In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-d...In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-dimensional data.We extended and modified the traditionally threedimensional data cube into four dimensions,which are space,date,time,and user,each with a user-specified hierarchy,and took transaction numbers and travel time as two quantitative measures.The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays,while the volume at weekends has an approximate even distribution.Bad weather condition significantly restricts the bikeshare usage.Besides,seamless smartcard users generally take a longer trip than exclusive smartcard users;and non-native users ride faster than native users.These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data,but also raise equity concerns among bikeshare users with different demographic backgrounds.展开更多
A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution through...A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.展开更多
The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phon...The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.展开更多
In order to understand the travel characteristics and behavior patterns of women in Wangjing area and explore whether the existing situation can meet women's needs for the use of street space,the area around Wangj...In order to understand the travel characteristics and behavior patterns of women in Wangjing area and explore whether the existing situation can meet women's needs for the use of street space,the area around Wangjing South Station of Metro Line 14 was taken as an example for analysis and research.Wangjing area was classified to the following six use attributes:company enterprise,transportation hub,education and culture,residential area,municipal facilities,leisure and entertainment.The proportion of each use attribute was evaluated according to four levels:A 25%and above(including 25%),B 15%-25%,C 15%-5%,D 5%and below(including 5%).Finally,whether the plot had composite functions was judged,and the spatio-temporal laws and behavior patterns of surrounding women were analyzed from the perspectives of time and space.展开更多
Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have...Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.展开更多
Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain fac...Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.展开更多
Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressu...Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.展开更多
基金Supported by Projects of International Cooperation and Exchange of the National Natural Science Foundation of China(51561135003)Key Project of National Natural Science Foundation of China(51338003)Scientific Research Foundation of Graduated School of Southeast University(YBJJ1842)
文摘In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-dimensional data.We extended and modified the traditionally threedimensional data cube into four dimensions,which are space,date,time,and user,each with a user-specified hierarchy,and took transaction numbers and travel time as two quantitative measures.The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays,while the volume at weekends has an approximate even distribution.Bad weather condition significantly restricts the bikeshare usage.Besides,seamless smartcard users generally take a longer trip than exclusive smartcard users;and non-native users ride faster than native users.These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data,but also raise equity concerns among bikeshare users with different demographic backgrounds.
文摘A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.
基金Under the auspices of the National Natural Science Foundation of China(No.41571146)China Postdoctoral Science Foundation(No.2019M651784)。
文摘The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.
基金Sponsored by 2022 Beijing Undergraduate Innovation and Entrepreneurship Training PlanConstruction of Demonstration Off-campus Practice Base for Integration of Industry and Education+1 种基金Beijing Municipal Education Commission Social Science Project(KM202010009002)“Young Yu You Talents Training Plan”of North China University of Technology。
文摘In order to understand the travel characteristics and behavior patterns of women in Wangjing area and explore whether the existing situation can meet women's needs for the use of street space,the area around Wangjing South Station of Metro Line 14 was taken as an example for analysis and research.Wangjing area was classified to the following six use attributes:company enterprise,transportation hub,education and culture,residential area,municipal facilities,leisure and entertainment.The proportion of each use attribute was evaluated according to four levels:A 25%and above(including 25%),B 15%-25%,C 15%-5%,D 5%and below(including 5%).Finally,whether the plot had composite functions was judged,and the spatio-temporal laws and behavior patterns of surrounding women were analyzed from the perspectives of time and space.
基金supported by the National Natural Science Foundation of China(Grant No.52102382)the Shanghai Science and Technology Committee(Grant No.20DZ1203201)+1 种基金the Fundamental Research Funds for the Central Universities(2022-5-YB-04)the Shanghai Shentong Metro Group Co.,Ltd.(Grant Nos.JSKY21R005-1-WT-21064 and JS-KY21R005-2).
文摘Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.
基金supported by the National Natural Science Foundation of China(No.52372332)the Fundamental Research Funds for the Central Universities of China(No.2022-5-YB-04)the Shanghai Shentong Metro Group Co.,Ltd.(Nos.JSKY21R005-1-WT-21064,and JS-KY22R033-2).
文摘Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.
基金Project supported by the Russian Foundation for Basic Research(No.18-07-00518)the National Natural Science Foundation of China(No.10972212)
文摘Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.
