From 8321f3c68cdb150bf6d92e4879601695a8784895 Mon Sep 17 00:00:00 2001 From: Joao Artur Leles Date: Tue, 7 Nov 2023 01:57:31 -0300 Subject: [PATCH] =?UTF-8?q?Criando=20documenta=C3=A7=C3=A3o=20sobre=20redu?= =?UTF-8?q?=C3=A7=C3=A3o=20dos=20ru=C3=ADdos?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "docs/Estudos/Remo\303\247\303\243oRuidos.md" | 86 ++++++++++++++++++ docs/Estudos/assets/Ruido.png | Bin 0 -> 120708 bytes 2 files changed, 86 insertions(+) create mode 100644 "docs/Estudos/Remo\303\247\303\243oRuidos.md" create mode 100644 docs/Estudos/assets/Ruido.png diff --git "a/docs/Estudos/Remo\303\247\303\243oRuidos.md" "b/docs/Estudos/Remo\303\247\303\243oRuidos.md" new file mode 100644 index 0000000..1cdcab3 --- /dev/null +++ "b/docs/Estudos/Remo\303\247\303\243oRuidos.md" @@ -0,0 +1,86 @@ +# Remoção de ruídos + +## 1. O que é um ruído? + +Ao transformar os arquivos disponibilizados pela SEMOB (Secretaria de Mobilidade e Transporte) em um grafo, algumas paradas acabaram por ficar muito próximas. Essas paradas foram chamadas de ruídos. + +Eles passaram a se mostrar um problema, ao afetar o cálculo da melhor rota. + +A imagem abaixo mostra um exemplo de ruído: + +![Imagem1](./assets/Ruido.png) + + +## 2. Eliminando ruídos + +Tendo em vista o problema que os ruídos haviam se tornado, foi necessário encontrar uma maneira de contorna-lo. + +Para isso foram idelizados alguns algortimos que poderiam resolver esses problemas. Os principais foram o de Média e Mediana e o da utilização do DBSCAN. + +### 2.1 Média e Mediana + +**Preparação para filtragem de paradas que não fazem parte de uma rota.** + +O projeto consiste em calcular a média das distâncias entre as paradas e em seguida colocar um limiar abaixo para que as paradas muito próximas uma das outras sejam eliminadas. + +$$ média\ das\ distâncias = \sum_{i=0}^{n} \frac{distâncias_{i}}{n-1}$$ + +$n: número\ de\ paradas$ + +As etapas de dessa filtragem são: + +**Passo 1: Preparação dos Dados** + +- Deixar o dataframe das paradas em ordem ao longo da rota e indexadas. +- Calular as distâncias entre paradas consecutivas. + +**Passo 2: Definição de Limiares** + +- Calcular a média das distâncias entre as paradas. +- Calcular o desvio padrão das distâncias entre as paradas. +- Definir um limiar que seja uma combinação da média e do desvio padrão, por exemplo, `limiar = média + k * desvio_padrao`, onde `k` é um fator que você pode ajustar com base na sensibilidade desejada. + +**Passo 3: Identificação e Eliminação de Paradas Próximas** + +- Iniciar um loop para percorrer todas as paradas da rota, começando pela primeira. +- Para cada parada atual, calcular a distância até a próxima parada. +- Se a distância for menor que o limiar definido no Passo 2, eliminar a próxima parada (atualize os dados originais para remover essa parada). +- Reordenar a lista de paradas, se necessário, após a eliminação. +- Voltar ao passo 3º caso não tenha chegado até a ultima parada. + +**Passo 4: Repetição e Verificação** + +- Repetir o Passo 3 até que não seja mais possível eliminar paradas (ou seja, a distância entre todas as paradas seja maior que o limiar). +- Isso garantirá que todas as paradas próximas demais sejam removidas. +- Verificar no gráfico se a qualidade da representação da rota melhorou visualizando os resultados no gráfico. + +**Resultado** + +Utilizar a média e mediana para reduzir os ruídos se mostrou um desafio, muitas das paradas acabavam sumindo, resultando em um grafo com muito menos paradas. + +Após esses resultados foi decidido que este não era o melhor metodo para a resolução do problema,sendo assim necessário procurar outro algoritmo capaz de reduzir os ruídos. + +### 2.2 DBSCAN + +O DBSCAN (Density-Based Spatial Clustering of Applications with Noise) é um algoritmo de clustering utilizado em mineração de dados e análise de padrões. Ele difere de outros métodos de clustering, como o K-means, por sua capacidade de descobrir grupos de formas arbitrárias e lidar com ruídos nos dados. O DBSCAN baseia-se na densidade de pontos em um espaço multidimensional, onde os pontos densos são considerados parte de um grupo, enquanto os pontos menos densos são tratados como ruído. + +O funcionamento do DBSCAN é relativamente simples. Ele começa selecionando um ponto de dados aleatório e verifica a densidade de pontos próximos a ele. Se houver pontos suficientes em uma vizinhança definida por um raio específico, o algoritmo inicia um novo cluster a partir desse ponto e explora seus vizinhos de maneira recursiva. Isso continua até que não haja mais vizinhos densos a serem adicionados ao cluster, marcando assim o fim do grupo. Os pontos que não se encaixam em nenhum cluster ou têm densidade muito baixa são classificados como ruído. + +**Resultado** + +O DBSCAN se mostrou uma excelente ferramenta ao conseguir eliminar grande parte dos ruídos do CSV utilizado para confeccionar os grafos. + +## 3. Proxímo Passo + +O proxímo passo é aplicar o DBSCAN no atual CSV para assim conseguir eliminar os ruídos, garantindo uma melhor precisão para o algortimo de melhor rota da API. + +## 4. Links Úteis + +- [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) +- [Uso do DBSCAN em trajetórias](https://www.mdpi.com/2220-9964/6/3/63) + +## 5. Histórico de Versão + +| Versão | Alteração | Responsável | Data | +| - | - | - | - | +| 1.0 | Criando documentação sobre ruídos | João Artur Leles | 23/10 | \ No newline at end of file diff --git a/docs/Estudos/assets/Ruido.png b/docs/Estudos/assets/Ruido.png new file mode 100644 index 0000000000000000000000000000000000000000..f9cfc7a3e25dec17d3b8923dafb490fe86489de7 GIT binary patch literal 120708 zcmYIv1yEaEv@R`$;#M4rySqzqf?IJZ?(R^axVr{-m*Nh^i@UoQcY^cIfA8FR3>lKJ zCzI^#v({%TTuDI+837*w3JMAtBrUE21@)mBxH-VV0?+V!aUKGHJ~@km)Zl=hpKvB& zz+*fY2~8JOdovezLnl)xb31!mQ$}ZFCsR{9XA66m3+QekU=h`Si^QBv4P7kl?MT!t zZB3z^P0dNz*h%bNT}aqifIF-#eC*tOtXyie4hc|DBv2r65jBs@(=2xn!u|Q?i}hu2 zR@F-Jd^z`MN_+GNV`wfkE74b#Ur->pqdgxn1bBGLI4F?xM^a`=7?o0ZO5BfmVm2&I zJunW5Rc3chD;p<}A*;od#stcGE_sOSW80~@S+=>l{y5k<+u2uY+Zna+dp_*`s(pgC z3rCqrrW)OcKMP;S|G9V}MC;e=4pT2qub%7~yn7|5>bF5R7VZsZobMJU#+W?T@ituG z?Hvktrz+8m%oI64UVTr6x{Nf~m<&N){m^nf5~=AV4!!x_lXZKGls8}AdbdFDkd*c~ 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