Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional prom...Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.展开更多
Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused...Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused on refining urban land use classification methods and generating data prod-ucts at various scales.However,detailed parcel-level urban land use mapping across China remains insuf-ficient with low accuracy.To address this issue,we propose an enhanced mapping framework of essential urban land use categories by integrating multi-modal deep learning models and multi-source geospatial data.Utilizing complete,accurate land parcels derived from the combined OpenStreetMap and Tianditu road networks as the smallest classification units,we have developed an enhanced Essential Urban Land Use Categories(EULUC)map covering all cities in China for 2022,termed EULUC-China 2.0.The mapping results show that residential,industrial,and park and greenspace are the dominant land use categories,collectively accounting for nearly 78%of the urban area.Compared to its predecessor,EULUC-China 1.0,the updated 2.0 version offers more detailed,spatially explicit information that reveals distinct spatial patterns within diverse land use compositions of each city.Our evaluation demonstrates that the overall accuracies of Level-I and Level-II classification reach up to 79%and 72%,respectively,representing sub-stantial enhancements across all categories over the previous product.These improvements are primarily attributed to the effectiveness of deep learning in processing multi-modal inputs,particularly through the graph modeling of Point-of-interest(POI)data.The publicly accessible product(https://zenodo.org/records/15180905)and the insights derived from this study offer a valuable dataset and references for researchers and practitioners addressing critical challenges in urbanization.展开更多
基金supported by the Key Research Base of Philosophy and Social Sciences in Sichuan Province—Modern Design and Culture Research Center(Grant No.MD24C003)the National Natural Science Foundation of China(Grant No.52308084)+1 种基金China Postdoctoral Science Foundation(Grant No.2022M712877)Key R&D and Promotion Projects of Henan Province(Grant No.222102110125)。
文摘Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.
基金supported by the National Natural Science Foun-dation of China Young Scientists Fund(42201373)Major Pro-gram of the National Natural Science Foundation of China(42090015)+4 种基金the National Key Research and Development Program of China(2022YFB3903703)the Guangdong Natural Science Foun-dation of General Program(260842044)the National Natural Science Foundation of China(NSFC)/Research Grants Council(RGC)Joint Research Scheme(N_HKU722/23)The University of Hong Kong HKU-100 Scholars Fundthe Seed Fund for Basic Research,Strategic Interdisciplinary Research Scheme Fund,and the Croucher Foundation(CAS22902/CAS22HU01).
文摘Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused on refining urban land use classification methods and generating data prod-ucts at various scales.However,detailed parcel-level urban land use mapping across China remains insuf-ficient with low accuracy.To address this issue,we propose an enhanced mapping framework of essential urban land use categories by integrating multi-modal deep learning models and multi-source geospatial data.Utilizing complete,accurate land parcels derived from the combined OpenStreetMap and Tianditu road networks as the smallest classification units,we have developed an enhanced Essential Urban Land Use Categories(EULUC)map covering all cities in China for 2022,termed EULUC-China 2.0.The mapping results show that residential,industrial,and park and greenspace are the dominant land use categories,collectively accounting for nearly 78%of the urban area.Compared to its predecessor,EULUC-China 1.0,the updated 2.0 version offers more detailed,spatially explicit information that reveals distinct spatial patterns within diverse land use compositions of each city.Our evaluation demonstrates that the overall accuracies of Level-I and Level-II classification reach up to 79%and 72%,respectively,representing sub-stantial enhancements across all categories over the previous product.These improvements are primarily attributed to the effectiveness of deep learning in processing multi-modal inputs,particularly through the graph modeling of Point-of-interest(POI)data.The publicly accessible product(https://zenodo.org/records/15180905)and the insights derived from this study offer a valuable dataset and references for researchers and practitioners addressing critical challenges in urbanization.
