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Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.7 ) Pub Date : 2020-09-04 , DOI: 10.1177/0361198120946016
Bahar Dadashova 1 , Greg P. Griffin 2 , Subasish Das 1 , Shawn Turner 1 , Bonnie Sherman 3
Affiliation  

Traffic volumes are fundamental for evaluating transportation systems, regardless of travel mode. A lack of counts for non-motorized modes poses a challenge for practitioners developing and managing multimodal transportation facilities, whether they want to evaluate transportation safety or the potential need for infrastructure changes, or to answer other questions about how and where people bicycle and walk. In recent years, researchers and practitioners alike have been using crowdsourced data to supplement the non-motorized counts. As such, several methods and tools have been developed. The objective of this paper is to take advantage of new data sources that provide a limited and biased sample of trips and combine them with traditional counts to develop a practical tool for estimating annual average daily bicycle (AADB) counts. This study developed a direct-demand model for estimating AADB in Texas. Data from 100 stations, installed in 12 cities across the state, was used together with the crowdsourced Strava, roadway inventory, and American Community Survey data to develop the count model for estimating AADB. The results indicate that crowdsourced Strava data is an acceptable predictor of bicycle counts, and when used with the roadway functional class and number of high-income households in a block group, can provide quite an accurate AADB estimate (29% prediction error).



中文翻译:

使用众包Strava数据估算平均每日每日自行车计数

无论交通方式如何,交通量都是评估交通系统的基础。缺乏非机动模式的数量对开发和管理多式联运设施的从业人员提出了挑战,无论他们是想评估运输安全性或基础设施变更的潜在需求,还是要回答有关人们骑自行车和步行的方式和地点的其他问题。近年来,研究人员和从业人员都一直在使用众包数据来补充非机动化计数。这样,已经开发了几种方法和工具。本文的目的是利用提供有限且有偏差的出行样本的新数据源,并将其与传统计数相结合,以开发出一种实用工具来估算年平均每日自行车(AADB)计数。这项研究开发了直接需求模型来估算德克萨斯州的AADB。来自全州12个城市的100个站点的数据与众包的Strava,道路清单和美国社区调查数据一起用于开发估算ADB的计数模型。结果表明,众包的Strava数据是可以接受的自行车数量预测指标,当与道路功能类别和成组的高收入家庭数量一起使用时,可以提供相当准确的AADB估算值(29%的预测误差)。

更新日期:2020-09-05
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