Optimising public transit using big data and machine learning

Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. In this thesis, the main objective is to optimise existing public transit services by utilising big data without...

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Main Author: Lee, Kelvin
Other Authors: Su Rong
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/173342
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1733422024-05-20T06:21:51Z Optimising public transit using big data and machine learning Lee, Kelvin Su Rong Interdisciplinary Graduate School (IGS) Energy Research Institute @ NTU (ERI@N) RSu@ntu.edu.sg Engineering Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. In this thesis, the main objective is to optimise existing public transit services by utilising big data without provision for extra resources. We propose innovative methods and examine the extent to which public transit can be improved through data-driven optimisation alone. In the first part of the thesis, we explore a predictive optimisation framework that employs machine learning techniques to forecast passenger demand, which are then used to optimise a demand-responsive transit service. Our findings demonstrate that using the predictive optimisation framework results in superior and more resilient solutions than relying on point estimates. We also characterised the relationship between prediction accuracy and the quality of solutions from downstream optimisation tasks. In the second part of the thesis, we investigate the potential of leveraging large amounts of high-resolution data collected by transit operators to improve public transit services. We investigate how utilising travel time and demand data obtained at higher frequencies in optimisation models can lead to better synchronisation and thus passenger experience. Furthermore, we explore the potential of opportunistically operating limited-stop services alongside existing all-stop services, based on observed travel times and demand to reduce passenger travel time. In conclusion, this thesis introduces multiple techniques for transit operators to utilise their existing data to conduct data-driven optimisation of their services. Our findings indicate that with careful planning and inventive methods, big data and machine learning can aid public transit operators in running their services more efficiently, thereby making them more attractive to passengers. Doctor of Philosophy 2024-02-20T07:37:40Z 2024-02-20T07:37:40Z 2023 Thesis-Doctor of Philosophy Lee, K. (2023). Optimising public transit using big data and machine learning. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173342 https://hdl.handle.net/10356/173342 10.32657/10356/173342 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Lee, Kelvin
Optimising public transit using big data and machine learning
description Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. In this thesis, the main objective is to optimise existing public transit services by utilising big data without provision for extra resources. We propose innovative methods and examine the extent to which public transit can be improved through data-driven optimisation alone. In the first part of the thesis, we explore a predictive optimisation framework that employs machine learning techniques to forecast passenger demand, which are then used to optimise a demand-responsive transit service. Our findings demonstrate that using the predictive optimisation framework results in superior and more resilient solutions than relying on point estimates. We also characterised the relationship between prediction accuracy and the quality of solutions from downstream optimisation tasks. In the second part of the thesis, we investigate the potential of leveraging large amounts of high-resolution data collected by transit operators to improve public transit services. We investigate how utilising travel time and demand data obtained at higher frequencies in optimisation models can lead to better synchronisation and thus passenger experience. Furthermore, we explore the potential of opportunistically operating limited-stop services alongside existing all-stop services, based on observed travel times and demand to reduce passenger travel time. In conclusion, this thesis introduces multiple techniques for transit operators to utilise their existing data to conduct data-driven optimisation of their services. Our findings indicate that with careful planning and inventive methods, big data and machine learning can aid public transit operators in running their services more efficiently, thereby making them more attractive to passengers.
author2 Su Rong
author_facet Su Rong
Lee, Kelvin
format Thesis-Doctor of Philosophy
author Lee, Kelvin
author_sort Lee, Kelvin
title Optimising public transit using big data and machine learning
title_short Optimising public transit using big data and machine learning
title_full Optimising public transit using big data and machine learning
title_fullStr Optimising public transit using big data and machine learning
title_full_unstemmed Optimising public transit using big data and machine learning
title_sort optimising public transit using big data and machine learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/173342
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