Assessment of the level of congestion based on natural language processing
CHEN Hongxin1, CUI Jian1, ZHANG Zuo1,2, YAO Danya1
1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
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.
陈洪昕, 崔健, 张佐, 姚丹亚. 基于自然语言处理的交通拥堵程度评价[J]. 清华大学学报（自然科学版）, 2016, 56(3): 287-293.
CHEN Hongxin, CUI Jian, ZHANG Zuo, YAO Danya. Assessment of the level of congestion based on natural language processing. Journal of Tsinghua University(Science and Technology), 2016, 56(3): 287-293.
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2016, Vol. 56 
