SBIR Phase I: Synthetic Household Travel Data Using Third-Party Targeted Marketing and Mobile Phone Data

SBIR 第一阶段：使用第三方定向营销和手机数据合成家庭旅行数据

• 批准号: 1415924
• 负责人: Josephine Kressner
• 金额: $ 15万
• 依托单位: Transport Foundry
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1415924&HistoricalAwards=false
• 关键词: SBIR; Phase; Synthetic; Household; Travel

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project includes improving the transportation planning process through better and more frequent data. An integral part of transportation planning at the federal, state, regional, and local levels of government involves testing infrastructure, policy, or population changes with travel demand models. Improved travel demand models enable more accurate predictions and more efficient funding to achieve important regional transportation goals, such as decreasing congestion on a specific corridor, enhancing regional economic growth, improving safety, and attainment of air quality standards. Unfortunately household travel surveys, the backbone data for these models, are increasingly expensive. They are so prohibitively expensive that sample sizes typically represent less than 0.5% of the population, and they are only collected about every ten years. Furthermore, only 5-10% of households respond to surveys, creating a significant nonresponse bias. This SBIR Phase I project aims to create a replacement for traditional household travel surveys with up-to-date, inexpensive, readily available, and representative data. The goal is to transform the practice of travel and urban modeling, helping the nation achieve important transportation and infrastructure goals for the future. This Small Business Innovation Research (SBIR) Phase I project will determine whether synthetic household travel data can be created by combining targeted marketing (i.e. consumer) data and anonymous mobile phone data from many different sources to create realistic synthetic household travel data for a full population in such a way that individual privacy is protected while near real-time travel data is provided to decision-makers. The process will use statistical weighting, imputation, simulation, and data fusion techniques to create synthetic household travel data at a fraction of the cost of current survey-based data collection methods. By fusing data together to create inexpensive, up-to-date, and detailed synthetic household travel data at regular intervals for nearly 100% of the population, researchers and decision-makers alike will be far better equipped to make important transportation decisions that affect our lives each day.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力包括通过更好和更频繁的数据改善交通规划过程。联邦、州、地区和地方各级政府交通规划的一个组成部分涉及到用旅行需求模型测试基础设施、政策或人口变化。改进的旅行需求模型可以更准确地预测和更有效地提供资金，以实现重要的区域交通目标，例如减少特定走廊的拥堵、促进区域经济增长、提高安全性和达到空气质量标准。不幸的是，作为这些模型基础数据的家庭旅行调查越来越昂贵。它们是如此昂贵，以至于样本大小通常只代表不到0.5%的人口，而且它们每十年才收集一次。此外，只有5-10%的家庭回应了调查，造成了显著的非回应偏差。SBIR第一阶段项目旨在用最新的、廉价的、现成的和有代表性的数据取代传统的家庭旅行调查。其目标是改变旅行和城市建模的实践，帮助国家实现未来重要的交通和基础设施目标。这个小企业创新研究（SBIR）第一阶段项目将确定是否可以通过结合目标营销（即消费者）数据和来自许多不同来源的匿名手机数据来创建合成家庭旅行数据，从而为全体人口创建真实的合成家庭旅行数据，从而在向决策者提供近乎实时的旅行数据的同时保护个人隐私。该过程将使用统计加权、输入、模拟和数据融合技术，以目前基于调查的数据收集方法的一小部分成本创建合成的家庭旅行数据。通过将数据融合在一起，为几乎100%的人口定期创建廉价、最新、详细的综合家庭旅行数据，研究人员和决策者都将更好地做出影响我们每天生活的重要交通决策。