Estimation of Road Roughness Condition and Ghat Complexity Analysis Using Smartphone Sensors

Our system works on producing the result sets which shows the road conditions and ghat complexity during traveling. We all use the map but it does not show potholes and bumps in the road. Our system analyzes the data through the sensors of mobile phone such as sensors like accelerometer, orientation sensor, and magnetometer to analyze the ghat complexity and roads quality we use the GPS system of an android phone

Irregularity Finding in Roads Conditions using Data Mining: A Survey

Road conditions play a vital role now days. Irregularity in road surface can cause accidents, vehicle failure and discomfort in drivers and passengers. Governments spend lots of amount every year in maintenance of roads for keeping roads in proper condition. But more maintenance work can increase the traffic, causing disturbance in road users. To avoid disturbances caused by road irregularity,this system can detect road irregularity using Smartphone sensors. The approach is based on data mining. In this, it used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final outputs show that it is possible to find out road irregularity

Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.Peer reviewe

Anomaly detection in roads with a data mining approach

Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final results show that it is possible detect road anomalies using only a smartphone.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020)This research is sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion nº 002797/2015 (INNOVCAR 2015-2018)info:eu-repo/semantics/publishedVersio

Real-time sensor data integration in vertical transport systems

In this project, mobile connectivity and an innovative approach to sensor data gathering and integration have been employed to automate maintenance inspection, performance monitoring and ride quality measurement in vertical transportation systems. An Inertial Navigation System (INS) has been proposed, implemented and tested to track lift car movement profile. The inherent characteristics of vertical motion have been used to minimize errors and obtain higher accuracy in the integration results. The measurement of a correlation between kinematic profiles constructed from lift-car tracking data compared to its nominal values provides key information on the lift condition at any time. A frequency analysis was applied to processing vibrations and noise data, effectively adding another dimension to the lift ride quality measurement. This approach enabled lift performance profiles to be compiled automatically and transmitted in real time, which significantly rationalized and improved the process of maintenance inspection and monitoring. An advanced prototype, AdInspect, has been produced, with the full set of described features. Industry partners are currently evaluating it

Machine Learning Approaches to Road Surface Anomaly Assessment Using Smartphone Sensors

Road surface quality is an essential component of roadway infrastructure that leads to better driving standards and reduces risk of traffic accident. Traditional road condition monitoring systems fall short of current need for quick responses to maintain road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles and with the ubiquitous use of smartphone for personal use and navigation, smartphone based road condition assessment has gained prominence. We propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focusses on classification of three main class labels- smooth road, pothole and deep transverse cracks. We investigate our conjecture that using features from all three axes of the sensors provide more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results consistently show that models trained with features from all axes of the smartphone sensors perform better than models that use only one axis. This shows that there is information in the vibration signals along all three axis for road anomalies. We also observe that the use of neural networks provide significantly accurate data classification. The approaches discussed here can be implemented on a larger scale to monitor road for defects that present a safety risk to commuters as well as provide maintenance information to relevant authorities

スマートフォンセンサーを用いた道路路面粗さの広域簡易推計手法の開発

首都大学東京, 2014-09-30, 博士（工学）首都大学東