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A constraint-aware optimized path recommender in a crowdsourced environment

机译：一个约束感知优化路径推荐在众群环境中

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摘要

Recommending an optimized path for a large crowd poses a unique challenge to existing routing algorithms due to the interactions between users and the dynamic changes over road networks. Industries, researchers and end users show an enormous interest in crowdsourced data comprising social networks and user-generated content to remain updated with their concerns. In this paper, we present a data collection framework that helps users to find optimized routes in a dynamic environment. We have developed a data collection framework to collect dynamic road conditions via a set of location-based services to support a very large Hajj crowd by capturing their locations using smartphones. We also collect geotagged social network data that provides more details about road conditions. The system leverages geotagged crowdsourced information to identify constraints such as accidents, congestions, and roadblocks. Moreover, by continuously collecting real-time geotagged data of moving users, the system can also find the flow of traffic and road conditions. We propose a spatial grid index to compute the optimized path, and to identify the affected users within impact zones. The plan is to test the whole application and back-end server during Hajj 2016, where over three million pilgrims from all over the world gather to perform their rituals.
机译：由于用户与道路网络之间的动态变化，建议为大众人群的优化路径对现有路由算法构成了唯一的挑战。行业，研究人员和最终用户对包括社交网络和用户生成的内容的众群数据表示巨大兴趣，以保持更新的问题。在本文中，我们提出了一个数据收集框架，帮助用户在动态环境中找到优化的路由。我们开发了一种数据收集框架，通过一系列基于位置的服务来收集动态道路状况，以通过使用智能手机捕获其位置来支持一个非常大的HAJJ人群。我们还收集地理标记的社交网络数据，提供有关道路状况的更多细节。该系统利用地理标记的众群信息来识别事故，拥塞和障碍等限制。此外，通过持续收集移动用户的实时地理标记数据，系统还可以找到流量和道路状况的流动。我们提出了一个空间网格索引来计算优化的路径，并识别受影响区域内的受影响的用户。该计划是在2016年的HAJJ期间测试整个应用程序和后端服务器，来自世界各地的超过300万朝圣者聚集在一起来执行他们的仪式。

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