Trip Generation Estimates for Spatial Units based on Built Environment and Geocoded Household Travel Surveys

基于建筑环境和地理编码家庭旅行调查的空间单位出行生成估计

• 批准号: 524931623
• 负责人: Professor Dr.-Ing. Tobias Kuhnimhof
• 金额: --
• 依托单位: Lehrstuhl und Institut für Stadtbauwesen und Stadtverkehr
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/524931623?language=en
• 关键词: Trip; Generation; Estimates; Spatial; Units

TRIGGSY aims at a novel approach to estimate trip rates, specifically the number of terminating trips per spatial unit. Such trip rates form a key input for numerous research and planning uses. However, existing trip generation estimation approaches either require laborious data collection (e.g. FGSV or Trip Generation Handbook approach) or an elaborate modelling process (e.g. travel demand model). The TRIGGSY-approach builds on a) geodata which is available open source in Germany as in many other countries and b) geocoded household travel survey data as is available from the latest German national travel survey “Mobilität in Deutschland 2017” (MiD). The TRIGGSY-approach can be applied nationwide for Germany to estimate trip rates and transferred to other locales with similar data. In the MiD (~1 million recorded trips by ~320,000 survey participants representing travel on an average day) there are 20.000 geographic grid cells (1 km by 1 km) for which trips terminating there have been recorded (return home trips excluded). For a given grid cell x, the number trips recorded in the MiD is influenced by two factors: I. Built environment and socio-economic factors of x and its environment; these determine the number of trips ending in x in reality (i.e. the universe of trips ending in x per day). II. Characteristics of the MiD-sample; naturally, the number of trips recorded ending in x (i.e. the respective sample of trips ending in x) is subject to the number of respondents interviewed in the vicinity of x. TRIGGSY aims at disentangling these two factors in order to establish a statistical relationship between the cell characteristics as the explanatory variables (factors I) and the rates of terminating trips as the explained variable. Poisson-regression is a possible approach, with the number of terminating trips recorded in the MiD scaling linearly with the survey respondents in the vicinity of the respective cell (i.e. the accessible survey respondents). Another possible approach is linear regression, e.g. when using the log of the recorded trips per respondent living in the vicinity (i.e. the trips per accessible respondent). Hence, the number of persons with accessibility to a respective cell in a) the MiD sample and b) the population plays a major role. Therefore, TRIGGSY will evaluate different measures of accessibility. Thus, TRIGGSY develops a model to estimate trip rates per accessible person for small geographic units based on their land use and additional spatial properties, e.g. trips generated per medical practice or per square meter of retail. Combining this with geodata on the accessible residential population allows for nationwide extrapolation of total trip rates. The project validates the TRIGGSY-approach through comparison with existing methods for estimating trip generation and publishes estimated trip rates online.

TRIGGSY的目的是一种新的方法来估计行程率，特别是每空间单元的终止行程的数量。这种出行率构成了许多研究和规划用途的关键投入。然而，现有的行程生成估计方法要么需要费力的数据收集（例如FGSV或行程生成手册的方法）或一个精心设计的建模过程（例如旅行需求模型）。TRIGGSY方法建立在以下基础之上：a）地理数据，该数据在德国和许多其他国家都是开源的; B）地理编码的家庭旅行调查数据，该数据可从最新的德国国家旅行调查“2017年德国移动性”（MiD）中获得。TRIGGSY方法可以在全国范围内应用于德国，以估计出行率，并转移到具有类似数据的其他地区。在MiD中（约320，000名调查参与者记录了约100万次旅行，代表平均每天的旅行），有20，000个地理网格单元（1 km × 1 km），其中记录了终止于此的旅行（不包括回家旅行）。对于给定的网格单元x，MiD中记录的行程数受两个因素影响：x的建成环境和社会经济因素及其环境;这些因素决定了现实中以x结尾的旅行次数（即每天以x结尾的旅行范围）。二. MiD样本的特征;当然，以x结尾的行程记录数量（即以x结尾的行程的相应样本）取决于在x附近的受访者数量。TRIGGSY旨在解开这两个因素，以建立作为解释变量（因素I）的细胞特性和作为解释变量的终止旅行率之间的统计关系。泊松回归是一种可能的方法，MiD中记录的终止行程数量与相应小区附近的调查受访者（即可访问的调查受访者）呈线性比例。另一种可能的方法是线性回归，例如，使用居住在附近的每个受访者的记录行程日志（即每个可接触的受访者的行程）。因此，在a）MiD样本和B）人群中，可访问相应单元格的人数起主要作用。因此，TRIGGSY将评估不同的无障碍措施。因此，TRIGGSY开发了一个模型，用于根据土地使用和其他空间属性（例如，每个医疗实践或每平方米零售店产生的旅行）来估计小地理单元的每个可访问人员的旅行率。将这一数据与关于无障碍居住人口的地理数据相结合，就可以对全国的总出行率进行外推。该项目通过与现有的出行生成估算方法进行比较，验证TRIGGSY方法，并在线发布估算的出行率。