The complexity in transportation systems: the study of a bus loop

We present a study of buses serving commuters through bus stops located in a loop as a complex system. We adopt the approaches of mathematical analysis, agent-based simulations, and machine learning in this study. Our aim is to attain bus scheduling configurations that minimise the average waiting t...

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Main Author: Vismara, Luca
Other Authors: Chew Lock Yue
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/157945
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1579452023-03-05T16:37:27Z The complexity in transportation systems: the study of a bus loop Vismara, Luca Chew Lock Yue Interdisciplinary Graduate School (IGS) Complexity Institute lockyue@ntu.edu.sg Engineering::Mathematics and analysis Science::Physics Engineering::Civil engineering::Transportation We present a study of buses serving commuters through bus stops located in a loop as a complex system. We adopt the approaches of mathematical analysis, agent-based simulations, and machine learning in this study. Our aim is to attain bus scheduling configurations that minimise the average waiting time of the commuters. By performing simplified dynamical and statistical formulations, we delve into the essence of bus-to-bus and bus-to-commuters interactions. Our mathematical analysis cum computer simula- tions uncovered the effects of the interactions on (a) the rate at which buses bunches; (b) the efficiency of a proposed synchronized bunched bus configuration in serving a bus loop where commuters arrive in periodic spikes in one of the bus stops; and (c) the benefits of decoupling a set of bus stops to form the express bus configuration. Through the ma- chine learning technique of reinforcement learning, we found that holding and no-boarding strategies emerge as the elementary low-level actions for active control of staggered buses to optimally serve regular bus stops. Moreover, reinforcement learning had revealed that a novel semi-express bus configuration is an optimal set-up for an autonomous bus loop system, with the enhanced performance a consequence of the system situating at the edge-of-chaos. Doctor of Philosophy 2022-05-16T09:46:49Z 2022-05-16T09:46:49Z 2022 Thesis-Doctor of Philosophy Vismara, L. (2022). The complexity in transportation systems: the study of a bus loop. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157945 https://hdl.handle.net/10356/157945 10.32657/10356/157945 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::Mathematics and analysis
Science::Physics
Engineering::Civil engineering::Transportation
spellingShingle Engineering::Mathematics and analysis
Science::Physics
Engineering::Civil engineering::Transportation
Vismara, Luca
The complexity in transportation systems: the study of a bus loop
description We present a study of buses serving commuters through bus stops located in a loop as a complex system. We adopt the approaches of mathematical analysis, agent-based simulations, and machine learning in this study. Our aim is to attain bus scheduling configurations that minimise the average waiting time of the commuters. By performing simplified dynamical and statistical formulations, we delve into the essence of bus-to-bus and bus-to-commuters interactions. Our mathematical analysis cum computer simula- tions uncovered the effects of the interactions on (a) the rate at which buses bunches; (b) the efficiency of a proposed synchronized bunched bus configuration in serving a bus loop where commuters arrive in periodic spikes in one of the bus stops; and (c) the benefits of decoupling a set of bus stops to form the express bus configuration. Through the ma- chine learning technique of reinforcement learning, we found that holding and no-boarding strategies emerge as the elementary low-level actions for active control of staggered buses to optimally serve regular bus stops. Moreover, reinforcement learning had revealed that a novel semi-express bus configuration is an optimal set-up for an autonomous bus loop system, with the enhanced performance a consequence of the system situating at the edge-of-chaos.
author2 Chew Lock Yue
author_facet Chew Lock Yue
Vismara, Luca
format Thesis-Doctor of Philosophy
author Vismara, Luca
author_sort Vismara, Luca
title The complexity in transportation systems: the study of a bus loop
title_short The complexity in transportation systems: the study of a bus loop
title_full The complexity in transportation systems: the study of a bus loop
title_fullStr The complexity in transportation systems: the study of a bus loop
title_full_unstemmed The complexity in transportation systems: the study of a bus loop
title_sort complexity in transportation systems: the study of a bus loop
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157945
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