利用社交网络的近时间预测来缓解流量
Exploiting Near Time Forecasting From Social Network To Decongest Traffic
摘要：
通过预测交通流量来预防交通拥挤是一个重要的问题，因为它可以有效地利用交通资源。社交网络提供有关人类活动和社会事件的信息。因此，在社交网络的帮助下，我们可以提取哪些人将参加一个特定的事件（在附近的时间），并可以估计交通流量的基础上，它开辟了一个广泛的研究领域，需要有一个框架的交通管理，可以捕捉到现实生活中的行为的基本参数，并提供一种方法来验证和评估新的想法。在本文中，我们提出了一个框架和一个系统来模拟一个城市的交通系统，人类和他们的社会网络的构建块。重点介绍了相关参数的选取和框架的模块化设计。我们的框架定义的指标来评估拥塞避免策略。为了显示效用的框架，我们提出了一些策略的公共交通系统的实验研究。
英文摘要：
Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
