are locally captured and processed to support decision-based processes.…”

“…A summary of these processes, illustrating a transaction from A to B, is given in Figure 1. Due to the transparent nature of blockchain technology, it is highlighted by the academia and the industry alike, as a potential solution for efficiently managing massive IIoT networks [5,6,9]. Moreover, decentralized IIoT devices and trustless network architectures are expected to play a key role in the advancement of IIoT networks where data will be processed locally at the site of generation and not in a centralized manner.…”