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
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
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
城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车...城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。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
为分析建成环境对公共自行车出行模式的影响,文章结合公共自行车运营数据和建成环境数据,以公共自行车站点为中心建立缓冲区并提取缓冲区内兴趣点(point of interest,POI),在考虑POI规模的基础上划分站点类型;根据站点类型对出行起讫点(...为分析建成环境对公共自行车出行模式的影响,文章结合公共自行车运营数据和建成环境数据,以公共自行车站点为中心建立缓冲区并提取缓冲区内兴趣点(point of interest,POI),在考虑POI规模的基础上划分站点类型;根据站点类型对出行起讫点(origin-destination,OD)分类,以OD类型确定公共自行车出行模式,使用地理加权回归(geographically weighted regression,GWR)模型,分析建成环境对公共自行车出行模式的影响;以昆明市为例进行实证分析。结果表明:昆明市公共自行车出行模式可划分为16种,OD皆为住宅主导型和公司(企业)主导型站点的出行模式约占69.26%;建成环境对不同出行模式的影响效应存在差异;土地利用混合度是公共自行车出行模式的主要影响因素。研究结果可为公共自行车布局优化及运营管理提供参考。展开更多
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.
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.
文摘城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。
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.
文摘为分析建成环境对公共自行车出行模式的影响,文章结合公共自行车运营数据和建成环境数据,以公共自行车站点为中心建立缓冲区并提取缓冲区内兴趣点(point of interest,POI),在考虑POI规模的基础上划分站点类型;根据站点类型对出行起讫点(origin-destination,OD)分类,以OD类型确定公共自行车出行模式,使用地理加权回归(geographically weighted regression,GWR)模型,分析建成环境对公共自行车出行模式的影响;以昆明市为例进行实证分析。结果表明:昆明市公共自行车出行模式可划分为16种,OD皆为住宅主导型和公司(企业)主导型站点的出行模式约占69.26%;建成环境对不同出行模式的影响效应存在差异;土地利用混合度是公共自行车出行模式的主要影响因素。研究结果可为公共自行车布局优化及运营管理提供参考。
