Vehicle road navigation to minimize pollutant exposure

Ji Luo, Alexander Vu, M. Barth
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引用次数: 2

Abstract

Intelligent Transportation System (ITS) technology is often aimed at improving vehicle safety and mobility. Lately a number of ITS applications are also focusing on environmental issues such as reducing greenhouse gases (through improved fuel economy) and reducing overall pollutant emissions. Typical environmental ITS applications (e.g., eco-routing) focus on reducing total mass vehicle emissions for generalized areas. To date however, environmental ITS applications haven't considered emissions from a pollutant exposure point-of-view. In this paper, we introduce a new vehicle routing methodology that goes beyond minimizing overall pollutant emissions, instead minimizing pollutant exposure to localized populations along roadways. As part of this effort, a unique modeling suite has been developed to allow for the evaluation of environmental ITS applications from a traffic emissions exposure point of view. For the routing algorithm, the human intake fraction that is commonly used for quantifying emission exposure is modeled and used as a routing cost. Experimental modeling results show that the intake fraction of particulate matter for 5-14 year-old school children on school days can be reduced approximately 80%-90% on a typical schoolday with the implementation of intelligent routing algorithms.
车辆道路导航,尽量减少污染物暴露
智能交通系统(ITS)技术往往旨在提高车辆的安全性和机动性。最近,许多智能交通应用也关注环境问题,如减少温室气体(通过提高燃油经济性)和减少总体污染物排放。典型的环境ITS应用(例如,生态路线)侧重于减少一般地区的总质量车辆排放。然而,到目前为止,环境ITS应用还没有从污染物暴露的角度考虑排放。在本文中,我们介绍了一种新的车辆路线方法,该方法超越了最小化总体污染物排放,而是最小化道路沿线局部人群的污染物暴露。作为这项工作的一部分,我们开发了一个独特的建模套件,以便从交通排放暴露的角度对环境ITS应用进行评估。对于路由算法,通常用于量化排放暴露的人体摄入分数被建模并用作路由成本。实验模拟结果表明,实施智能路由算法后,5-14岁学龄儿童上学日的颗粒物吸入分数可减少约80%-90%。
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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