Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Complex networks have been used to model public transportation systems (PTS) considering the relationship beten bus lines and bus stops. Previous works focused on statistically characterize either the whole network or their individual bus stops and lines. The present work focused on statistically characterize different regions of a city (Curitiba, Brazil) assuming that a passenger could easily access different unconnected bus stops in a geographic area. K-means algorithm was used to partition the bus stops in (K =) 2 to 40 clusters with similar geographic area. Results shod strong inverse relationship (p  −16  and R 2  = 0.74 for K = 40 in a log model) beten the degree and the average path length of clustered bus stops. Regarding Curitiba, it revealed ll and badly served regions (downtown area, and few suburbs in Southern and stern Curitiba, respectively). Some of these ll served regions shod quantitative indication of potential bus congestion. By varying K, city planners could obtained zoomed view of the behavior of their PTS in terms of complex networks metrics.
['Emerson Luiz Chiesse da Silva', 'Marcelo de Oliveira Rosa', 'Keiko Verônica Ono Fonseca', 'Ricardo Lüders', 'Nádia P. Kozievitch']
I am writing a research paper on a idea Combining K-means method and complex network analysis to evaluate city mobility
[{"from": "human", "value": "I am writing a research paper on a idea Combining K-means method and complex network analysis to evaluate city mobility"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Complex networks have been used to model public transportation systems (PTS) considering the relationship beten bus lines and bus stops. Previous works focused on statistically characterize either the whole network or their individual bus stops and lines. The present work focused on statistically characterize different regions of a city (Curitiba, Brazil) assuming that a passenger could easily access different unconnected bus stops in a geographic area. K-means algorithm was used to partition the bus stops in (K =) 2 to 40 clusters with similar geographic area. Results shod strong inverse relationship (p  \u221216  and R 2  = 0.74 for K = 40 in a log model) beten the degree and the average path length of clustered bus stops. Regarding Curitiba, it revealed ll and badly served regions (downtown area, and few suburbs in Southern and stern Curitiba, respectively). Some of these ll served regions shod quantitative indication of potential bus congestion. By varying K, city planners could obtained zoomed view of the behavior of their PTS in terms of complex networks metrics."}]