ZTE Communications ›› 2019, Vol. 17 ›› Issue (3): 9-14.DOI: 10.12142/ZTECOM.201903003
• Special Topic • Previous Articles Next Articles
ZHAO Juanjuan1, XU Chengzhong2, MENG Tianhui1
Received:2019-05-09
Online:2019-09-29
Published:2019-12-06
About author:ZHAO Juanjuan received the Ph.D. degree from Chinese Academy of Sciences, China in 2017. She is an assistant professor with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. Her research interests include big data processing, data privacy, and urban computing.|XU Chengzhong received the Ph.D. degree from the University of Hong Kong, China in 1993. He is the Dean of the Faculty of Science and Technology, University of Macau, China and a Chair Professor of Computer Science of UM. He was a Chief Scientist of Shenzhen Institutes of Advanced Technology (SIAT) of Chinese Academy of Sciences and the Director of Institute of Advanced Computing and Digital Engineering of SIAT. He was also in the faculty of Wayne State University, USA for 18 years. Dr. Xu’s research interest is mainly in the areas of parallel and distributed systems, cloud and edge computing, and data-driven intelligence. He has published over 300 peer-reviewed papers on these topics with over 10K citations. Dr. Xu served in the editorial boards of leading journals, including IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Parallel and Distributed Systems, and Journal of Parallel and Distributed Computing. He is the Associate Editor-in-Chief of ZTE Communication. He is IEEE Fellow and the Chair of IEEE Technical Committee of Distributed Processing.|MENG Tianhui received the Ph.D. degree in computer science from Free University of Berlin, Germany in 2017. He is currently an assistant professor with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. His research interests include mobile edge computing, big data processing, blockchain and Internet of Things.
Supported by:ZHAO Juanjuan, XU Chengzhong, MENG Tianhui. Big Data-Driven Residents’ Travel Mode Choice: A Research Overview[J]. ZTE Communications, 2019, 17(3): 9-14.
Add to citation manager EndNote|Ris|BibTeX
URL: https://zte.magtechjournal.com/EN/10.12142/ZTECOM.201903003
Figure 1. Framework of a big data-driven residents’ travel mode choice model.
| [1] | STOPHER P, FITZGERALD C, ZHANG J . Search for a Global Positioning System Device to Measure Person Travel[J]. Transportation Research Part C Emerging Technologies, 2008,16(3):350-369. DOI: 10.1016/j.trc.2007.10.002 |
| [2] | BOHTE W, MAAT K . Deriving and Validating Trip Purposes and Travel Modes for Multi-Day GPS-Based Travel Surveys: A Large-Scale Application in the Netherlands[J]. Transportation Research Part C, 2009,17(3):285-297. DOI: 10.1016/j.trc.2008.11.004 |
| [3] | CHEN C, GONG H, LAWSON C , et al. Evaluating the Feasibility of a Passive Travel Survey Collection in a Complex Urban Environment: Lessons Learned from the New York City Case Study[J]. Transportation Research Part A Policy & Practice, 2010,44(10):830-840. DOI: 10.1016/j.tra.2010.08.004 |
| [4] | ZHENG Y, LIU L, WANG L , et al. Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web [C]//International Conference on World Wide Web, WWW 2008. Beijing, China, 2008: 247-256. DOI: 10.1145/1367497.1367532 |
| [5] | \n \n LAN G, XU W, KHALIFA S , et al. Transportation Mode Detection Using Kinetic Energy Harvesting Wearables [C]//IEEE International Conference on Pervasive Computing and Communication Workshops. Sydney, Australia, 2016: 1-4. DOI: 10.1109/PERCOMW.2016.7457048 |
| [6] | ENDO Y, TODA H, NISHIDA K , et al. Deep Feature Extraction from Trajectories for Transportation Mode Estimation [M]. Advances in Knowledge Discovery and Data Mining. New York, USA: Springer International Publishing, 2016. DOI: 10.1007/978-3-319-31750-2_5 |
| [7] | DAS R D, WINTER S . Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory[J]. International Journal of Geo-Information, 2016,5(11):207. DOI: 10.3390/ijgi5110207 |
| [8] | LOPEZ A J, OCHOA D, GAUTAMA S . Detecting Changes of Transportation-Mode by Using Classification Data [C]//IEEE International Conference on Information Fusion. San Diego, USA, 2015: 2078-2083 |
| [9] | FENG T, TIMMERMANS H J P . Transportation Mode Recognition Using GPS and Accelerometer Data[J]. Transportation Research Part C Emerging Technologies, 2013,37(3):118-130. DOI: 10.1016/j.trc.2013.09.014 |
| [10] | SHIN D, ALIAGA D, TUN?ER B , et al. Urban Sensing: Using Smartphones for Transportation Mode Classification[J]. Computers Environment & Urban Systems, 2015,53:76-86 |
| [11] | JAHANGIRI A, RAKHA H A . Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2015,16(5):2406-2417. DOI: 10.1109/TITS.2015.2405759 |
| [12] | ZHAO F, LI M T, CHOW L F , et al. FSUTMS Mode Choice Modeling: Factors Affecting Transit Use and Access [R]. Tampa, USA: University of South Florida, 2002 |
| [13] | RACCA D P, RATLEDGE E C . Factors that Affect and/or can Alter Mode Choice [R]. Newark, USA: University of Delaware, 2004. |
| [14] | YE X, PENDYALA R M, GOTTARDI G . An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns[J]. Transportation Research Part B Methodological, 2007,41(1):96-113. DOI: 10.1016/j.trb.2006.03.004 |
| [15] | MADHUWANTHI M, MARASINGHE A, RAJAPAKSE R P C J , et al. Factors Influencing to Travel Behavior on Transport Mode Choice—A Case of Colombo Metropolitan Area in Sri Lanka[J]. International Journal of Affective Engineering, 2015,15(2):63-72. DOI: 10.5057/ijae.IJAE-D-15-00044 |
| [16] | BARABINO B, FRANCESCO M D, MOZZONI S . Rethinking Bus Punctuality by Integrating Automatic Vehicle Location Data and Passenger Patterns[J]. Transportation Research Part A, 2015,75:84-95. DOI: 10.1016/j.tra.2015.03.012 |
| [17] | BARABINO B, FRANCESCO M D, MURRU R . An Offline Framework for Reliability Diagnosis by Automatic Vehicle Location Data [C]//Conference on Advanced Systems in Public Transport. Rotterdam, Netherlands, 2015. DOI: 10.1109/tits.2016.2581024 |
| [18] | MA Z, FERREIRA L, MESBAH M , et al. Modelling Bus Travel Time Reliability Using Supply and Demand Data from AVL and Smart Card Systems[J]. Transportation Research Record Journal of the Transportation Research Board, 2015,2533:17-27 |
| [19] | HADJIDIMITRIOU S N, KAPARIAS I, DELL’AMICO M . Investigating Urban Bus Travel Time Reliability Patterns in London Using iBus Automatic Vehicle Locating and Live Bus Arrivals data [C]//6th Symposium of the European Association for Research in Transportation. Haifa, Israel, 2017 |
| [20] | CHEPURI A, JAIRAM R, ARKATKAR S, et al. Travel Time Reliability-Based Performance Indicators Assessment for Bus Routes Using GPS-Based Bus Trajectory under Mixed Traffic Conditions [C]//3rd Conference of Transportation Systems Engineering and Management (CTSEM). Bangalore, India, 2016 |
| [21] | ZHENG Y, LIU Y, YUAN J, et al. Urban Computing with Taxicabs [C]//13th ACM International Conference on Ubiquitous Computing. Beijing, China, 2011: 89-98. DOI: 10.1145/2030112.2030126 |
| [22] | WAITING E, HOWEVER A S F . Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System[J]. Discrete Dynamics in Nature and Society, 2015(1):1-11. DOI: 10.1155/2015/539756 |
| [23] | ZHANG F, ZHAO J, TIAN C , et al. Spatiotemporal Segmentation of Metro Trips Using Smart Card Data [J]. IEEE Transactions on Vehicular Technology, 2016, 65(3):1137-1149. 10. 11 DOI: 09/TVT.2015.2409815 |
| [24] | YUE Z, CHEN F, WANG Z , et al. Classifications of Metro Stations by Clustering Smart Card Data Using the Gaussian Mixture Model[J]. Urban Rapid Rail Transit, 2017,30(2):48-51 |
| [25] | CEAPA I, SMITH C, CAPRA L . Avoiding the Crowds: Understanding Tube Station Congestion Patterns from Trip Data [C]//ACM SIGKDD International Workshop on Urban Computing. Beijing, China, 2012: 134-141. DOI: 10.1145/2346496.2346518 |
| [26] | WANG Y L, ZHENG Y, XUE Y X . Travel Time Estimation of a Path Using Sparse Trajectories [C]//Knowledge Discovery and Data Mining (KDD’14). New York, USA, 2014: 25-34. DOI: / 10.1145/2623330.2623656 |
| [27] | ZHANG J, YU X, TIAN C , et al. Analyzing Passenger Density for Public Bus: Inference of Crowdedness and Evaluation of Scheduling Choices [C]//IEEE 17th International Conference on Intelligent Transportation Systems. Qingdao, China, 2014: 2015-2022. DOI: 10.1109/ITSC.2014.6958000 |
| [28] | ZHAO J, ZHANG F, TU L , et al. Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2017,18(4):790-801. DOI: 10.1109/TITS.2016.2587864 |
| [29] | LI G, YU L, NG W S , et al. Predicting Home and Work Locations Using Public Transport Smart Card Data by Spectral Analysis [C]//IEEE 18th International Conference on Intelligent Transportation Systems. Gran Canaria, Spain, 2015: 2788-2793. DOI: 10.1109/ITSC.2015.445 |
| [30] | ZOU Q, YAO X, ZHAO P , et al. Detecting Home Location and Trip Purposes for Cardholders by Mining Smart Card Transaction Data in Beijing Subway[J]. Transportation, 2016: 1-26. DOI: 10.1007/s11116-016-9756-9 |
| [31] | MA X, LIU C, WEN H , et al. Understanding Commuting Patterns Using Transit Smart Card Data[J]. Journal of Transport Geography, 2017,58:135-145. DOI: 10.1016/j.jtrangeo.2016.12.001 |
| [32] | HUANG L, LI Q, YUE Y . Activity Identification from GPS Trajectories Using Spatial Temporal POIs’ Attractiveness [C]//2nd ACM SIGSPATIAl International Workshop on Location Based Social Networks. San Jose, USA, 2010: 27-30. DOI: 10.1145/1867699.1867704 |
| [33] | FURLETTI B, CINTIA P, RENSO C , et al. Inferring Human Activities from GPS Tracks [C]//ACM SIGKDD International Workshop on Urban Computing. Chicago, USA, 2013. DOI: 10.1145/2505821.2505830 |
| [34] | LIU X . Inferring Trip Purposes and Uncovering Travel Patterns from Taxi Trajectory Data[J]. Cartography & Geographic Information Science, 2016,43(2):103-114. DOI: 10.1080/15230406.2015.1014424 |
| [35] | LIU X, HE J, YAO Y , et al. Classifying Urban Land Use by Integrating Remote Sensing and Social Media Data[J]. International Journal of Geographical Information Science, 2017,31(8):1675-1696. DOI: 10.1080/13658816.2017.1324976 |
| [36] | YUAN N J, ZHENG Y, XIE X , et al. Discovering Urban Functional Zones Using Latent Activity Trajectories[J]. IEEE Transactions on Knowledge & Data Engineering, 2015,27(3):712-725. DOI: 10.1109/TKDE.2014.2345405 |
| [37] | YAO Y, LIU X, LI X , et al. Mapping Fine-Scale Population Distributions at the Building Level by Integrating Multi-Source Geospatial Big Data[J]. International Journal of Geographical Information Science, 2017,31(6):1220-1244. DOI: 10.1080/13658816.2017.1290252 |
| [38] | CASCETTA E . Transportation Systems Engineering: Theory and Methods[M]. Dordrecht, Netherlands: Kluwer Academic Publishers, 2001. DOI: 10.1007/978-1-4757-6873-2 |
| [39] | ABUHAMOUD M A A, RAHMAT R A O K, ISMAIL A B . Modeling of Transport Mode in Libya: A Binary Logit Model for Government Transportation Encouragement[J]. Australian Journal of Basic & Applied Sciences, 2011,5(5):1291-1296 |
| [40] | PRAVEEN KUMAR M, MALLIKARJUNA C . Mode Choice Modelling for Intercity Transportation in India: A Case of Guwahati to Five Metro Cities[J]. International Journal of Earth Sciences and Engineering, 2011,4(6):364-374 |
| [41] | BHAT C R . A Heteroscedastic Extreme Value Model of Intercity Travel Mode Choice[J]. Transportation Research Part B Methodological, 1995,29(6):471-483. DOI: 10.1016/0191-2615(95)00015-6 |
| [42] | HESS S . Advanced Discrete Choice Models with Applications to Transport Demand [D]. London, UK: Imperial College London, 2005 |
| [43] | MINAL, SEKHAR C R . Mode Choice Analysis: The Data, the Models and Future Ahead[J]. International Journal for Traffic and Transport Engineering, 2014,4(3):269-285. DOI: 10.7708/ijtte.2014.4(3).03 |
| [44] | GHAREIB A H . Evaluation of Logit and Probit Models in Mode-Choice Situation[J]. Journal of Transportation Engineering-Asce, 1996,122(4):282-290. DOI: 10.1061/(ASCE)0733-947X(1996)122:4(282) |
| [45] | DOW J K, ENDERSBY J W . Multinomial Probit and Multinomial Logit: a Comparison of Choice Models for Voting Research[J]. Electoral Studies, 2004,23(1):107-122. DOI: 10.1016/S0261-3794(03)00040-4 |
| [46] | BHAT C R, SARDESAI R . The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice[J]. Transportation Research Part B-methodological, 2006,40(9):709-730. DOI: 10.1016/j.trb.2005.09.008 |
| [47] | ABDEL-ATY M, ABDELWAHAB H . Calibration of nested mode choice model for Florida, Final research report, University of central Florida, 2001. |
| [48] | GAUNDRY M J, DAGENAIS M G . The Dogit Model[J]. Transportation Research Part B-methodological, 1979,13(2):105-111. DOI: 10.1016/0191-2615(79)90028-6 |
| [49] | HENSHER D A, TU T T . A Comparison of the Predictive Potential of Artificial Neural Networks and Nested Logit Models for Commuter Mode Choice[J]. Transportation Research Part E Logistics & Transportation Review, 2000,36(3):155-172. DOI: 10.1016/S1366-5545(99)00030-7 |
| [50] | CANTARELLA G E, DE LUCA S . Modeling Transportation Mode Choice Through Artificial Neural Networks [C]///Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. College Park, USA, 2003: 84-90. DOI: 10.1109/ISUMA.2003.1236145 |
| [51] | OMRANI H . Predicting Travel Mode of Individuals by Machine Learning[J]. Transportation Research Procedia, 2015,10:840-849. DOI: 10.1016/j.trpro.2015.09.037 |
| [52] | LEE D, DERRIBLE S, PEREIRA F C . Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling[J]. Transportation Research Record Journal of the Transportation Research Board, 2018. DOI: 10.1177/0361198118796971 |
| [1] | YANG Haojun, ZHENG Kan, LEI Lei, and XIANG Wei. Heterogeneous Vehicular Networks for Social Networks: Requirements and Challenges [J]. ZTE Communications, 2016, 14(3): 29-35. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
