Fleet routing in transportation networks

This project was undertaken by a group of three students. The purpose of this project was to analyse the taxi movement pattern and find taxi demand in order to optimize the use of taxis in Singapore. Before analyzing the taxi moving pattern, different algorithms should be researched in order to...

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Main Author: Lu, Meihong
Other Authors: Justin Dauwels
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/67523
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-675232023-07-07T17:02:49Z Fleet routing in transportation networks Lu, Meihong Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Business::Accounting::Tax::Mathematical models This project was undertaken by a group of three students. The purpose of this project was to analyse the taxi movement pattern and find taxi demand in order to optimize the use of taxis in Singapore. Before analyzing the taxi moving pattern, different algorithms should be researched in order to have a better understanding about how taxis operate. Some algorithms such as Genetic Programming, Tabu Search, Feasible Mobility on Demand (FMOD) and Real-time taxi dispatch Algorithms (Nearest Vehicle Dispatch and Insertion Heuristic) were researched. To analyse the taxi moving pattern and taxi demand, a huge confidential database was provided by SMART. One month data was provided on 2010 August, with an average 15000 taxis data in one day and 15000 times GPS tracking information for one taxi. Based on the assumptions on similar taxi demand at office hours on weekdays, weekends and public holidays respectively, whole month’s data was divided into these three parts. And the method used to analyze the data was, choosing one week data first, and then increasing to one month. Upon plotting graphs and making movies, some common patterns were found. But the data was chosen by only two days, and the results were accidental. Therefore, Joint and Individual Variation Explained (JIVE) method was applied. Scientific results were generated to estimate taxi travelling patterns and passenger travelling preference on weekdays and weekends. Matlab should be used as main tool to generate useful data and figures for analysis of taxi GPS data. Some movies were made to show the changes of taxis pick-up points within one hour in 5 minutes interval. Some 3D graphs were made to show the highest demand of taxis within one week. The purpose of using these movies and graphs was to have a better vision about the real situation. Bachelor of Engineering 2016-05-17T08:16:46Z 2016-05-17T08:16:46Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67523 en Nanyang Technological University 71 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Business::Accounting::Tax::Mathematical models
spellingShingle DRNTU::Business::Accounting::Tax::Mathematical models
Lu, Meihong
Fleet routing in transportation networks
description This project was undertaken by a group of three students. The purpose of this project was to analyse the taxi movement pattern and find taxi demand in order to optimize the use of taxis in Singapore. Before analyzing the taxi moving pattern, different algorithms should be researched in order to have a better understanding about how taxis operate. Some algorithms such as Genetic Programming, Tabu Search, Feasible Mobility on Demand (FMOD) and Real-time taxi dispatch Algorithms (Nearest Vehicle Dispatch and Insertion Heuristic) were researched. To analyse the taxi moving pattern and taxi demand, a huge confidential database was provided by SMART. One month data was provided on 2010 August, with an average 15000 taxis data in one day and 15000 times GPS tracking information for one taxi. Based on the assumptions on similar taxi demand at office hours on weekdays, weekends and public holidays respectively, whole month’s data was divided into these three parts. And the method used to analyze the data was, choosing one week data first, and then increasing to one month. Upon plotting graphs and making movies, some common patterns were found. But the data was chosen by only two days, and the results were accidental. Therefore, Joint and Individual Variation Explained (JIVE) method was applied. Scientific results were generated to estimate taxi travelling patterns and passenger travelling preference on weekdays and weekends. Matlab should be used as main tool to generate useful data and figures for analysis of taxi GPS data. Some movies were made to show the changes of taxis pick-up points within one hour in 5 minutes interval. Some 3D graphs were made to show the highest demand of taxis within one week. The purpose of using these movies and graphs was to have a better vision about the real situation.
author2 Justin Dauwels
author_facet Justin Dauwels
Lu, Meihong
format Final Year Project
author Lu, Meihong
author_sort Lu, Meihong
title Fleet routing in transportation networks
title_short Fleet routing in transportation networks
title_full Fleet routing in transportation networks
title_fullStr Fleet routing in transportation networks
title_full_unstemmed Fleet routing in transportation networks
title_sort fleet routing in transportation networks
publishDate 2016
url http://hdl.handle.net/10356/67523
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