利用智能手机和蓝牙信标收集具有人口代表性的自行车骑行 GPS 数据,以了解自行车骑行活动和模式

IF 5.1 2区 工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-10-11 DOI:10.1016/j.tbs.2024.100919
Debjit Bhowmick , Danyang Dai , Meead Saberi , Trisalyn Nelson , Mark Stevenson , Sachith Seneviratne , Kerry Nice , Christopher Pettit , Hai L. Vu , Ben Beck
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引用次数: 0

摘要

自行车骑行 GPS 数据提供了详细的洞察力和个人层面的流动性信息,这对于了解自行车骑行出行行为、提高交通安全和公平性以及开发高时空分辨率的自行车路线选择和交通量估算模型至关重要。然而,针对自行车的大规模 GPS 数据收集研究并不常见,许多现有研究缺乏强大的空间和/或时间覆盖范围,或受到取样偏差的影响,导致这些数据缺乏代表性。此外,在没有人工干预的情况下,从连续采集的 GPS 原始数据中准确检测自行车骑行次数仍是一项挑战。本研究提出了一种新颖的 GPS 数据收集方法,将智能手机应用程序与连接到参与者自行车上的蓝牙信标相结合。在最小启发式后处理的辅助下,我们的方法将数据收集限制在无需参与者干预的骑车行程上,在调查参与度、隐私挑战、参与者工作量和稳健的骑车行程检测之间进行了精心优化。我们的方法应用于收集澳大利亚大墨尔本地区 673 名成年人的 19,782 次自行车出行,时间跨度为八个月和三个季节。从人口统计学(性别、职业和就业类型)、时间和空间模式来看,所收集的数据集代表了潜在的成人自行车骑行人群。男性、中年人和老年人的平均出行长度(中位数 = 4.8 公里)、持续时间(中位数 = 20.9 分钟)和骑车出行频率(中位数 = 2.7 次/周)都更高。有兴趣但担心 "的骑行者(根据盖勒类型学进行分类)骑行频率更高,而 "坚强无畏 "和 "充满活力和自信 "的群体骑行距离更远、时间更长。按骑行距离计算,一半以上的参与者在没有自行车基础设施的公路/街道上骑行,而24% 和17% 的参与者在非公路道路和自行车道上骑行。这一具有人口代表性的数据集将成为城市规划和政策制定的关键。
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Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons
Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.
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来源期刊
CiteScore
9.80
自引率
7.70%
发文量
109
期刊介绍: Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.
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