A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data
The major objective of this study is to apply integrated data-driven methods to estimate cyclist risk and discomfort in Berlin based on the OpenSenseMap dataset. The proposed approach makes use of crowd-sourced sensor data collected during cycling (speed, bike vibration, distance to other objects),...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Annals of GIS |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2025.2453550 |
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| author | Bakhtiar Feizizadeh Davoud Omarzadeh |
| author_facet | Bakhtiar Feizizadeh Davoud Omarzadeh |
| author_sort | Bakhtiar Feizizadeh |
| collection | DOAJ |
| description | The major objective of this study is to apply integrated data-driven methods to estimate cyclist risk and discomfort in Berlin based on the OpenSenseMap dataset. The proposed approach makes use of crowd-sourced sensor data collected during cycling (speed, bike vibration, distance to other objects), spatial statistics and multi-criteria decision analysis to provide a continuous estimation of cycling discomfort and risk in the traffic network. We employed cycling traffic volume and route discomfort estimation techniques to determine the spatiotemporal patterns of cycling traffic volume and discomfort levels. Accordingly, a GIS-based multiple criteria analysis approach was applied to map areas with high cycling traffic volume based on the condition of the cycling lanes, environmental, road traffic, land use and sociodemographic characteristics. The results show that the central area of Berlin has a high cycling traffic volume as well as a high level of discomfort. In this context, we found a significant spatial correlation between the cycling traffic volume and discomfort with the land use characteristics such as commercial or residential areas, motor vehicle traffic volume and sociodemographic characteristics in Berlin. Furthermore, results revealed a correlation between intensive traffic volume and commercial zones, schools and university areas. As we identified high-risk cycling directions and their autocorrelation with relevant indicators, the obtained results from this study will support decision-makers and authorities in recognizing the high-risk cycling areas and optimizing the risk areas, which accordingly increase the cycling safety and wellbeing of citizens. |
| format | Article |
| id | doaj-art-30eed1b041a842e09937abf2a96e2a85 |
| institution | Kabale University |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of GIS |
| spelling | doaj-art-30eed1b041a842e09937abf2a96e2a852025-01-22T05:23:38ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912025-01-0111910.1080/19475683.2025.2453550A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor dataBakhtiar Feizizadeh0Davoud Omarzadeh1Department of Remote Sensing and GIS, University of Tabriz, IranInternet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, SpainThe major objective of this study is to apply integrated data-driven methods to estimate cyclist risk and discomfort in Berlin based on the OpenSenseMap dataset. The proposed approach makes use of crowd-sourced sensor data collected during cycling (speed, bike vibration, distance to other objects), spatial statistics and multi-criteria decision analysis to provide a continuous estimation of cycling discomfort and risk in the traffic network. We employed cycling traffic volume and route discomfort estimation techniques to determine the spatiotemporal patterns of cycling traffic volume and discomfort levels. Accordingly, a GIS-based multiple criteria analysis approach was applied to map areas with high cycling traffic volume based on the condition of the cycling lanes, environmental, road traffic, land use and sociodemographic characteristics. The results show that the central area of Berlin has a high cycling traffic volume as well as a high level of discomfort. In this context, we found a significant spatial correlation between the cycling traffic volume and discomfort with the land use characteristics such as commercial or residential areas, motor vehicle traffic volume and sociodemographic characteristics in Berlin. Furthermore, results revealed a correlation between intensive traffic volume and commercial zones, schools and university areas. As we identified high-risk cycling directions and their autocorrelation with relevant indicators, the obtained results from this study will support decision-makers and authorities in recognizing the high-risk cycling areas and optimizing the risk areas, which accordingly increase the cycling safety and wellbeing of citizens.https://www.tandfonline.com/doi/10.1080/19475683.2025.2453550Cycling risk analysisOpenSenseMap datacrowd-sourced sensor dataspatiotemporal analysis |
| spellingShingle | Bakhtiar Feizizadeh Davoud Omarzadeh A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data Annals of GIS Cycling risk analysis OpenSenseMap data crowd-sourced sensor data spatiotemporal analysis |
| title | A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data |
| title_full | A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data |
| title_fullStr | A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data |
| title_full_unstemmed | A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data |
| title_short | A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data |
| title_sort | gis based spatiotemporal modelling approach for cycling risk mapping using crowd sourced sensor data |
| topic | Cycling risk analysis OpenSenseMap data crowd-sourced sensor data spatiotemporal analysis |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2025.2453550 |
| work_keys_str_mv | AT bakhtiarfeizizadeh agisbasedspatiotemporalmodellingapproachforcyclingriskmappingusingcrowdsourcedsensordata AT davoudomarzadeh agisbasedspatiotemporalmodellingapproachforcyclingriskmappingusingcrowdsourcedsensordata AT bakhtiarfeizizadeh gisbasedspatiotemporalmodellingapproachforcyclingriskmappingusingcrowdsourcedsensordata AT davoudomarzadeh gisbasedspatiotemporalmodellingapproachforcyclingriskmappingusingcrowdsourcedsensordata |