Abstract

Accident detection systems help reduce fatalities stemming from car accidents by decreasing the response time of emergency responders. Smartphones and their onboard sensors (such as GPS receivers and accelerometers) are promising platforms for constructing such systems. This paper provides three contributions to the study of using smartphone-based accident detection systems. First, we describe solutions to key issues associated with detecting traffic accidents, such as preventing false positives by utilizing mobile context information and polling onboard sensors to detect large accelerations. Second, we present the architecture of our prototype smartphone-based accident detection system and empirically analyze its ability to resist false positives as well as its capabilities for accident reconstruction. Third, we discuss how smartphone-based accident detection can reduce overall traffic congestion and increase the preparedness of emergency responders. Document type: Part of book or chapter of book

Abstract

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Article

Abstract

This paper reports the results of vibration and meteorological observations of a buildng built on Hashima, Nagasaki, southern part of Japan, which was registered as a World Heritage Site in 2015 [1], and analyzes its dynamic characteristics. Hashima is called "Battleship Island" because its appearance resembles a battleship due to the many buildings standing upon it. The structure contributing most to the battleship appearance is No. 3 Building. An accelerometer and a weather sensor have been installed in No. 3 Building for long-term measurements. Multi-modal analysis of vibration and meteorological data was performed for 5 months, and the dynamic characteristics of No. 3 Building were examined.