Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas.
Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas.
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DOI：
10.1016/j.aap.2017.06.012
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Jie Bao;Pan Liu;Hao Yu;Chengcheng Xu
中科院分区：
文献类型：
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作者：
Jie Bao;Pan Liu;Hao Yu;Chengcheng Xu
本研究的主要目的是探讨如何将人类活动的信息在城市地区的碰撞使用Twitter的签到数据的空间分析。本研究使用了从美国洛杉矶市收集的数据来说明这一过程。收集了以下五类数据：碰撞数据、人类活动数据、传统的交通风险变量、道路网络属性和社会人口数据。通过Python开发了一个网络爬虫，自动从Twitter签到数据中收集场地类型信息。人类活动分为七类所获得的场地类型。收集到的数据被汇总到896个交通分析区（TAZ）。开发了地理加权回归（GWR）模型来建立TAZ中报告的碰撞次数与各种影响因素之间的关系。进行了比较分析，以比较GWR模型的性能，认为传统的交通暴露变量，仅基于Twitter的人类活动变量，传统的交通暴露和基于Twitter的人类活动变量。模型规范的结果表明，人类活动变量显着影响的崩溃计数在TAZ。对比分析结果表明，同时考虑传统交通暴露和人类活动变量的模型具有最高的R2和最低的AICc值，拟合优度最好。这一发现似乎证实了将人类活动信息纳入使用Twitter签到数据进行碰撞空间分析的好处。
The primary objective of this study was to investigate how to incorporate human activity information in spatial analysis of crashes in urban areas using Twitter check-in data. This study used the data collected from the City of Los Angeles in the United States to illustrate the procedure. The following five types of data were collected: crash data, human activity data, traditional traffic exposure variables, road network attributes and social-demographic data. A web crawler by Python was developed to collect the venue type information from the Twitter check-in data automatically. The human activities were classified into seven categories by the obtained venue types. The collected data were aggregated into 896 Traffic Analysis Zones (TAZ). Geographically weighted regression (GWR) models were developed to establish a relationship between the crash counts reported in a TAZ and various contributing factors. Comparative analyses were conducted to compare the performance of GWR models which considered traditional traffic exposure variables only, Twitter-based human activity variables only, and both traditional traffic exposure and Twitter-based human activity variables. The model specification results suggested that human activity variables significantly affected the crash counts in a TAZ. The results of comparative analyses suggested that the models which considered both traditional traffic exposure and human activity variables had the best goodness-of-fit in terms of the highest R2and lowest AICc values. The finding seems to confirm the benefits of incorporating human activity information in spatial analysis of crashes using Twitter check-in data.