CityNet: A Deep Learning Model for Trip Demand Prediction
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This research focused on developing a deep learning model for trip demand prediction. The research work explored the effect of demographic, and land-use types on trip demand by society. A new model, titled CityNet, was developed, which captures correlations in trip data by learning the socio-economic and land-use features of city regions. The model predicts the number of incoming and outgoing trips to all city regions and then utilizes these values to estimate trip demand between region pairs. Our experimental results show that CityNet achieves a 56% improved error rate compared to several time-series forecasting techniques and neural network methods, in addition to a 15% improvement compared to two variants of our model.
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Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2021
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seet-citynetadeeplearningmodelfortripdemandprediction.pdf | 2023-05-05 | Public | Download |