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

Evaluating roadway surface rating technologies

Final Report 09/01/2013 to 9/30/14The key project objective was to assess and evaluate the feasibility and accuracy of custom software used in smartphones to measure road roughness from the accelerometer data collected from smartphones and compare results with PASER (Pavement Surface and Evaluation Rating System) and IRI (International Roughness Index) measurement values collected from the same roadway segments. This project is MDOT’s first large implementation of a customized Android smartphone to collect road roughness data using a methodology developed from previous research work performed by UMTRI. Accelerometer data collection was performed via Android-based smartphones using a customized software application called DataProbe. During the project’s initial phase smartphones were installed in each of nine Michigan Department of Transportation (MDOT) vehicles driven by MDOT employees. These same vehicles also were used during 2012 and 2013 tocollect data on road distress using PASER Ratings for comparison. The DataProbe software application was used to collect data and transmit it to a University of Michigan Transportation Research server, where it was sorted, stored, and analyzed. All MDOT regions are represented in this analysis that compares road roughness ratings for nearly 6000 one tenth of a mile road segments. For the second phase of the project, road distress (PASER Rating) data was collected in 2014 simultaneously with an MDOT vehicle equipped with an IRI device and two DataProbe smartphones and two UMTRI vehicles equipped with five DataProbe smartphones. The analysis of the 2012 and 2013 data found that there were a number of significant predictors of IRI road roughness including: the phone and the vehicle used to collect the data, the speed of the vehicle collecting the data, the type of road surface, date of data collection, and accelerometer variance. By including quadratic terms to adjust for non-linear relationships and interactions among the predictors studied in this project, the multiple regression model predicted nearly 45 percent and 43 percent of the variance in IRI scores, respectively. An analysis of commonly used IRI categories (3 level/5 level) using ordinal logistic regression found that DataProbe accurately predicted these categories 68/71 percent of the time (2012 data), 77/76 percent of the time (2013 data). Analysis of the data collected in 2014 showed multiple regression models with variance among accelerometer measurements and speed accounting for 37 percent of the variance, while the ordinal logistic regression accurately predicted the IRI (3 level/5 level) categories 86/83 percent of the time. These results are promising when considering the near term application of the DataProbe technology for smaller locales that drive over their local roads more often, generating web-based road roughness visuals of each of the roads in their jurisdiction. In the longer term, state-wide road roughness measurement may be performed through the crowd-sourcing model available through Connected Vehicle initiatives, where all vehicles will be equipped with devices that support safety applications as well as other applications such as those that measure road roughness.Michigan Department of Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/111891/1/103192.pd

Surface monitoring of road pavements using mobile crowdsensing technology

Pavement-surface characteristics should be considered during road maintenance for safe and comfortable driving. A detailed and up-to-date report of road-pavement network conditions is required to optimize a maintenance plan. However, manual road inspection methods, such as periodic visual surveys, are time-consuming and expensive. A common technology used to address this issue is SmartRoadSense, a collaborative system for the automatic detection of road-surface characteristics using Global Positioning System receivers and triaxial accelerometers contained in mobile devices. In this study, the results of the SmartRoadSense surveys conducted on Provincial Road 2 (SP2) in Salerno, Italy, were compared with the Distress Cadastre data for the same province and the pavement condition indices of different sections of the SP2. Although the effectiveness of the crowdsensing-based SmartRoadSense was found to vary with the distress type, the system was confirmed to be very efficient for monitoring the most critical road failures

Kwanalytics: A geographic information system for crowdsourcing and aggregating road surface quality information from smartphones

Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020Road surface quality information is critical to road users when navigating road networks, and road authorities when making decisions on road infrastructure. However, not many systems exist that readily provide this information. This work connects prior work and proposes a geographic information system for crowdsourcing and aggregating probabilistic road surface quality information collected from users’ smartphones from various times and at different geographic locations.Ashesi Universit

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

6G Connected Vehicle Framework to Support Intelligent Road Maintenance using Deep Learning Data Fusion

The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV), to drive the development of transformative intelligent transport applications for addressing smart city challenges. A critical challenge is timely and efficient road infrastructure maintenance. This paper proposes an intelligent hierarchical framework for road infrastructure maintenance that exploits the latest developments in 6G communication technologies, deep learning techniques, and mobile edge AI training approaches. The proposed framework abides with the stringent requirements of training efficient machine learning applications for CAV, and is able to exploit the vast numbers of CAVs forecasted to be present on future road networks. At the core of our framework is a novel Convolution Neural Networks (CNN) model which fuses imagery and sensory data to perform pothole detection. Experiments show the proposed model can achieve state of the art performance in comparison to existing approaches while being simple, cost- effective and computationally efficient to deploy. The proposed system can form part of a federated learning framework for facilitating large scale real-time road surface condition monitoring and support adaptive resource allocation for road infrastructure maintenance

Road Management Systems to Support Bicycling: A Case Study of Montreal’s Bike Network

Bicycling is a sustainable mode of transportation given its health benefits, reduced air and noise pollution, savings in fuel consumption, and role in shifting demand away from the automobile. A significant increase of bicycle users is an aim of many cities around the world. Responding to this, various cities announced their strategies to extend and/or upgrade their bikeway networks. However, there is a disconnection between the strategies to support bicycles and road management systems, which are typically used for optimal scheduling of maintenance and interventions for roads’ infrastructure. Traditional road management systems consider neither the need to sustain bicycle pathways at good levels of service, nor consider bicycling demand to prioritize their selection. This thesis extends road management systems to support bicycling networks. This enables the ability to optimally allocate available resources for sustaining the surface of bicycle pathways in good condition, and implement physically-separated bicycle lanes to enhance safety conditions and encourage bicycle ridership. A simple formulation of bicycle demand is proposed; it employs the capabilities of smartphones for collecting and estimating bicycling demand based on GPS trajectories of cyclists. Goal programming optimization is applied to address scheduling of maintenance and upgrade investments of pathways. Two scenarios are investigated with different annual budgets. The results show that the first scenario allows a rapid upgrade of existing bicycle lanes to protected paths while accomplishing good conditions of pavements. However, the second scenario is not able to prevent the deterioration of pavement segments

Monitoraggio della regolarità delle pavimentazioni stradali mediante valutazione dei livelli di accelerazione

Una delle principali funzioni di una pavimentazione stradale è garantire un piano di rotolamento regolare e poco deformabile, tale da corrispondere a prestabiliti requisiti minimi di sicurezza e comfort. A tale scopo, di fondamentale importanza è l’implementazione di un opportuno sistema di monitoraggio del livello di regolarità che interessi una specifica strada, al fine di consentire tempestivi interventi di manutenzione atti al ripristino di livelli prestazionali accettabili, prima dell’insorgere di un eccessivo degrado della pavimentazione con conseguenti effetti dannosi sulla circolazione stradale (e.g. aumento dell’incidentalità, riduzione della qualità di marcia percepita dagli utenti, incremento del carico dinamico trasmesso alla pavimentazione, etc.). Nella presente tesi è stata presa in esame la possibilità di impiego di un metodo di valutazione della regolarità delle pavimentazioni (in particolare quelle urbane) basato sul rilievo delle accelerazioni verticali misurate all’interno dei veicoli stradali. A tale scopo, sono state prese in esame diverse strumentazioni tra cui una piattaforma inerziale con ricevitore GPS sincronizzato, in aggiunta alla quale sono stati impiegati due cellulari smartphone con l’obiettivo di valutare l’utilizzo di tali apparecchiature oramai diffuse e disponibili a un costo contenuto. Il parametro prescelto per l'implementazione di un siffatto metodo è l'accelerazione verticale ponderata in frequenza descritta nella norma ISO 2631-1. Sono quindi state effettuate una serie di analisi finalizzate alla comparazione di tale indicatore con i principali indici di regolarità attualmente in uso, valutando nel contempo le possibili problematiche concernenti la metodologia proposta (e.g. scelta del punto di osservazione, frequenza di campionamento dei sensori) e l'influenza delle condizioni operative reali durante i rilievi dei livelli di accelerazione sui valori così misurati. Infine, è stata esaminata la ripetibilità della grandezza proposta effettuando diversi passaggi su di una medesima sezione, comparando inoltre tale indicatore della qualità di marcia con il Pavement Condition Index (PCI); essendo quest'ultimo un indicatore dello stato di ammaloramento complessivo delle pavimentazioni stradali