Spatial Patterns in Traffic Accident Risks: Residential Location Based Analyses

交通事故风险的空间模式:基于住宅位置的分析

基本信息

项目摘要

The spatial categorisation of traffic accidents is typically made on the basis of places of accident. This approach, however, leaves only limited room for conclusions about population-based risks, because the place of accident does not necessarily match a victim's place of residence. This project employs a residence-based approach to geographically analyse population-based accident risks. Combined place-of-residence and place-of-accident-based analyses are undertaken as well.The analyses are based on accident casualty counts provided by the Lower Saxony Ministry of the Interior covering the period 2006-2013. They include the number of casualties, categorised by severity of accident, age, sex and transport mode. The data include postal codes of the casualties' place of residence and place of accident. These data will be matched with sociodemographic, economic, exposure, transport-related and urban form attributes.Doing so, associations between these context attributes and population-based accident risks can be studied in considerably more detail than to date. This will be done for Lower Saxony as a whole on the municipality level, and for the Region of Hannover on the micro-spatial level of neighbourhoods. In terms of methodology, descriptive statistics and maps will be used as well as regression analysis and multiple equation models (structural equation models). Detailed analyses will be undertaken for population groups based on age and sex, social status of the residential neighbourhood (for the Region of Hannover) and transport mode. Spatial differences in risk trends will be studied as well.
交通事故的空间分类通常是根据事故地点进行的。然而,这种方法只留下了有限的空间来得出基于人口的风险的结论,因为事故地点不一定与受害者的居住地相匹配。该项目采用以居住地为基础的方法,对以人口为基础的事故风险进行地理分析。还进行了基于居住地和事故地点的综合分析,分析依据是下萨克森州内政部提供的2006-2013年期间的事故伤亡统计数字。这些数字包括按意外严重程度、年龄、性别和交通工具分类的伤亡数字。这些数据包括伤亡人员居住地和事故地点的邮政编码。这些数据将与社会人口、经济、暴露、与交通相关的属性和城市形态属性相匹配。这样,这些背景属性与基于人口的事故风险之间的关联可以比目前更详细地研究。这将在市政一级针对整个下萨克森州进行,在社区微观空间层面针对汉诺威地区进行。在方法方面,将使用描述性统计和地图以及回归分析和多元方程模型(结构方程模型)。将根据年龄和性别、居民区(汉诺威地区)的社会地位和交通方式对人口群体进行详细分析。风险趋势的空间差异也将被研究。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Professor Dr. Joachim Scheiner其他文献

Professor Dr. Joachim Scheiner的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Professor Dr. Joachim Scheiner', 18)}}的其他基金

Transport infrastructure, urban structure and the trip to school - a case study of Luenen (NRW)
交通基础设施、城市结构和上学之旅——以 Luenen(北威州)为例
  • 批准号:
    392795144
  • 财政年份:
    2017
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Quantitative and qualitative gendered time-use in travel-based multitasking
基于旅行的多任务处理中的定量和定性性别时间使用
  • 批准号:
    366195702
  • 财政年份:
    2017
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Change in long-distance travel: uncovering travel activity trends, inequalities, and dynamics over the life course
长途旅行的变化:揭示生命历程中的旅行活动趋势、不平等和动态
  • 批准号:
    404319843
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

相似海外基金

Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems
职业:物理正则化机器学习理论:为智能移动系统建模随机交通流模式
  • 批准号:
    2234289
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems
职业:物理正则化机器学习理论:为智能移动系统建模随机交通流模式
  • 批准号:
    2047268
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2020
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2019
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Research on NoC system robust to fluctuation of traffic patterns
抗流量模式波动的NoC系统研究
  • 批准号:
    18K11226
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Multi-scale hierarchical congestion cotrol strategies based on stability properties of dya-to-day traffic flow patterns in metropolitan express networks
城域快运网络中基于每日交通流模式稳定性的多尺度分层拥塞控制策略
  • 批准号:
    18H01551
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic
在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通
  • 批准号:
    RGPIN-2017-05950
  • 财政年份:
    2017
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了