Public transport coverage fails to keep pace with urbanization and urban expansion,which makes the“last kilometer"problem of residents’travel increasingly prominent”.However,the practice has proved that microc...Public transport coverage fails to keep pace with urbanization and urban expansion,which makes the“last kilometer"problem of residents’travel increasingly prominent”.However,the practice has proved that microcirculation public transportation plays an important role in expanding the coverage of public transportation and promoting the integration of public transportation.Therefore,this paper takes a city bus community as an example.Firstly,it analyses the bus travel demand of commuters connecting to the subway station during the early workday rush hours on basis of IC Big Data,obtains candidate stations of microcirculation bus lines through K-means clustering.Secondly,it establishes the model,the target of which is to minimize the cost residents'travel and bus operation,under the limited condition of walking distance,passenger number,station spacing and departure frequency.Finally,the genetic algorithm is used to find the optimal solution of the model,so it’s no doubt that the most feasible circular bus route is obtained.The results have positive significance for promoting the construction and operation of public transport integration and promoting the convenience and efficiency of public transport travel.展开更多
In recent years,the rise of machine learning has made it possible to further explore large data in various fields.In order to explore the attributes of loyalty of public transport travelers and divide these people int...In recent years,the rise of machine learning has made it possible to further explore large data in various fields.In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters,this paper uses K-means clustering algorithm(K-means)to cluster the holding time,recharge amount and swiping frequency of bus travelers.Then we use Kernel Density Estimation Algorithms(KDE)to analyze the density distribution of the data of holding time,recharge amount and swipe frequency,and display the results of the two algorithms in the way of data visualization.Finally,according to the results of data visualization,the loyalty of users is classified,which provides theoretical and data support for public transport companies to determine the development potential of users.展开更多
文摘Public transport coverage fails to keep pace with urbanization and urban expansion,which makes the“last kilometer"problem of residents’travel increasingly prominent”.However,the practice has proved that microcirculation public transportation plays an important role in expanding the coverage of public transportation and promoting the integration of public transportation.Therefore,this paper takes a city bus community as an example.Firstly,it analyses the bus travel demand of commuters connecting to the subway station during the early workday rush hours on basis of IC Big Data,obtains candidate stations of microcirculation bus lines through K-means clustering.Secondly,it establishes the model,the target of which is to minimize the cost residents'travel and bus operation,under the limited condition of walking distance,passenger number,station spacing and departure frequency.Finally,the genetic algorithm is used to find the optimal solution of the model,so it’s no doubt that the most feasible circular bus route is obtained.The results have positive significance for promoting the construction and operation of public transport integration and promoting the convenience and efficiency of public transport travel.
文摘In recent years,the rise of machine learning has made it possible to further explore large data in various fields.In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters,this paper uses K-means clustering algorithm(K-means)to cluster the holding time,recharge amount and swiping frequency of bus travelers.Then we use Kernel Density Estimation Algorithms(KDE)to analyze the density distribution of the data of holding time,recharge amount and swipe frequency,and display the results of the two algorithms in the way of data visualization.Finally,according to the results of data visualization,the loyalty of users is classified,which provides theoretical and data support for public transport companies to determine the development potential of users.
