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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骑自行车的巨大增长。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，在减少自行车碰撞的数量的主要障碍之一是缺乏有关在街道一级骑自行车的安全信息。从历史上看，自行车服务水平（BLOS）模型一直用于衡量街道安全。不幸的是，这些模型需要关于每个特定路段的大量信息，而这些信息往往是不可用的。EARLY概念探索性研究补助金（EAGER）项目将提供创新工具，从群众来源的公民投诉中自动估计街道安全水平，并揭示这些安全值背后与交通相关的原因。最终，该项目的成果将有助于实现智能互联社区（S CC）的整体愿景，通过使用从互联公民收集的大量数据流来帮助减少城市中的撞车和人员死亡数量。该项目得到了包括Bike Share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非常细粒度的时空尺度上的公民投诉中提取的信息自动计算自行车安全措施。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）准确和可解释的模型，以根据用户生成的数据估计街道安全水平;（B）一套易于解释的可操作项目，供当地交通部门使用，以改善骑自行车的体验和总体安全;以及（c）具有用户生成的投诉的数据集，自行车视频和每个路段的安全水平与其他研究人员分享，以推进数据驱动的自行车安全的最新水平。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Predicting Perceived Cycling Safety Levels Using Open and Crowdsourced Data.

使用开放和众包数据预测感知的骑行安全水平。

DOI：
发表时间：
2018
期刊：
IEEE International Conference on Big Data
影响因子：
0
作者：
Wu, J.
通讯作者：
Wu, J.

Predicting Perceived Level of Cycling Safety for Cycling Trips

预测骑行出行的骑行安全感知水平

DOI：
发表时间：
2019
期刊：
ACM SIGSPATIAL
影响因子：
0
作者：
Wu, Jiahui;Hong, Lingzi;Frias-Martinez, Vanessa
通讯作者：
Frias-Martinez, Vanessa

Understanding Cycling Trip Purpose and Route Choice Using GPS Traces and Open Data

DOI：
10.1145/3314407
发表时间：
2019-03
期刊：
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
影响因子：
0
作者：
Suraj Nair;Kiran Javkar;Jiahui Wu;V. Frías-Martínez
通讯作者：
Suraj Nair;Kiran Javkar;Jiahui Wu;V. Frías-Martínez