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
{"title":"Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland.","authors":"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","doi":"10.1038/s41597-024-04111-4","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1305"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04111-4","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
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.