This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Land-Use Classification Using Taxi GPS Traces

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

[1]  Lin Sun,et al.  Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[2]  K. Seto,et al.  Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics , 2005, Landscape Ecology.

[3]  Qingquan Li,et al.  Visualizing hot spot analysis result based on mashup , 2009, LBSN '09.

[4]  Guangjin Tian,et al.  Socio-economic driving forces of arable land conversion: A case study of Wuxian City, China , 2005 .

[5]  Zhi-Hua Zhou,et al.  iBAT: detecting anomalous taxi trajectories from GPS traces , 2011, UbiComp '11.

[6]  D. Lu,et al.  Urban classification using full spectral information of landsat ETM+ imagery in Marion County, Indiana , 2005 .

[7]  Shupeng Chen,et al.  Remote sensing and GIS for urban growth analysis in China , 2000 .

[8]  Victor Soto,et al.  Robust Land Use Characterization of Urban Landscapes using Cell Phone Data , 2011 .

[9]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[10]  Xiaojun Yang,et al.  Drivers of Land-Use/Land-Cover Changes and Dynamic Modeling for the Atlanta, Georgia Metropolitan Area , 2002 .

[11]  Marvin E. Bauer,et al.  Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification , 2000 .

[12]  Tong Zhang,et al.  Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations , 2011, IEEE Transactions on Information Theory.

[13]  Alberta Bianchin,et al.  Remote Sensing and Urban Analysis , 2008, Communication Systems and Applications.

[14]  Daqing Zhang,et al.  Measuring social functions of city regions from large-scale taxi behaviors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[15]  Javier A. Barria,et al.  Detection and Classification of Traffic Anomalies Using Microscopic Traffic Variables , 2011, IEEE Transactions on Intelligent Transportation Systems.

[16]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[17]  R. F. Nalepka,et al.  Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas , 1976 .

[18]  Graeme G. Wilkinson,et al.  Results and implications of a study of fifteen years of satellite image classification experiments , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Liang Liu,et al.  Uncovering cabdrivers' behavior patterns from their digital traces , 2010, Comput. Environ. Urban Syst..

[20]  Zhaohui Wu,et al.  ScudWare: A Semantic and Adaptive Middleware Platform for Smart Vehicle Space , 2007, IEEE Transactions on Intelligent Transportation Systems.

[21]  C. Aubrecht,et al.  Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use , 2009, Comput. Environ. Urban Syst..

[22]  M. Herold,et al.  Spatial Metrics and Image Texture for Mapping Urban Land Use , 2003 .

[23]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[24]  Zhaohui Wu,et al.  TaskShadow: Toward Seamless Task Migration across Smart Environments , 2011, IEEE Intelligent Systems.

[25]  F. Canters,et al.  Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data , 2011 .

[26]  S. Barr,et al.  INFERRING URBAN LAND USE FROM SATELLITE SENSOR IMAGES USING KERNEL-BASED SPATIAL RECLASSIFICATION , 1996 .

[27]  Zhaohui Wu,et al.  Context-aware smart car: from model to prototype , 2009 .

[28]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[29]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[30]  Jianguo Wu,et al.  A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA , 2004, Landscape Ecology.

[31]  P. Gong,et al.  Frequency-based contextual classification and gray-level vector reduction for land-use identification , 1992 .

[32]  Eric F. Lambin,et al.  What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence , 2001 .

[33]  P. Gong,et al.  The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. , 1990 .

[34]  J. R. Jensen,et al.  Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes , 2011 .

[35]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[36]  Fei-Yue Wang,et al.  Agent-Based Control for Networked Traffic Management Systems , 2005, IEEE Intell. Syst..

[37]  Siyuan Liu,et al.  Towards mobility-based clustering , 2010, KDD.

[38]  Yen-Chu Weng Spatiotemporal changes of landscape pattern in response to urbanization , 2007 .

[39]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[40]  Wei Ji,et al.  Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics , 2006, Comput. Environ. Urban Syst..

[41]  Eléonore Wolff,et al.  Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features , 2006 .

[42]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[43]  Gang Pan,et al.  Mining the semantics of origin-destination flows using taxi traces , 2012, UbiComp '12.

[44]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[45]  Alan T. Murray,et al.  Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data , 2000 .

[46]  Zhaohui Wu,et al.  Prediction of urban human mobility using large-scale taxi traces and its applications , 2012, Frontiers of Computer Science.

[47]  Dongmei Chen,et al.  Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case , 2004 .

[48]  John Krumm,et al.  From GPS traces to a routable road map , 2009, GIS.

[49]  J. Qi,et al.  Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization , 2009 .

[50]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[51]  Renee Gluch,et al.  URBAN GROWTH DETECTION USING TEXTURE ANALYSIS ON MERGED LANDSAT TM AND SPOT-P DATA , 2002 .

[52]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[53]  M. Ramsey,et al.  Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers , 2001 .