Taking Guangzhou as a case,this paper adopted a questionnaire survey to gather first-hand data and analyzed the characteristics and influencing factors of private car travel in Chinese cities.As the research indicated...Taking Guangzhou as a case,this paper adopted a questionnaire survey to gather first-hand data and analyzed the characteristics and influencing factors of private car travel in Chinese cities.As the research indicated,trip purposes of private car travel are mainly commute and business affairs with a more flexible trip in the urban core area.And trip intensities are concentrated in a certain extent,with trip frequency being lower in the urban core area than the peripheral area.In addition,the trip time has two significant peaks occurring in the morning and afternoon,and one trough in the midday.And trip spatial distribution is mainly within commute with both residence and employment in urban area and inward commute with residence in suburban area while employment in urban area.Both kinds of commutes direct to the urban area.The study also shows that the characteristics of private car travel are principally influenced by two aspects:travelers' attributes and urban characteristics.The main travelers' social and economic attributes influenced it include the gender,education attainment,age,driving experience and per capita monthly household income.The urban characteristics influenced it mainly cover the land use pattern,public traffic facilities and spatial attributes of residential environment.展开更多
A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways,...A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.展开更多
基金Under the auspices of National Natural Science Foundation of China (No 40571052,40301014)
文摘Taking Guangzhou as a case,this paper adopted a questionnaire survey to gather first-hand data and analyzed the characteristics and influencing factors of private car travel in Chinese cities.As the research indicated,trip purposes of private car travel are mainly commute and business affairs with a more flexible trip in the urban core area.And trip intensities are concentrated in a certain extent,with trip frequency being lower in the urban core area than the peripheral area.In addition,the trip time has two significant peaks occurring in the morning and afternoon,and one trough in the midday.And trip spatial distribution is mainly within commute with both residence and employment in urban area and inward commute with residence in suburban area while employment in urban area.Both kinds of commutes direct to the urban area.The study also shows that the characteristics of private car travel are principally influenced by two aspects:travelers' attributes and urban characteristics.The main travelers' social and economic attributes influenced it include the gender,education attainment,age,driving experience and per capita monthly household income.The urban characteristics influenced it mainly cover the land use pattern,public traffic facilities and spatial attributes of residential environment.
基金The Project of Research on Technologyand Devices for Traffic Guidance (Vehicle Navigation)System of Beijing Municipal Commission of Science and Technology(No H030630340320)the Project of Research on theIntelligence Traffic Information Platform of Beijing Education Committee
文摘A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
