Crowdsourcing Urban Bicycle Level of Service Measures

众包城市自行车服务水平衡量标准

基本信息

批准号：
1636915
负责人：
Vanessa Frias-Martinez
金额：
$ 20万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-15 至 2019-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636915&HistoricalAwards=false
关键词：
Crowdsourcing Urban Bicycle Level Service

项目摘要

Over the past two decades, cities across the country have experienced a tremendous growth in cycling. As cities expand and improve their bicycle networks, local governments and bicycle associations are looking into ways of making cycling in urban areas safer. However, one of the main obstacles in decreasing the number of bicycle crashes is the lack of information regarding cycling safety at the street level. Historically, Bicycle Level of Service (BLOS) models have been used to measure street safety. Unfortunately, these models require extensive information about each particular roadway section, which often times is not available. This EArly-concept Grant for Exploratory Research (EAGER) project will provide innovative tools to automatically estimate street safety levels from crowd-sourced citizens' complaints as well as to shed some light into the traffic-related reasons behind such safety values. Ultimately, the outcomes of this project will contribute to the overall vision for Smart and Connected Communities (S&CC) by helping to reduce the number of crashes and human fatalities in the city using large streams of data collected from connected citizens. The project has strong support from multiple local institutions including Bike Share and local transportation departments.From a technical perspective, the main innovation will be the ability to automatically compute cycling safety measures using information extracted from citizen-generated complaints at very fine-grained spatio-temporal scales. For that purpose, the project will use data mining and machine-learning techniques to extract relevant quantitative and textual features from the crowd-sourced data. The expected outcomes of this project will be: (a) accurate and interpretable models to estimate street safety levels from user-generated data; (b) a set of easy-to-interpret, actionable items for local Departments of Transportation to improve cycling experiences and general safety; and (c) a dataset with user-generated complaints, cycling videos and safety levels per road segments to share with other researchers so as to advance the state of the art in data-driven cycling safety.

在过去的二十年里，全国各地的城市都经历了自行车运动的巨大增长。随着城市扩大和改善自行车网络，地方政府和自行车协会正在寻找使城市地区骑自行车更安全的方法。然而，减少自行车事故数量的主要障碍之一是缺乏有关街道自行车安全的信息。历史上，自行车服务水平 (BLOS) 模型曾被用来衡量街道安全。不幸的是，这些模型需要有关每个特定道路部分的大量信息，而这些信息通常是无法获得的。这个早期概念探索性研究资助 (EAGER) 项目将提供创新工具，根据众包公民的投诉自动估算街道安全水平，并揭示此类安全值背后与交通相关的原因。最终，该项目的成果将利用从互联公民收集的大量数据，帮助减少城市中的车祸和人员死亡数量，从而为智能互联社区 (S&CC) 的总体愿景做出贡献。该项目得到了包括共享单车和当地交通部门在内的多个当地机构的大力支持。从技术角度来看，主要创新将是能够利用从公民产生的投诉中提取的信息，以非常细粒度的空间自动计算骑行安全措施。时间尺度。为此，该项目将使用数据挖掘和机器学习技术从众包数据中提取相关的定量和文本特征。该项目的预期成果将是： (a) 根据用户生成的数据估算街道安全水平的准确且可解释的模型； (b) 为地方交通部门提供一套易于解释、可操作的项目，以改善骑行体验和总体安全； (c) 包含用户投诉、骑行视频和每个路段安全级别的数据集，可与其他研究人员共享，以推进数据驱动的骑行安全的最新技术水平。