Using mobility data to estimate bus arrival time in a smart city
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/29662 |
Resumo: | Connected cities use pervasive information and communication technologies, especially sensing and data analysis, to offer new decision support tools and services. One of the key use cases of connected cities is smart mobility, which addresses the use of computational tools to enhance transportation systems and private mobility. In this context, reliable information systems concerning bus arrival times provide useful services for end-users. Porto is often presented as a smart city, which has deployed a Vehicular Ad-Hoc Network with more than 600 vehicles (buses, taxis and garbage trucks) generating data regarding the GPS location of the (moving) nodes. Traces of buses location offer new possibilities to understand the city mobility patterns. The goal of this work is to develop a system for estimating bus arrival times, using Machine Learning techniques in the data available from the existing vehicular network. The developed system has three main modules: (1) line detection, responsible for inferring possible lines on which a bus may be operating; (2) machine learning model capable of predicting travel times between two bus stops and (3) service linking the current context of buses’ locations with the historical prediction model that returns predictions for a given destination stop. The prediction results obtained are in line with those reported in the literature. A proof-of-concept mobile application for the citizen was also developed, demonstrating the real-life applicability of the system. |
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Using mobility data to estimate bus arrival time in a smart cityMachine LearningEstimated Time of ArrivalVehicular Ad-Hoc NetworkSmart CitiesConnected cities use pervasive information and communication technologies, especially sensing and data analysis, to offer new decision support tools and services. One of the key use cases of connected cities is smart mobility, which addresses the use of computational tools to enhance transportation systems and private mobility. In this context, reliable information systems concerning bus arrival times provide useful services for end-users. Porto is often presented as a smart city, which has deployed a Vehicular Ad-Hoc Network with more than 600 vehicles (buses, taxis and garbage trucks) generating data regarding the GPS location of the (moving) nodes. Traces of buses location offer new possibilities to understand the city mobility patterns. The goal of this work is to develop a system for estimating bus arrival times, using Machine Learning techniques in the data available from the existing vehicular network. The developed system has three main modules: (1) line detection, responsible for inferring possible lines on which a bus may be operating; (2) machine learning model capable of predicting travel times between two bus stops and (3) service linking the current context of buses’ locations with the historical prediction model that returns predictions for a given destination stop. The prediction results obtained are in line with those reported in the literature. A proof-of-concept mobile application for the citizen was also developed, demonstrating the real-life applicability of the system.As cidades inteligentes utilizam informação pervasiva e tecnologias de comunicação, nomeadamente a detecção e a análise de dados, para fornecer novas ferramentas e serviços de apoio à decisão. Um dos principais casos de uso é a mobilidade inteligente, que aborda o uso de ferramentas computacionais para melhorar os sistemas de transporte e a mobilidade privada. Neste contexto, os sistemas de informação fiáveis relativos a tempos de chegada dos autocarros proporcionam serviços úteis para os utilizadores finais. A cidade do Porto é frequentemente apresentada como uma cidade inteligente, que tem implementada uma rede veicular que incorpora mais de 600 veículos (autocarros, táxis e camiões do lixo) gerando dados sobre a localização GPS dos nós (móveis). Os registos de localização dos autocarros oferecem novas possibilidades para compreender os padrões de mobilidade da cidade. O objetivo desta dissertação é o desenvolvimento de um sistema capaz de estimar o tempo de chegada dos autocarros, utilizando técnicas de aprendizagem automática sobre os dados disponíveis da rede veicular existente. O sistema desenvolvido tem três módulos principais: (1) deteção de linhas, responsável por inferir possíveis linhas que um autocarro possa estar a operar; (2) modelo de aprendizagem automática capaz de prever o tempo de viagem de um autocarro entre duas paragens e (3) serviço que liga o contexto atual da localização dos autocarros com o modelo de previsão histórica que devolve as previsões para uma dada paragem de destino. Os resultados de previsão obtidos estão em linha com os relatados na literatura. Como prova de conceito, também foi desenvolvida uma aplicação móvel para os passageiros, demonstrando a aplicabilidade prática do sistema.2020-10-30T15:39:08Z2019-07-01T00:00:00Z2019-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/29662engTavares, Ana Filipa Ferreirainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:57:25Zoai:ria.ua.pt:10773/29662Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:56.693216Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using mobility data to estimate bus arrival time in a smart city |
title |
Using mobility data to estimate bus arrival time in a smart city |
spellingShingle |
Using mobility data to estimate bus arrival time in a smart city Tavares, Ana Filipa Ferreira Machine Learning Estimated Time of Arrival Vehicular Ad-Hoc Network Smart Cities |
title_short |
Using mobility data to estimate bus arrival time in a smart city |
title_full |
Using mobility data to estimate bus arrival time in a smart city |
title_fullStr |
Using mobility data to estimate bus arrival time in a smart city |
title_full_unstemmed |
Using mobility data to estimate bus arrival time in a smart city |
title_sort |
Using mobility data to estimate bus arrival time in a smart city |
author |
Tavares, Ana Filipa Ferreira |
author_facet |
Tavares, Ana Filipa Ferreira |
author_role |
author |
dc.contributor.author.fl_str_mv |
Tavares, Ana Filipa Ferreira |
dc.subject.por.fl_str_mv |
Machine Learning Estimated Time of Arrival Vehicular Ad-Hoc Network Smart Cities |
topic |
Machine Learning Estimated Time of Arrival Vehicular Ad-Hoc Network Smart Cities |
description |
Connected cities use pervasive information and communication technologies, especially sensing and data analysis, to offer new decision support tools and services. One of the key use cases of connected cities is smart mobility, which addresses the use of computational tools to enhance transportation systems and private mobility. In this context, reliable information systems concerning bus arrival times provide useful services for end-users. Porto is often presented as a smart city, which has deployed a Vehicular Ad-Hoc Network with more than 600 vehicles (buses, taxis and garbage trucks) generating data regarding the GPS location of the (moving) nodes. Traces of buses location offer new possibilities to understand the city mobility patterns. The goal of this work is to develop a system for estimating bus arrival times, using Machine Learning techniques in the data available from the existing vehicular network. The developed system has three main modules: (1) line detection, responsible for inferring possible lines on which a bus may be operating; (2) machine learning model capable of predicting travel times between two bus stops and (3) service linking the current context of buses’ locations with the historical prediction model that returns predictions for a given destination stop. The prediction results obtained are in line with those reported in the literature. A proof-of-concept mobile application for the citizen was also developed, demonstrating the real-life applicability of the system. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-01T00:00:00Z 2019-07 2020-10-30T15:39:08Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/29662 |
url |
http://hdl.handle.net/10773/29662 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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