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Identification of Clients Affected b...

Pereira, Miguel Antunes,

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Identification of Clients Affected by a Canceled Journey on Metro Network in Porto /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identification of Clients Affected by a Canceled Journey on Metro Network in Porto // Miguel Antunes Pereira.
作者:	Pereira, Miguel Antunes,
面頁冊數:	1 electronic resource (63 pages)
附註:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
標題:	Probability. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30206359
ISBN:	9798358420038

Identification of Clients Affected by a Canceled Journey on Metro Network in Porto /
Pereira, Miguel Antunes,

Identification of Clients Affected by a Canceled Journey on Metro Network in Porto /Miguel Antunes Pereira. - 1 electronic resource (63 pages)

Source: Masters Abstracts International, Volume: 84-06.

The growth and development of urban areas have been constant year by year. The urge to improve the public transport network not only to face this challenge but also to answer the pollution caused by the fast growth of the society opens the possibility for numerous improvements. Nowadays, the emphasis that each city places on its public transportation network can be seen by examining the continuous improvements to the network tracks and equipment in order to provide a more reliable service. Although many fields have been studied in recent years, it is critical to improve the customer experience in order to deliver a quality service to the client. In this digital revolution currently being faced, offering a more intelligent and trustworthy service is the key to staying on top. Therefore, Metro of Porto challenged itself to improve the customer experience by facing the daily challenges that this operation can bring. One challenge is to notice their clients of an eventual breakdown on the metro network as soon as possible. With this information, the client will plan other ways of making his journey and avoid a massive traffic flow on the metro stations.This thesis intends to investigate the possibility of improving customer experience by evaluating data collected from consumers who have a monthly metro subscription in Porto. In order to achieve this it will be proposed an approach using sequential pattern mining. By examining the frequent item sets and rules generated by a sequential pattern mining algorithm, it will be possible to investigate each individual profile of each network user utilizing this methodology. The obtained output will then contain the information needed to understand if a specific client will be affected by a breakdown of the network.To demonstrate how this approach may be applied in a real-world setting, a Python script will be developed that will mimic a network collapse. The algorithm's result will then be a boolean variable that shows whether or not a user is affected by the simulated breakdown.

English

ISBN: 9798358420038Subjects--Topical Terms:

518898
Probability.

Identification of Clients Affected by a Canceled Journey on Metro Network in Porto /
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520 $a The growth and development of urban areas have been constant year by year. The urge to improve the public transport network not only to face this challenge but also to answer the pollution caused by the fast growth of the society opens the possibility for numerous improvements. Nowadays, the emphasis that each city places on its public transportation network can be seen by examining the continuous improvements to the network tracks and equipment in order to provide a more reliable service. Although many fields have been studied in recent years, it is critical to improve the customer experience in order to deliver a quality service to the client. In this digital revolution currently being faced, offering a more intelligent and trustworthy service is the key to staying on top. Therefore, Metro of Porto challenged itself to improve the customer experience by facing the daily challenges that this operation can bring. One challenge is to notice their clients of an eventual breakdown on the metro network as soon as possible. With this information, the client will plan other ways of making his journey and avoid a massive traffic flow on the metro stations.This thesis intends to investigate the possibility of improving customer experience by evaluating data collected from consumers who have a monthly metro subscription in Porto. In order to achieve this it will be proposed an approach using sequential pattern mining. By examining the frequent item sets and rules generated by a sequential pattern mining algorithm, it will be possible to investigate each individual profile of each network user utilizing this methodology. The obtained output will then contain the information needed to understand if a specific client will be affected by a breakdown of the network.To demonstrate how this approach may be applied in a real-world setting, a Python script will be developed that will mimic a network collapse. The algorithm's result will then be a boolean variable that shows whether or not a user is affected by the simulated breakdown.
520 $a O crescimento e o desenvolvimento das areas urbanas tem sido constantes ano apos ano. A necessidade de melhorar a rede de transporte publico nao so para enfrentar este desafio, mas tambem para responder a poluicao causada pelo rapido crescimento da sociedade abre a possibilidade para inumeras melhorias. Hoje em dia, e possivel perceber a importancia que cada cidade da a sua rede de transporte publico, analisando a constante melhoria das vias e equipamentos da rede para se tornar um servico mais confiavel. Embora varios campos possam ser explorados nos ultimos anos, para prestar um bom servico ao cliente, e essencial melhorar a experiencia do cliente. Nesta revolucao digital que se enfrenta, oferecer um servico mais inteligente e confiavel e a chave para se manter no topo. Assim, a rede de metro do Porto desafiou-se a melhorar a experiencia do cliente, enfrentando os desafios diarios que esta operacao pode trazer. Um desafio e perceber quais os clientes que sao afetados numa eventual falha da rede do metro. Com essas informacoes, o cliente vai planear outras formas de fazer sua viagem e evitar um fluxo macico de trafego nas estacoes do metro.Esta tese pretende explorar a possibilidade de melhoria da experiencia do cliente atraves da analise dos dados obtidos junto dos clientes que possuem assinatura mensal do metro do Porto. E possivel aplicar metodos de extracao de padroes sequenciais para obter o perfil de cada. Este metodo ira fazer com que seja obtido para cada cliente um conjunto de padroes frequentes assim como regras entre os padroes obtidos. Com isto e assim possivel determinar se um determinado cliente vai ser afetado por uma eventual avaria da rede do metro.De modo a demonstrar como esta metodologia pode ser aplicada em tempo real, foi criado um script em Python que ira simular uma avaria da rede de metro e verificar, consoante os dados obtidos, se o cliente e afetado ou nao pela avaria.
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