A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City

IF 2.4 3区工程技术 Q3 TRANSPORTATION

Journal of Transportation Safety & Security Pub Date : 2021-10-27 DOI:10.1080/19439962.2021.1994681

Yuxuan Wang, Ruoxin Xiong, Hao Yu, Jie Bao, Zhao Yang

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引用次数: 3

Abstract

Abstract This study introduces a hybrid Latent Dirichlet Allocation (LDA) model to excavate hidden crash patterns from the large-scale crash dataset. External semantic descriptions have been attached to raw GPS coordinates of crash events. The K-means clustering algorithm is first applied to determine land use characteristics of crash points by grouping surrounding Points of Interests (POIs). Then, each crash record is transformed into a formalized label consisting of land use, Annual Average Daily Traffic (AADT), and time stamps, allowing the analysis of massive traffic crash data as document corpora. Finally, a data-driven modeling approach based on the LDA is conducted to discover hidden crash patterns from traffic crash records combining the external semantic information. The approach is verified using motor vehicle crash data in Manhattan County of New York City. The novel semantic analysis of crash records provides an effective method to investigate the hidden information in traffic crashes. Identifying spatial-temporal patterns on motor vehicle crashes would provide insights into underlying traffic behaviors for intelligent policy-making and resource allocation.

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机动车碰撞记录的语义嵌入方法:以纽约市曼哈顿区交通安全为例

摘要本文引入一种混合潜狄利克雷分配(Latent Dirichlet Allocation, LDA)模型，从大规模碰撞数据集中挖掘隐藏的碰撞模式。外部语义描述已附加到碰撞事件的原始GPS坐标。首先应用k均值聚类算法，通过对周边兴趣点(poi)进行分组，确定碰撞点的土地利用特征。然后，将每个碰撞记录转换为由土地使用、年平均每日交通量(AADT)和时间戳组成的正式标签，从而允许将大量交通碰撞数据作为文档语料库进行分析。最后，提出了一种基于LDA的数据驱动建模方法，结合外部语义信息从交通碰撞记录中发现隐藏的碰撞模式。该方法使用纽约市曼哈顿县的机动车碰撞数据进行了验证。新的碰撞记录语义分析方法为研究交通碰撞中隐藏的信息提供了一种有效的方法。识别机动车碰撞的时空模式将为智能决策和资源配置提供对潜在交通行为的洞察。

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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来源期刊

Journal of Transportation Safety & Security TRANSPORTATION-

CiteScore

6.00

自引率

15.40%

发文量