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A constraint-aware optimized path recommender in a crowdsourced environment

机译：众包环境中的约束感知优化路径推荐器

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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：由于用户之间的交互作用以及道路网络的动态变化，为大量人群推荐最佳路径对现有的路由算法提出了独特的挑战。行业，研究人员和最终用户对包含社交网络和用户生成的内容的众包数据表现出极大的兴趣，以保持他们关注的最新信息。在本文中，我们提出了一个数据收集框架，该框架可帮助用户在动态环境中找到优化的路线。我们已经开发了一个数据收集框架，可通过一组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非常多的朝Ha人群来支持他们的位置。我们还将收集带有地理标签的社交网络数据，这些数据可提供有关道路状况的更多详细信息。该系统利用经过地理标记的众包信息来识别约束，例如事故，交通拥堵和路障。此外，通过连续收集移动用户的实时地理标记数据，系统还可以查找交通流量和道路状况。我们提出了一个空间网格索引来计算优化路径，并确定受影响区域内的受影响用户。该计划计划在2016年朝Ha期间测试整个应用程序和后端服务器，来自世界各地的三百万以上朝圣者齐聚一堂，以执行其仪式。

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