Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-29 DOI:10.1038/s41597-024-04111-4
Amirhossein Hassani, Anna Nicińska, Arkadiusz Drabicki, Ewa Zawojska, Gabriela Sousa Santos, Grzegorz Kula, Henrik Grythe, Jakub Zawieska, Joanna Jaczewska, Joanna Rachubik, Katarzyna Archanowicz-Kudelska, Katarzyna Zagórska, Maciej Grzenda, Magdalena Kubecka, Marcin Luckner, Michał Jakubczyk, Michał Wolański, Nuria Castell, Paweł Gora, Pål Wilter Skedsmo, Satia Rożynek, Szymon Horosiewicz
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Abstract

The present study describes the data sets produced in Warsaw, Poland with the aim of developing tools and methods for the implementation of human-centred and data-driven solutions to the enhancement of sustainable mobility transition. This study focuses on school commutes and alternatives to private cars for children drop off and pick up from primary schools. The dataset enables the complex analysis of interactions between determinants of transport mode choice, revealed choices, and air quality impact. We draw on four data collection methods, namely, (i) air quality and noise sensors' measurements, (ii) in-person observations of transport behaviours, (iii) travel diaries, and (iv) social surveys. Moreover, all trip data from travel diaries are complemented with the calculated attributes of alternative travel modes. The data produced in the project can be also combined with publicly available information on air quality, public transport schedules, and traffic flows. The present data sets help to open new venues for interdisciplinary analyses of sustainable mobility transition effectiveness and efficiency.

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空气质量和运输行为:来自波兰华沙的传感器、现场和调查数据。
本研究描述了在波兰华沙产生的数据集,旨在开发工具和方法,以实施以人为本和数据驱动的解决方案,以增强可持续的移动性转型。这项研究的重点是学校通勤和替代私家车接送孩子上下学。该数据集能够对运输方式选择的决定因素、揭示的选择和空气质量影响之间的相互作用进行复杂的分析。我们利用了四种数据收集方法,即(i)空气质量和噪音传感器的测量,(ii)交通行为的亲自观察,(iii)旅行日记,以及(iv)社会调查。此外,所有来自旅行日记的旅行数据都与计算出的替代旅行模式属性相补充。该项目产生的数据还可以与有关空气质量、公共交通时刻表和交通流量的公开信息相结合。目前的数据集有助于为可持续流动、过渡有效性和效率的跨学科分析开辟新的场所。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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