Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach
Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Modelling |
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| Online Access: | https://www.mdpi.com/2673-3951/5/4/84 |
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| author | Ricardo Montoya-Zamora Luisa Ramírez-Granados Teresa López-Lara Juan Bosco Hernández-Zaragoza Rosario Guzmán-Cruz |
| author_facet | Ricardo Montoya-Zamora Luisa Ramírez-Granados Teresa López-Lara Juan Bosco Hernández-Zaragoza Rosario Guzmán-Cruz |
| author_sort | Ricardo Montoya-Zamora |
| collection | DOAJ |
| description | Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings. |
| format | Article |
| id | doaj-art-b15d0f873a98445da9190828c4f3bd91 |
| institution | OA Journals |
| issn | 2673-3951 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj-art-b15d0f873a98445da9190828c4f3bd912025-08-20T02:01:10ZengMDPI AGModelling2673-39512024-11-01541601161710.3390/modelling5040084Predicting Cyclist Speed in Urban Contexts: A Neural Network ApproachRicardo Montoya-Zamora0Luisa Ramírez-Granados1Teresa López-Lara2Juan Bosco Hernández-Zaragoza3Rosario Guzmán-Cruz4Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, Querétaro 76010, MexicoFacultad de Ingeniería, Campus Amazcala, Universidad Autónoma de Querétaro, Carr. Chichimequillas-Amazcala Km 1 s/n, Amazcala, Querétaro 76265, MexicoBicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings.https://www.mdpi.com/2673-3951/5/4/84predictionrecurrent neural networkcyclist speedurban area |
| spellingShingle | Ricardo Montoya-Zamora Luisa Ramírez-Granados Teresa López-Lara Juan Bosco Hernández-Zaragoza Rosario Guzmán-Cruz Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach Modelling prediction recurrent neural network cyclist speed urban area |
| title | Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach |
| title_full | Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach |
| title_fullStr | Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach |
| title_full_unstemmed | Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach |
| title_short | Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach |
| title_sort | predicting cyclist speed in urban contexts a neural network approach |
| topic | prediction recurrent neural network cyclist speed urban area |
| url | https://www.mdpi.com/2673-3951/5/4/84 |
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