大数据时代的交通模型
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摘要
通过新的大数据来源诸如手机通信记录、智能卡数据以及社交媒体地理编码记录,可以前所未有地观察和了解出行行为的细节。尽管有如此庞大的大数据来源,但在规划实践中使用的交通需求模型,其数据源仍大多来自交通调查和人口普查等传统方法。对近期利用大数据研究交通行为,以及使交通规划师可以进行假设情景分析的交通需求模型的最新进展进行梳理。从出行识别到出行活动推理,回顾和分析现有数据分析方法,这些传统方法使收集到的出行轨迹信息能响应交通需求模型。未来的研究应该侧重将概率模型和机器学习技术应用于数据科学。设计这些数据挖掘方法是为了处理由手机移动追踪数据衍生的零散和掺杂偏差的数据的不确定性。此外,这些方法还适用于将不同的相关数据组整合到一个数据融合方案中,以便用出行日志信息丰富大数据。总之,建模知识已经在交通运输领域发展成熟,因此强烈建议在交通规划方面应用数据驱动方法时应建立相应领域专业知识的基础。这些新的挑战呼吁交通模型师和数据科学家之间的多学科协作。
New Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail.Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census.This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what-if scenarios.From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.
New Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail.Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census.This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what-if scenarios.From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.
引文
[1]Castiglione J,Bradley M,Gliebe J.ActivityBased Travel Demand Models:Aprimer[R].Washington DC:Transportation Research Board2015.
[2]de Dios Ortuzar J,Willumsen L G.Modelling Transport[M].New Jersey:John Wiley&Sons,2011.
[3]Rasouli S,Timmermans H.Activity-Based Models of Travel Demand:Promises,Progress and Prospects[J].International Journal of Urban Sciences,2014,18(1):31-60.
[4]Balmer M,Axhausen K,Nagel K.Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations[J].Transportation Research Record:Journal of the Transportation Research Board,2006(1985):125-134.
[5]Smith L,Beckman R J,Anson D,et al.TRAN-SIMS:Transportation Analysis and Simulation System[C]//The 5th TRB National Transportation Planning Methods Applications Conference,Seattle,April 17-21,1995.
[6]Horni A,Nagel K,Axhausen K W.The MultiAgent Transport Simulation MATSim[M].London:Ubiquity Press,2016.
[7]Adnan M,Pereira F C,Lima Azevedo C M,et al.SimMobility:A Multi-Scale Integrated Agent-Based Simulation Platform[C]//The 95th Annual Meeting of the Transportation Research Board,Walter E Washington Convention Center,Washington DC,January 10-14,2016.
[8]Goulias K G,Bhat C R,Pendyala R M,et al.Simulator of Activities,Greenhouse Emissions,Networks,and Travel(SimAGENT)in Southern California[C]//The 91st Annual Meeting of the Transportation Research Board,Washington Marriott Wardman Park,Omni Shoreham,and Washington Hilton hotels,Washington DC,January 22-26,2012.
[9]Calabrese F,Ferrari L,Blondel V D.Urban Sensing Using Mobile Phone Network Data:A Survey of Research[J].ACM Computing Surveys,2014,47(2):1-20.
[10]Barry J,Newhouser R,Rahbee A,et al.Origin and Destination Estimation in New York City with Automated Fare System Data[J].Transportation Research Record:Journal of the Transportation Research Board,2002(1817):183-187.
[11]Zhao J,Rahbee A,Wilson N H M.Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems[J].Computer-Aided Civil and Infrastructure Engineering,2010,22(5):376-387.
[12]Trépanier M,Tranchant N,Chapleau R.Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System[J].Journal of Intelligent Transportation Systems,2007,11(1):1-14.
[13]Munizaga M A,Palma C.Estimation of a Disaggregate Multimodal Public Transport Origin-Destination Matrix from Passive Smartcard Data from Santiago,Chile[J].Transportation Research Part C:Emerging Technologies,2012,24(9):9-18.
[14]Yuan N J,Wang Y,Zhang F,et al.Reconstructing Individual Mobility from Smart Card Transactions:A Space Alignment Approach[C]//The 13th IEEE International Conference on Data Mining,Dallas,December7-10,2013.
[15]Seaborn C,Attanucci J,Wilson N.Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data[J].Transportation Research Record:Journal of the Transportation Research Board,2009(2121):55-62.
[16]Chakirov A,Erath A.Activity Identification and Primary Location Modelling Based on Smart Card Payment Data for Public Transport[C]//The 13th International Conference on Travel Behaviour Research,Toronto,July15-20,2012.
[17]Devillaine F,Munizaga M,Trépanier M.Activities of Public Transport Users by Analyzing Smart Card Data[J].Transportation Research Record:Journal of the Transportation Research Board,2012(2761):48-55.
[18]Zou Qingru,Yao Xiangming,Zhao Peng,et al.Detecting Home Location and Trip Purposes for Cardholders by Mining Smart Card Transaction Data in Beijing Subway[J]Transportation,2018,45(3):919-944.
[19]Isaacman S,Becker R,Cáceres R,et al.Identifying Important Places in People's Lives from Cellular Network Data[C]//Lyons KHightower J,Huang E M.Pervasive Computing,No.6696 in Lecture Notes in Computer Science.Berlin:Springer,2011:133-151.
[20]Han G,Sohn K.Activity Imputation for Tripchains Elicited from Smart-Card Data Using a Continuous Hidden Markov Model[J]Transportation Research Part B:Methodological,2016,83:121-135.
[21]Bouman P.Recognizing Demand Patterns from Smart Card Data for Agent-Based Microsimulation of Public Transport[D].Rotterdam:Erasmus University Rotterdam,2012.
[22]Bouman P,Van der Hurk E,Kroon L,et al Detecting Activity Patterns from Smart Card Data[C]//Delft University of Technology BNAIC 2013:Proceedings of the 25th Benelux Conference on Artificial Intelligence Delft:Delft University of Technology,2013.
[23]Eagle N,Pentland A S.Eigenbehaviors:Identifying Structure in Routine[J].Behavioral Ecology and Sociobiology,2009,63(7):1057-1066.
[24]Fourie P J,Erath A L,Ordó?ez Medina S Aet al.Using Smartcard Data for Agent-Based Transport Simulation[C]//Schm?cker JDKurauchi F.Public Transport Planning with Smart Card Data.Boca Raton:CRC Press2016:133-160.
[25]Fourie P J.Reconstructing Bus Vehicle Trajectories from Transit Smart-Card Data[R/OL].2014[2016-06-01].http://transp-or.epfl.ch/heart/2014/abstracts/257.pdf.
[26]Sarlas G,Axhausen K W.Localized Speed Prediction with the Use of Spatial Simultaneous Autoregressive Models[C]//The 94th Annual Meeting of the Transportation Research Board,Walter E Washington Convention Center,Washington DC,January 11-15,2015.
[27]Sun Lijun,Tirachini A,Axhausen K W,et al Models of Bus Boarding and Alighting Dynamics[J].Transportation Research Part A:Policy and Practice,2014,69:447-460.
[28]Widhalm P,Yang Y,Ulm M,et al.Discovering Urban Activity Patterns in Cell Phone Data[J].Transportation,2015,42(4):597-623.
[29]Caceres N,Wideberg J P,Benitez F G.Deriving Origin-Destination Data from a Mobile Phone Network[J].Intelligent Transport Systems Iet,2007,1(1):15-26.
[30]Iqbal M S,Choudhury C F,Wang P,et al.Development of Origin-Destination Matrices Using Mobile Phone Call Data[J].Transportation Research Part C:Emerging Technologies,2014,40(1):63-74.
[31]Wang P,Hunter T,Bayen A M,et al.Understanding Road Usage Patterns in Urban Areas[J].Scientific Reports,2012(2):1-6.
[32]Jiang S,Ferreira Jr J,González M C.Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data:A Case Study of Singapore[C]//ACM SIGKDD 4th International Workshop on Urban Computing,Sydney,May 20,2015.
[33]Horn C,Klampfl S,Cik M,et al.Detecting Outliers in Cell Phone Data[J].Transportation Research Record:Journal of the Transportation Research Board,2014,2405:49-56.
[34]Calabrese F,Lorenzo G D,Liu L,et al.Estimating Origin-Destination Flows Using Mobile Phone Location Data[J].IEEE Pervasive Computing,2011,10(4):36-44.
[35]Ficek M,Kencl L.Inter-Call Mobility Model:A Spatio-Temporal Refinement of Call Data Records Using a Gaussian Mixture Model[J].INFOCOM,2012,131(5):469-477.
[36]Eagle N,Pentland A.Reality Mining:Sensing Complex Social Systems[J].Personal and Ubiquitous Computing,2006,10(4):255-268.
[37]Schlaich J,Otterst?tter T,Friedrich M.Generating Trajectories from Mobile Phone Data[C]//The 89th Annual Meeting Transportation Research Board,Washington DC,January 10-14,2010.
[38]Jiang Shan,Fiore G A,Yang Y,et al.A Review of Urban Computing for Mobile Phone Traces:Current Methods,Challenges and Opportunities[C]//The 2nd ACM SIGKDDInternational Workshop on Urban Computing,Chicago,2013.
[39]Toole J L,Colak S,Sturt B,et al.The Path Most Traveled:Travel Demand Estimation Using Big Data Resources[J].Transportation Research Part C:Emerging Technologies2015,58:162-177.
[40]Alexander L,Jiang S,Murga M,et al.Origin-Destination Trips by Purpose and Time of Day Inferred from Mobile Phone Data[J]Transportation Research Part C:Emerging Technologies,2015,58:240-250.
[41]Yin M,Sheehan M,Feygin S,et al.A Generative Model of Urban Activities from Cellular Data[J].IEEE Transactions on Intelligent Transportation Systems,2018,19(6):1682-1696.
[42]Yang Y,Widhalm P,Athavale S,et al.Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data[C]//The 30th AAAIConference on Artificial Intelligence,Phoenix,February 12-17,2016.
[43]Wang Huayong,Calabrese F,Lorenzo G Det al.Transportation Mode Inference from Anonymized and Aggregated Mobile Phone Call Detail Records[C]//The 13th International IEEE Conference on Intelligent Transportation Systems,2010:318-323.
[44]Zilske M,Nagel K.Studying the Accuracy of Demand Generation from Mobile Phone Trajectories with Synthetic Data[J].Procedia Computer Science,2014,32:802-807.
[45]Zilske M,Nagel K.A Simulation-Based Approach for Constructing All-Day Travel Chains from Mobile Phone Data[J].Procedia Computer Science,2015,52:468-475.
[46]Pozdnoukhov A,Campbell A,Feygin S,et al.San Francisco Bay Area:The Smartbay Project-Connected Mobility[C]//Horni A,Nagel K,Axhausen K W.The Multi-Agent Transport Simulation MATSim,London:Ubiquity Press,2016:485-490.
[47]Kaiser C,Pozdnoukhov A.Enabling RealTime City Sensing with Kernel Stream Oracles and MapReduce[J].Pervasive and Mobile Computing,2013,9(5):708-721.
[48]Liao Lin,Fox D,Kautz H.Location-Based Activity Recognition Using Relational Markov Networks[J].International Joint Conference on Artificial Intelligence,2005,26(2):773-778.
[49]Liao Lin,Fox D,Kautz H.Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields[J].International Journal of Robotics Research,2007,26(1):119-134.
[50]Duong T V,Bui H H,Phung D Q,et al.Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,1(4):838-845.
[51]Baratchi M,Meratnia N,Havinga P J M,et al.A Hierarchical Hidden Semi-Markov Model for Modeling Mobility Data[C]//2014ACM International Joint Conference on Pervasive and Ubiquitous Computing,Motif Seattle Hotel,Seattle,September 13-17,2014.
[52]Liao Lin,Patterson D J,Fox D,et al.Learning and Inferring Transportation Routines[J].National Conference of Artificial Intelligence,2007,171(5/6):348-353.
[53]Yan Z,Chakraborty D,Parent C,et al.SeMiTri:A Framework for Semantic Annotation of Heterogeneous Trajectories[C]//The 14th International Conference on Extending Database Technology,Uppsala,Sweden,March21-24,2011.
[54]Bishop C M.Model-Based Machine Learning[J].Philosophical Transactions A:Mathematical,Physicial and Engineering Sciences2013,371(1984):1-17.
[55]Zheng Yu.Methodologies for Cross-Domain Data Fusion:An Overview[J].IEEE Transactions on Big Data,2015,1(1):16-34.
[56]de Montjoye Y A,Hidalgo C A,Verleysen M,et al.Unique in the Crowd:The Privacy Bounds of Human Mobility[J].Scientific Reports,2013(3):1-5.
[57]Mir D J,Isaacman S,Caceres R,et al.DP-WHERE:Differentially Private Modeling of Human Mobility[C]//2013 IEEE International Conference on Big Data,Santa Clara,October 6-9,2013.
[58]Andrés M E,Bordenabe N E,Chatzikokolakis K,et al.Geo-Indistinguish Ability:Differential Privacy for Location-Based Systems[C]//the 2013 ACM SIGSAC Conference on Computer&Communications Security,Berlin,November 4-8,2013.
[59]Hu X,Yuan M,Yao J,et al.Differential Privacy in Telco Big Data Platform[J].Biopolymers,2015,8(12):1692-1703.
[60]Primault V,Mokhtar S B,Lauradoux C,et al.Differentially Private Location Privacy in Practice[C]//The Third Workshop on Mobile Security Technologies,San Jose,May 172014.
(1)主要工作从2010年至2016年第二季度。
(2)一些权威机构已经开始使用智能手机进行连续调查,以降低相应负担并提高数据质量,特别是在捕捉短时间活动方面。
[2]de Dios Ortuzar J,Willumsen L G.Modelling Transport[M].New Jersey:John Wiley&Sons,2011.
[3]Rasouli S,Timmermans H.Activity-Based Models of Travel Demand:Promises,Progress and Prospects[J].International Journal of Urban Sciences,2014,18(1):31-60.
[4]Balmer M,Axhausen K,Nagel K.Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations[J].Transportation Research Record:Journal of the Transportation Research Board,2006(1985):125-134.
[5]Smith L,Beckman R J,Anson D,et al.TRAN-SIMS:Transportation Analysis and Simulation System[C]//The 5th TRB National Transportation Planning Methods Applications Conference,Seattle,April 17-21,1995.
[6]Horni A,Nagel K,Axhausen K W.The MultiAgent Transport Simulation MATSim[M].London:Ubiquity Press,2016.
[7]Adnan M,Pereira F C,Lima Azevedo C M,et al.SimMobility:A Multi-Scale Integrated Agent-Based Simulation Platform[C]//The 95th Annual Meeting of the Transportation Research Board,Walter E Washington Convention Center,Washington DC,January 10-14,2016.
[8]Goulias K G,Bhat C R,Pendyala R M,et al.Simulator of Activities,Greenhouse Emissions,Networks,and Travel(SimAGENT)in Southern California[C]//The 91st Annual Meeting of the Transportation Research Board,Washington Marriott Wardman Park,Omni Shoreham,and Washington Hilton hotels,Washington DC,January 22-26,2012.
[9]Calabrese F,Ferrari L,Blondel V D.Urban Sensing Using Mobile Phone Network Data:A Survey of Research[J].ACM Computing Surveys,2014,47(2):1-20.
[10]Barry J,Newhouser R,Rahbee A,et al.Origin and Destination Estimation in New York City with Automated Fare System Data[J].Transportation Research Record:Journal of the Transportation Research Board,2002(1817):183-187.
[11]Zhao J,Rahbee A,Wilson N H M.Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems[J].Computer-Aided Civil and Infrastructure Engineering,2010,22(5):376-387.
[12]Trépanier M,Tranchant N,Chapleau R.Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System[J].Journal of Intelligent Transportation Systems,2007,11(1):1-14.
[13]Munizaga M A,Palma C.Estimation of a Disaggregate Multimodal Public Transport Origin-Destination Matrix from Passive Smartcard Data from Santiago,Chile[J].Transportation Research Part C:Emerging Technologies,2012,24(9):9-18.
[14]Yuan N J,Wang Y,Zhang F,et al.Reconstructing Individual Mobility from Smart Card Transactions:A Space Alignment Approach[C]//The 13th IEEE International Conference on Data Mining,Dallas,December7-10,2013.
[15]Seaborn C,Attanucci J,Wilson N.Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data[J].Transportation Research Record:Journal of the Transportation Research Board,2009(2121):55-62.
[16]Chakirov A,Erath A.Activity Identification and Primary Location Modelling Based on Smart Card Payment Data for Public Transport[C]//The 13th International Conference on Travel Behaviour Research,Toronto,July15-20,2012.
[17]Devillaine F,Munizaga M,Trépanier M.Activities of Public Transport Users by Analyzing Smart Card Data[J].Transportation Research Record:Journal of the Transportation Research Board,2012(2761):48-55.
[18]Zou Qingru,Yao Xiangming,Zhao Peng,et al.Detecting Home Location and Trip Purposes for Cardholders by Mining Smart Card Transaction Data in Beijing Subway[J]Transportation,2018,45(3):919-944.
[19]Isaacman S,Becker R,Cáceres R,et al.Identifying Important Places in People's Lives from Cellular Network Data[C]//Lyons KHightower J,Huang E M.Pervasive Computing,No.6696 in Lecture Notes in Computer Science.Berlin:Springer,2011:133-151.
[20]Han G,Sohn K.Activity Imputation for Tripchains Elicited from Smart-Card Data Using a Continuous Hidden Markov Model[J]Transportation Research Part B:Methodological,2016,83:121-135.
[21]Bouman P.Recognizing Demand Patterns from Smart Card Data for Agent-Based Microsimulation of Public Transport[D].Rotterdam:Erasmus University Rotterdam,2012.
[22]Bouman P,Van der Hurk E,Kroon L,et al Detecting Activity Patterns from Smart Card Data[C]//Delft University of Technology BNAIC 2013:Proceedings of the 25th Benelux Conference on Artificial Intelligence Delft:Delft University of Technology,2013.
[23]Eagle N,Pentland A S.Eigenbehaviors:Identifying Structure in Routine[J].Behavioral Ecology and Sociobiology,2009,63(7):1057-1066.
[24]Fourie P J,Erath A L,Ordó?ez Medina S Aet al.Using Smartcard Data for Agent-Based Transport Simulation[C]//Schm?cker JDKurauchi F.Public Transport Planning with Smart Card Data.Boca Raton:CRC Press2016:133-160.
[25]Fourie P J.Reconstructing Bus Vehicle Trajectories from Transit Smart-Card Data[R/OL].2014[2016-06-01].http://transp-or.epfl.ch/heart/2014/abstracts/257.pdf.
[26]Sarlas G,Axhausen K W.Localized Speed Prediction with the Use of Spatial Simultaneous Autoregressive Models[C]//The 94th Annual Meeting of the Transportation Research Board,Walter E Washington Convention Center,Washington DC,January 11-15,2015.
[27]Sun Lijun,Tirachini A,Axhausen K W,et al Models of Bus Boarding and Alighting Dynamics[J].Transportation Research Part A:Policy and Practice,2014,69:447-460.
[28]Widhalm P,Yang Y,Ulm M,et al.Discovering Urban Activity Patterns in Cell Phone Data[J].Transportation,2015,42(4):597-623.
[29]Caceres N,Wideberg J P,Benitez F G.Deriving Origin-Destination Data from a Mobile Phone Network[J].Intelligent Transport Systems Iet,2007,1(1):15-26.
[30]Iqbal M S,Choudhury C F,Wang P,et al.Development of Origin-Destination Matrices Using Mobile Phone Call Data[J].Transportation Research Part C:Emerging Technologies,2014,40(1):63-74.
[31]Wang P,Hunter T,Bayen A M,et al.Understanding Road Usage Patterns in Urban Areas[J].Scientific Reports,2012(2):1-6.
[32]Jiang S,Ferreira Jr J,González M C.Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data:A Case Study of Singapore[C]//ACM SIGKDD 4th International Workshop on Urban Computing,Sydney,May 20,2015.
[33]Horn C,Klampfl S,Cik M,et al.Detecting Outliers in Cell Phone Data[J].Transportation Research Record:Journal of the Transportation Research Board,2014,2405:49-56.
[34]Calabrese F,Lorenzo G D,Liu L,et al.Estimating Origin-Destination Flows Using Mobile Phone Location Data[J].IEEE Pervasive Computing,2011,10(4):36-44.
[35]Ficek M,Kencl L.Inter-Call Mobility Model:A Spatio-Temporal Refinement of Call Data Records Using a Gaussian Mixture Model[J].INFOCOM,2012,131(5):469-477.
[36]Eagle N,Pentland A.Reality Mining:Sensing Complex Social Systems[J].Personal and Ubiquitous Computing,2006,10(4):255-268.
[37]Schlaich J,Otterst?tter T,Friedrich M.Generating Trajectories from Mobile Phone Data[C]//The 89th Annual Meeting Transportation Research Board,Washington DC,January 10-14,2010.
[38]Jiang Shan,Fiore G A,Yang Y,et al.A Review of Urban Computing for Mobile Phone Traces:Current Methods,Challenges and Opportunities[C]//The 2nd ACM SIGKDDInternational Workshop on Urban Computing,Chicago,2013.
[39]Toole J L,Colak S,Sturt B,et al.The Path Most Traveled:Travel Demand Estimation Using Big Data Resources[J].Transportation Research Part C:Emerging Technologies2015,58:162-177.
[40]Alexander L,Jiang S,Murga M,et al.Origin-Destination Trips by Purpose and Time of Day Inferred from Mobile Phone Data[J]Transportation Research Part C:Emerging Technologies,2015,58:240-250.
[41]Yin M,Sheehan M,Feygin S,et al.A Generative Model of Urban Activities from Cellular Data[J].IEEE Transactions on Intelligent Transportation Systems,2018,19(6):1682-1696.
[42]Yang Y,Widhalm P,Athavale S,et al.Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data[C]//The 30th AAAIConference on Artificial Intelligence,Phoenix,February 12-17,2016.
[43]Wang Huayong,Calabrese F,Lorenzo G Det al.Transportation Mode Inference from Anonymized and Aggregated Mobile Phone Call Detail Records[C]//The 13th International IEEE Conference on Intelligent Transportation Systems,2010:318-323.
[44]Zilske M,Nagel K.Studying the Accuracy of Demand Generation from Mobile Phone Trajectories with Synthetic Data[J].Procedia Computer Science,2014,32:802-807.
[45]Zilske M,Nagel K.A Simulation-Based Approach for Constructing All-Day Travel Chains from Mobile Phone Data[J].Procedia Computer Science,2015,52:468-475.
[46]Pozdnoukhov A,Campbell A,Feygin S,et al.San Francisco Bay Area:The Smartbay Project-Connected Mobility[C]//Horni A,Nagel K,Axhausen K W.The Multi-Agent Transport Simulation MATSim,London:Ubiquity Press,2016:485-490.
[47]Kaiser C,Pozdnoukhov A.Enabling RealTime City Sensing with Kernel Stream Oracles and MapReduce[J].Pervasive and Mobile Computing,2013,9(5):708-721.
[48]Liao Lin,Fox D,Kautz H.Location-Based Activity Recognition Using Relational Markov Networks[J].International Joint Conference on Artificial Intelligence,2005,26(2):773-778.
[49]Liao Lin,Fox D,Kautz H.Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields[J].International Journal of Robotics Research,2007,26(1):119-134.
[50]Duong T V,Bui H H,Phung D Q,et al.Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,1(4):838-845.
[51]Baratchi M,Meratnia N,Havinga P J M,et al.A Hierarchical Hidden Semi-Markov Model for Modeling Mobility Data[C]//2014ACM International Joint Conference on Pervasive and Ubiquitous Computing,Motif Seattle Hotel,Seattle,September 13-17,2014.
[52]Liao Lin,Patterson D J,Fox D,et al.Learning and Inferring Transportation Routines[J].National Conference of Artificial Intelligence,2007,171(5/6):348-353.
[53]Yan Z,Chakraborty D,Parent C,et al.SeMiTri:A Framework for Semantic Annotation of Heterogeneous Trajectories[C]//The 14th International Conference on Extending Database Technology,Uppsala,Sweden,March21-24,2011.
[54]Bishop C M.Model-Based Machine Learning[J].Philosophical Transactions A:Mathematical,Physicial and Engineering Sciences2013,371(1984):1-17.
[55]Zheng Yu.Methodologies for Cross-Domain Data Fusion:An Overview[J].IEEE Transactions on Big Data,2015,1(1):16-34.
[56]de Montjoye Y A,Hidalgo C A,Verleysen M,et al.Unique in the Crowd:The Privacy Bounds of Human Mobility[J].Scientific Reports,2013(3):1-5.
[57]Mir D J,Isaacman S,Caceres R,et al.DP-WHERE:Differentially Private Modeling of Human Mobility[C]//2013 IEEE International Conference on Big Data,Santa Clara,October 6-9,2013.
[58]Andrés M E,Bordenabe N E,Chatzikokolakis K,et al.Geo-Indistinguish Ability:Differential Privacy for Location-Based Systems[C]//the 2013 ACM SIGSAC Conference on Computer&Communications Security,Berlin,November 4-8,2013.
[59]Hu X,Yuan M,Yao J,et al.Differential Privacy in Telco Big Data Platform[J].Biopolymers,2015,8(12):1692-1703.
[60]Primault V,Mokhtar S B,Lauradoux C,et al.Differentially Private Location Privacy in Practice[C]//The Third Workshop on Mobile Security Technologies,San Jose,May 172014.
(1)主要工作从2010年至2016年第二季度。
(2)一些权威机构已经开始使用智能手机进行连续调查,以降低相应负担并提高数据质量,特别是在捕捉短时间活动方面。