Assessment of the level of congestion based on natural language processing

doi:10.16511/j.cnki.qhdxxb.2016.21.034

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Abstract In recent years, an increasing amount of traffic information has been posted on social media such as micro-blogs. This information provides a new opportunity for traffic analysis using micro-blog traffic data to supplement traditional traffic data. The micro-blog data has been analyzed to identify frequently-used natural language description of traffic conditions with fuzzy assessments used to quantify the subjective feelings of different people describing traffic congestion with natural language. The fuzzy reasoning data fusion method aggregated evaluations by different people describing the congestion of the same section of a road. Videos were collected from three road segments with observers invited to evaluate the road traffic conditions in the videos. The integration results are similar to the real-time traffic scenarios released by Baidu Map, which verify the feasibility of this fuzzy method.

Keywords transportation engineering assessment method fuzzy reasoning natural language

ZTFLH:

U491.25

Issue Date: 15 March 2016

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	CHEN Hongxin
	CUI Jian
	ZHANG Zuo
	YAO Danya

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CHEN Hongxin,CUI Jian,ZHANG Zuo, et al. Assessment of the level of congestion based on natural language processing[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(3): 287-293.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.21.034 OR http://jst.tsinghuajournals.com/EN/Y2016/V56/I3/287

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