A spatio-temporal graph neural network framework for predicting usage efficiency of e-scooter sharing services
(2023) In Student thesis series INES NGEM01 20231Dept of Physical Geography and Ecosystem Science
- Abstract
- This thesis focuses on predicting the usage efficiency of e-scooters through the application of a Spatio-Temporal Graph Neural Network (STGNN). The predictions made by the STGNN will be compared with classical machine learning (ML) methods, including Random Forest (RF), Artificial Neural Network (ANN), and Linear Regression (LR). To facilitate the prediction of usage efficiency, the Time to Booking metric was utilized. PyTorch Geometric Temporal was employed to handle the spatio-temporal data modelling. Two STGNN models, namely Long Short-Term Memory R-GCN (LRGCN) and Attention Temporal Graph Convolutional Network (A3T-GCN), will be implemented and evaluated. Initially, usage efficiency will be measured based on the Time to Booking at the... (More)
- This thesis focuses on predicting the usage efficiency of e-scooters through the application of a Spatio-Temporal Graph Neural Network (STGNN). The predictions made by the STGNN will be compared with classical machine learning (ML) methods, including Random Forest (RF), Artificial Neural Network (ANN), and Linear Regression (LR). To facilitate the prediction of usage efficiency, the Time to Booking metric was utilized. PyTorch Geometric Temporal was employed to handle the spatio-temporal data modelling. Two STGNN models, namely Long Short-Term Memory R-GCN (LRGCN) and Attention Temporal Graph Convolutional Network (A3T-GCN), will be implemented and evaluated. Initially, usage efficiency will be measured based on the Time to Booking at the DeSo level. Exploratory variables in both time and space will then be calculated as features for the prediction task. Subsequently, the STGNN models and ML models will utilize these calculated features to predict the usage efficiency. In order to determine the significance of variables in the RF and ANN ML models, permutation feature analysis will be conducted. The results of this analysis indicate that the start hour of the trip and the Point of Interest (POI) category "Lodging" are the most influential variables. This highlights the importance of incorporating temporal variables in prediction tasks and demonstrates the effectiveness of the STGNN approach in handling complex predictive tasks. The overall results indicate that the STGNN models exhibit superior performance compared to the ML methods in predicting the usage efficiency of e-scooters (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9136857
- author
- Chelule, Kelvin LU
- supervisor
- organization
- course
- NGEM01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography, Machine Learning, Deep Learning, Urban mobility, Usage efficiency, Geomatics
- publication/series
- Student thesis series INES
- report number
- 628
- language
- English
- id
- 9136857
- date added to LUP
- 2023-09-08 11:03:37
- date last changed
- 2023-09-08 11:03:37
@misc{9136857, abstract = {{This thesis focuses on predicting the usage efficiency of e-scooters through the application of a Spatio-Temporal Graph Neural Network (STGNN). The predictions made by the STGNN will be compared with classical machine learning (ML) methods, including Random Forest (RF), Artificial Neural Network (ANN), and Linear Regression (LR). To facilitate the prediction of usage efficiency, the Time to Booking metric was utilized. PyTorch Geometric Temporal was employed to handle the spatio-temporal data modelling. Two STGNN models, namely Long Short-Term Memory R-GCN (LRGCN) and Attention Temporal Graph Convolutional Network (A3T-GCN), will be implemented and evaluated. Initially, usage efficiency will be measured based on the Time to Booking at the DeSo level. Exploratory variables in both time and space will then be calculated as features for the prediction task. Subsequently, the STGNN models and ML models will utilize these calculated features to predict the usage efficiency. In order to determine the significance of variables in the RF and ANN ML models, permutation feature analysis will be conducted. The results of this analysis indicate that the start hour of the trip and the Point of Interest (POI) category "Lodging" are the most influential variables. This highlights the importance of incorporating temporal variables in prediction tasks and demonstrates the effectiveness of the STGNN approach in handling complex predictive tasks. The overall results indicate that the STGNN models exhibit superior performance compared to the ML methods in predicting the usage efficiency of e-scooters}}, author = {{Chelule, Kelvin}}, language = {{eng}}, note = {{Student Paper}}, series = {{Student thesis series INES}}, title = {{A spatio-temporal graph neural network framework for predicting usage efficiency of e-scooter sharing services}}, year = {{2023}}, }