Forest path condition monitoring based on crowd-based trajectory data analysis

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Ambient Intelligence and Smart Environments Pub Date : 2021-01-01 DOI:10.3233/ais-200586

Francisco Arcas-Túnez, Fernando Terroso-Sáenz

引用次数: 2

Abstract

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

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基于人群轨迹数据分析的森林路径状态监测

基于移动群体感知(MCS)范式的道路信息采集系统(RIASs)的开发在过去几年得到了广泛的研究。从这个意义上说，大多数现有的基于mcs的RIASs都专注于城市道路网络，并假设了一个基于汽车的场景。然而，关注农村和乡村道路网络的方法缺乏。从这个意义上说，森林小径被许多不同的人用于各种各样的娱乐和体育活动，它们也可能受到不同问题或阻碍它们的障碍物的影响。因此，本文介绍了基于MCS的农村路网监测框架SAMARITAN。SAMARITAN分析从健身应用Strava中提取的骑行者的时空轨迹，从而发现目标路网中的潜在障碍物。该框架已在Cieza市(西班牙)的真实森林路径网络中进行了评估，显示出相当有希望的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

4.30

自引率

17.60%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.