通过众包和MapReduce来感知道路拥堵

Proceedings of the 14th International Conference on Information Processing in Sensor Networks Pub Date : 2015-04-13 DOI:10.1145/2737095.2742931

Ayush Jain, Saswat Raj, Harshit, R. Misra, B. M. Baveja

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引用次数: 3

摘要

道路拥堵已成为发展中国家城市的一个主要问题，造成大量延误、燃料浪费和道路事故。要妥善处理，必须留意道路挤塞的情况。道路摄像头等方法需要大量投资，而众包方法会产生大量冗余数据。本文提出了一种利用事件感知捕获相关众包数据来估计道路交通拥堵的新方法，并利用MapReduce高效地生成分析结果。

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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Road congestion sensing via crowdsourcing and MapReduce

Road congestion has become a major problem in cities in developing countries resulting in massive delays, wastage of fuel and road accidents. For proper handling it is essential to observe the road congestion patterns. Methods like on-road cameras, etc. require huge investments whereas crowdsourcing methods generate large amount of redundant data. This paper presents a new approach using event sensing to capture relevant crowdsourced data to estimate road traffic congestion and utilizes MapReduce for generating analytics efficiently.

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Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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