Passenger Assignment for Ridesharing Through Supervised Learning
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Ridesharing systems are transforming urban transportation in an economically feasible, environmentally friendly and socially beneficial way. The Ridesharing problem is an intriguing problem in transportation research and has been studied for several years. The objective is to find efficient assignments for transportation of items through a complex network while respecting the capacity constraints of the available vehicles in the fleet. In recent times, more work has been focused on using data driven and intelligent approaches to solve problems arising in transportation research. In this thesis we aim to solve the ridesharing problem using neural network architectures. Our results indicate that the neural network architecture models generate solutions with reasonable accuracy and consume less computational time.
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Copyright © 2020 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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- 2020
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hamid-passengerassignmentforridesharingthroughsupervised.pdf | 2023-05-05 | Public | Download |