A Review of GPS Trajectories Classification Based on Transportation Mode
Abstract
:1. Introduction
2. Preliminaries
2.1. Discussion: GPS Data Acquisition and Characteristics
2.2. A Macroscopic Classification of GPS Data Based on Trajectories Generation Way
3. GPS Data Classification Based on the Transportation Mode
3.1. Overview
3.2. SMT Classification Based on Transportation Mode
3.3. MMT Classification Based on Transportation Mode
3.3.1. Point-Based Classification for MMT
3.3.2. Segment-Based Classification for MMT
3.3.3. Evaluation Indicators for GPS Data Classification Based on Transportation Mode
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Method | GPS Data Source | Additional Information | Movement Features | Classification Algorithm | Precision |
---|---|---|---|---|---|
Ref. [61] | Collected 60 days of GPS data from one person | POI information (bus stops and parking lots) | Location; Velocity; Direction | Hierarchical Markov model (unsupervised learning) | 98% |
Ref. [62] | GPS device built-in Smart phone | no | Speed; Acceleration; Number of satellites | Neural networks (supervised learning) | 82% |
Ref. [63] | GPS device built-in Smart phone | Real-time bus locations; spatial rail and spatial bus stop | GPS data precision; Speed; Heading; Acceleration | Bayesian Net; Decision Tree; Random Forest; Naïve Bayesian and Multilayer Perceptron | 93.5% (Random Forest) |
Ref. [64] | GPS device built-in Smart phone | Sensor data from accelerometer and magnetometer | Speed; Acceleration; Number of satellites; Electromagnetic levels | Neural network-based artificial intelligence (supervised learning) | 85% |
Ref. [54] | GPS data collected by Android-based smartphone | Bus, train, and tram network | Average speed, maximum speed | Multi-layered neuro-fuzzy based model (MLANFIS) | 83% |
Ref. [65] | GPS devices built-in smart phone | Bus stops, rail stations, road network, socio-demographic characteristics of travelers | speed | Dynamic Bayesian Networks (Unsupervised classification) | 72.5% |
Ref. [66] | GPS devices built-in smartphone | Railway, motorway, charging stations, public transport stops | speed | Support Vectors machines-based model | 94% |
Method | GPS Data Source | Additional Information | Movement Features | Classification Algorithm | Precision |
---|---|---|---|---|---|
Ref. [55] | 4 months of GPS data by one person; collected by 5 participants in 1 week | bus stops and parking lots | Location; Velocity; Direction | Bayesian network (supervised learning) | 80% |
Ref. [36] | Public data of OSM | no | Velocity; acceleration; turning angle; straightness; | SVM (supervised learning) | 94% |
Ref. [51] | Collected by 65 users by using GPS-enabled device | no | Distance; Speed; Acceleration; Heading; Stop | Decision Tree-based inference model (supervised learning) | 75% |
Ref. [58] | Public data on OSM website | Bus station | Stop; Signal shortage; Speed; Distance; | Fuzzy logic concept (supervised learning) | 91.6% |
Ref. [81] | Bus traces were acquired from Inovative Tampere Site’s Journey APIs; other trajectories were acquired from the OSM and Geolife projects | no | Speed, Acceleration | Random forest | 88.5% |
Ref. [71] | Public data of Geo-life | Bus station | Velocity category, Acceleration category, Behavior category (e.g., bus stop rate) | DT and five kinds of DT-based combinatorial classification method | 86.5% |
Ref. [77] | GPS dataset from the Space-Time Activity Research project in Halifax, Canada | no | Median speed, median change in heading, total duration | Multinomial logit model | 90% |
Ref. [82] | Collected by 81 participants in two-weeks | no | Distance; Speed; Acceleration; Heading | SVM (supervised learning) | 88% |
Ref. [80] | Public data of Geo-life | no | Time-slice type, Acceleration change rate, Velocity, Acceleration, VCR, SR, HCR | Random Forest (supervised learning) | 82.85% |
References
- Chon, J.; Cha, H. Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Comput. 2011, 10, 58–67. [Google Scholar] [CrossRef]
- Chatzimilioudis, G.; Konstantinidis, A.; Laoudias, C.; Zeinalipour-Yazti, D. Crowdsourcing with smartphones. IEEE Internet Comput. 2012, 16, 36–44. [Google Scholar] [CrossRef]
- Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal 2014, 79, 1–14. [Google Scholar] [CrossRef]
- Realini, E.; Caldera, S.; Pertusini, L. Precise GNSS Positioning Using Smart Devices. Sensors 2017, 17, 2434. [Google Scholar] [CrossRef] [PubMed]
- Odolinski, R.; Teunissen, P.J.G. An assessment of smartphone and low-cost multi-GNSS single-frequency RTK positioning for low, medium and high ionospheric disturbance periods. J. Geod. 2018, 1–22. [Google Scholar] [CrossRef]
- Phithakkitnukoon, S.; Horanont, T.; Di Lorenzo, G.; Shibasaki, R.; Ratti, C. Activity-aware map: Identifying human daily activity pattern using mobile phone data. Hum. Behav. Underst. 2010, 6219, 14–25. [Google Scholar]
- Hu, X.; An, S.; Wang, J. Taxi driver’s operation behavior and passengers’ demand analysis based on GPS data. J. Adv. Transp. 2018, 2018, 6197549. [Google Scholar] [CrossRef]
- Hassel, D.V.; Velden, L.V.D.; Bakker, D.D.; Batenburg, R. Age-related differences in working hours among male and female GPS: An SMS-based time use study. Hum. Resour. Health 2017, 15, 84. [Google Scholar] [CrossRef] [PubMed]
- Nethery, E.; Mallach, G.; Rainham, D.; Goldberg, M.S.; Wheeler, A.J. Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: An automated method. Environ. Health 2014, 13, 33. [Google Scholar] [CrossRef] [PubMed]
- Yang, X.; Tang, L.; Niu, L.; Zhang, X.; Li, Q. Generating lane-based intersection maps from crowdsourcing big trace data. Transp. Res. Part C Emerg. Technol. 2018, 89, 168–187. [Google Scholar] [CrossRef]
- Yang, X.; Tang, L.; Stewart, K.; Dong, Z.; Zhang, X.; Li, Q. Automatic change detection in lane-level road networks using GPS trajectories. Int. J. Geogr. Inf. Sci. 2017, 12, 1–21. [Google Scholar] [CrossRef]
- Tang, L.; Yang, X.; Dong, Z.; Li, Q. CLRIC: Collecting lane-based road information via crowdsourcing. IEEE Trans. Intell. Transp. Syst. 2016, 17, 2552–2562. [Google Scholar] [CrossRef]
- Tang, L.; Kan, Z.; Zhang, X.; Yang, X.; Huang, F.; Li, Q. Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data. Cartogr. Geogr. Inf. Sci. 2016, 43, 417–426. [Google Scholar] [CrossRef]
- Pan, G.; Qi, G.; Wu, Z.; Zhang, D.; Li, S. Land-use classification using taxi GPS trajectories. IEEE Trans. Intell. Transp. Syst. 2013, 14, 113–123. [Google Scholar] [CrossRef]
- Tu, W.; Cao, J.; Yue, Y.; Shaw, S.-L.; Zhou, M.; Wang, Z.; Chang, X.; Xu, Y.; Li, Q. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. Int. J. Geogr. Inf. Sci. 2017, 31, 2331–2358. [Google Scholar] [CrossRef]
- Yang, J.; Dong, J.; Hu, L. A data-driven optimization-based approach for siting and sizing of electric taxi charging station. Transp. Res. Part C Emerg. Technol. 2017, 77, 462–477. [Google Scholar] [CrossRef]
- Bao, J.; Zheng, Y.; Wilkie, D.; Mokbel, M. Recommendations in location-based social networks: A survey. GeoInformatica 2015, 19, 525–565. [Google Scholar] [CrossRef]
- Cui, G.; Luo, J.; Wang, X. Personalized travel route recommendation using collaborative filtering based on GPS trajectories. Int. J. Digit. Earth 2018, 11, 284–307. [Google Scholar] [CrossRef]
- Jean Damascène, M.; Timpf, S. Trajectory data mining: A review of methods and applications. J. Spat. Inf. Sci. 2016, 13, 61–99. [Google Scholar]
- Pablo Samuel, C.; Zhang, D.; Chen, C.; Li, S.; Pan, G. From taxi GPS traces to social and community dynamics: A survey. ACM Comput. Surv. 2013, 46, 17. [Google Scholar]
- Tao, F.; Harry, J.P. Timmermans. Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data. Transp. Plan. Technol. 2016, 39, 180–194. [Google Scholar]
- Shen, L.; Stopher, P.R. Review of GPS travel survey and GPS data-processing methods. Transp. Rev. 2014, 34, 316–334. [Google Scholar] [CrossRef]
- Gong, L.; Morikawa, T.; Yamamoto, T.; Sato, H. Deriving personal trip data from GPS data: A literature review on the existing methodologies. Procedia Soc. Behav. Sci. 2014, 138, 557–565. [Google Scholar] [CrossRef]
- Prelipcean, A.C.; Gidófalvi, G.; Susilo, Y.O. Transportation mode detection—An in-depth review of applicability and reliability. Transp. Rev. 2017, 37, 442–464. [Google Scholar] [CrossRef]
- Marija, N.; Bierlaire, M. Review of transportation mode detection approaches based on smartphone data. In Proceedings of the 17th Swiss Transport Research Conference, Ascona, Switzerland, 17–19 May 2017. [Google Scholar]
- Yang, X.; Tang, L.; Zhang, X.; Li, Q. A Data Cleaning Method for Big Trace Data Using Movement Consistency. Sensors 2018, 18, 824. [Google Scholar] [CrossRef] [PubMed]
- Lane, N.D.; Eisenman, S.B.; Musolesi, M.; Miluzzo, E.; Campbell, A.T. Urban sensing systems: Opportunistic or participatory? In Proceedings of the 9th ACM Workshop on Mobile Computing Systems and Applications, Napa Valley, CA, USA, 25–26 February 2008; pp. 11–16. [Google Scholar]
- Harris, D.; Smith, D.; O’Neil, C.; Severinsen, J. The role of real-time crowdsourced information and technology in supporting traveller information and network efficiency. In Proceedings of the Automated Vehicles Symposium, Stuttgart, Germany, 31 May–2 June 2016. [Google Scholar]
- Haklay, M.; Weber, P. Openstreetmap: User-generated street maps. IEEE Pervasive Comput. 2008, 7, 12–18. [Google Scholar] [CrossRef]
- Zheng, Y.; Chen, Y.; Li, Q.; Xie, X.; Ma, W.Y. Understanding transportation modes based on GPS data for web applications. ACM Trans. Web 2010, 4, 1. [Google Scholar] [CrossRef]
- Buchin, M.; Driemel, A.; van Kreveld, M.; Sacristan, V. Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. J. Spat. Inf. Sci. 2011, 3, 33–63. [Google Scholar]
- Soleymani, A.; Pennekamp, F.; Dodge, S.; Weibel, R. Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition. Methods Ecol. Evol. 2017, 8, 1113–1123. [Google Scholar] [CrossRef]
- Soleymani, A.; Cachat, J.; Robinson, K.; Dodge, S.; Kalueff, A.; Weibel, R. Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement. J. Spat. Inf. Sci. 2014, 8, 1–25. [Google Scholar] [CrossRef]
- Soleymani, A.; Van Loon, E.E.; Robert, W. Capability of movement features extracted from GPS trajectories for the classification of fine-grained behaviors. Connecting a Digital Europe through Location and Place. In Proceedings of the AGILE’2014 International Conference on Geographic Information Science, Castellón, Spain, 3–6 June 2014. [Google Scholar]
- Bovet, P.; Benhamou, S. Optimal sinuosity in central place foraging movements. Anim. Behav. 1991, 42, 57–62. [Google Scholar] [CrossRef]
- Fisher, N.I. Statistical Analysis of Circular Data; Cambridge University Press: Cambridge, UK, 1993. [Google Scholar]
- Benhamou, S. How to reliably estimate the tortuosity of an animal’s path: Straightness, sinuosity, or fractal dimension? J. Theor. Biol. 2004, 229, 209–220. [Google Scholar] [CrossRef] [PubMed]
- Dodge, S.; Weibel, R.; Forootan, E. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Comput. Environ. Urban Syst. 2009, 33, 419–434. [Google Scholar] [CrossRef] [Green Version]
- Nams, V.O. Using animal movement paths to measure response to spatial scale. Oecologia 2005, 143, 179–188. [Google Scholar] [CrossRef] [PubMed]
- Li, X. Using complexity measures of movement for automatically detecting movement types of unknown GPS trajectories. Am. J. Geogr. Inf. Syst. 2014, 3, 63–74. [Google Scholar]
- Ohashi, H.; Akiyama, T.; Yamamoto, M.; Sato, A. Modality Classification Method Based on the Model of Vibration Generation while Vehicles are Running. Inf. Process. Soc. Jpn. 2013, 56, 37–42. [Google Scholar]
- Etemad, M.; Júnior, A.S.; Matwin, S. Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal. In Proceedings of the Canadian Conference on Artificial Intelligence, Toronto, ON, Canada, 8–11 May 2018. [Google Scholar]
- Jahangiri, A.; Rakha, H.A. Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Trans. Intell. Transp. Syst. 2015, 16, 2406–2417. [Google Scholar] [CrossRef]
- Zhu, X.; Li, J.; Liu, Z.; Yang, F. Learning Transportation Mode Choice for Context-Aware Services with Directed-Graph-Guided Fused Lasso from GPS Trajectory Data. In Proceedings of the IEEE International Conference on Web Services, Honolulu, HI, USA, 25–30 June 2017; pp. 692–699. [Google Scholar]
- Gonzalez, P.A.; Weinstein, J.S.; Barbeau, S.J.; Labrador, M.A.; Winters, P.L.; Georggi, N.L.; Perez, R. Automating mode detection for travel behaviour analysis by using global positioning system senabled mobile phones and neural networks. IET Intell. Transp. Syst. 2010, 4, 37–49. [Google Scholar] [CrossRef]
- Xiao, G.; Juan, Z.; Zhang, C. Travel mode detection based on GPS track data and Bayesian networks. Comput. Environ. Urban Syst. 2015, 54, 14–22. [Google Scholar] [CrossRef]
- Endo, Y.; Toda, H.; Nishida, K.; Kawanobe, A. Deep Feature Extraction from Trajectories for Transportation Mode Estimation. In Advances in Knowledge Discovery and Data Mining; Springer: New York, NY, USA, 2016. [Google Scholar]
- Wang, H.; Liu, G.J.; Duan, J.; Zhang, L. Detecting Transportation Modes Using Deep Neural Network. IEICE Trans. Inf. Syst. 2017, 100, 1132–1135. [Google Scholar] [CrossRef]
- Dabiri, S.; Heaslip, K. Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C Emerg. Technol. 2018, 86, 360–371. [Google Scholar] [CrossRef] [Green Version]
- Mountain, D.; Raper, J. Modelling human spatio-temporal behaviour: A challenge for location-based services. In Proceedings of the 6th International Conference on Geocomputation, Brisbane, Australia, 24–26 September 2001. [Google Scholar]
- Zheng, Y.; Liu, L.; Wang, L.; Xie, X. Learning transportation mode from raw GPS data for geographic applications on the web. In Proceedings of the 17th International Conference on World Wide Web, Beijing, China, 21–25 April 2008; pp. 247–256. [Google Scholar]
- Gurarie, E.; Andrews, R.D.; Laidre, K.L. A novel method for identifying behavioural changes in animal movement data. Ecol. Lett. 2010, 12, 395–408. [Google Scholar] [CrossRef] [PubMed]
- Schuessler, N.; Axhausen, K.W. Processing Raw Data from Global Positioning Systems Without Additional Information. Transp. Res. Rec. J. Transp. Res. Board 2009, 2105, 28–36. [Google Scholar] [CrossRef]
- Zheng, Y.; Xie, X.; Ma, W.Y. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 2010, 33, 32–39. [Google Scholar]
- Xiao, G.; Juan, Z.; Gao, J. Inferring trip ends from GPS data based on smartphones in Shanghai. In Proceedings of the Transportation Research Board 94th Annual Meeting, Washington, DC, USA, 11–15 January 2015. [Google Scholar]
- Dodge, S.; Laube, P.; Weibel, R. Movement similarity assessment using symbolic representation of trajectories. Int. J. Geogr. Inf. Syst. 2012, 26, 1563–1588. [Google Scholar] [CrossRef] [Green Version]
- Thiebault, A.; Tremblay, Y. Splitting animal trajectories into fine-scale behaviorally consistent movement units: Breaking points relate to external stimuli in a foraging seabird. Behav. Ecol. Sociobiol. 2013, 67, 1013–1026. [Google Scholar] [CrossRef]
- Das, R.; Winter, S. Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory. ISPRS Int. J. Geo-Inf. 2016, 5, 207. [Google Scholar] [CrossRef]
- Liao, L.; Patterson, D.J.; Fox, D.; Kautz, H. Building personal maps from GPS data. Ann. N. Y. Acad. Sci. 2006, 1093, 249–265. [Google Scholar] [CrossRef] [PubMed]
- Thierry, B.; Chaix, B.; Yan, K. Detecting activity locations from raw GPS data: A novel kernel-based algorithm. Int. J. Health Geogr. 2013, 12, 14. [Google Scholar] [CrossRef] [PubMed]
- Hwang, S.; Evans, C.; Hanke, T.M. Detecting Stop Episodes from GPS Trajectories with Gaps. In Seeing Cities Through Big Data; Springer: New York, NY, USA, 2017. [Google Scholar]
- Biljecki, F.; Ledoux, H.; Van Oosterom, P. Transportation mode-based segmentation and classification of movement trajectories. Int. J. Geogr. Inf. Sci. 2013, 27, 385–407. [Google Scholar] [CrossRef] [Green Version]
- Prelipcean, A.C.; Gidofalvi, G.; Susilo, Y.O. Measures of transport mode segmentation of trajectories. Int. J. Geogr. Inf. Sci. 2016, 30, 1763–1784. [Google Scholar] [CrossRef]
- Geurs, K.T.; Thomas, T.; Bijlsma, M.; Douhou, S. Automatic trip and mode detection with move smarter: First results from the dutch mobile mobility panel. Transp. Res. Procedia 2015, 11, 247–262. [Google Scholar] [CrossRef]
- Liao, L.; Patterson, D.J.; Fox, D.; Kautz, H. Learning and inferring transportation routines. Artif. Intell. 2007, 171, 311–331. [Google Scholar] [CrossRef]
- Byon, Y.J.; Abdulhai, B.; Shalaby, A. Real-time transportation mode detection via tracking global positioning system mobile devices. J. Intell. Transp. Syst. 2009, 13, 161–170. [Google Scholar] [CrossRef]
- Stenneth, L.; Wolfson, O.; Yu, P.S.; Xu, B. Transportation mode detection using mobile phones and GIS information. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, USA, 1–4 November 2011; pp. 54–63. [Google Scholar]
- Byon, Y.J.; Liang, S. Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors. J. Intell. Transp. Syst. 2014, 18, 264–272. [Google Scholar] [CrossRef]
- Bantis, T.; Haworth, J. Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics. Transp. Res. Part C Emerg. Technol. 2017, 80, 286–309. [Google Scholar] [CrossRef]
- Semanjski, I.; Gautama, S.; Ahas, R.; Witlox, F. Spatial context mining approach for transport mode recognition from mobile sensed big data. Comput. Environ. Urban Syst. 2017, 66, 38–52. [Google Scholar] [CrossRef]
- Nick, T.; Coersmeier, E.; Geldmacher, J.; Goetze, J. Classifying means of transportation using mobile sensor data. In Proceedings of the 2010 IEEE International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18–23 July 2010. [Google Scholar]
- Feng, T.; Timmermans, H.J. Transportation mode recognition using GPS and accelerometer data. Transp. Res. Part C Emerg. Technol. 2013, 37, 118–130. [Google Scholar] [CrossRef]
- Hemminki, S.; Nurmi, P.; Tarkoma, S. Accelerometer-based transportation mode detection on smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy, 11–15 November 2013; p. 13. [Google Scholar]
- Shin, D.; Aliaga, D.; Tunçer, B.; Arisona, S.M.; Kim, S.; Zünd, D.; Schmitt, G. Urban sensing: Using smartphones for transportation mode classification. Comput. Environ. Urban Syst. 2015, 53, 76–86. [Google Scholar] [CrossRef]
- Shafique, M.A.; Hato, E. Use of acceleration data for transportation mode prediction. Transportation 2015, 42, 163–188. [Google Scholar] [CrossRef]
- Lan, G.; Xu, W.; Khalifa, S.; Hassan, M.; Hu, W. Transportation mode detection using kinetic energy harvesting wearables. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, Sydney, Australia, 14–18 March 2016. [Google Scholar]
- Jahangiri, A.; Rakha, H. Developing a support vector machine (SVM) classifier for transportation mode identification by using mobile phone sensor data. In Proceedings of the Transportation Research Board 93rd Annual Meeting, Washington, DC, USA, 12–16 January2014. [Google Scholar]
- Zhao, F.; Ghorpade, A.; Pereira, F.C.; Zegras, C.; Ben-Akiva, M. Stop detection in smartphone-based travel surveys. Transp. Res. Procedia 2015, 11, 218–226. [Google Scholar] [CrossRef]
- Gautama, S.; Atzmueller, M.; Kostakos, V.; Gillis, D.; Hosio, S. Observing Human Activity Through Sensing. In Participatory Sensing, Opinions and Collective Awareness; Springer: New York, NY, USA, 2017; pp. 47–68. [Google Scholar]
- Siłanowicka, K.; Vandrol, J.; Oshan, T. Analysis of human mobility patterns from GPS trajectories and contextual information. Int. J. Geogr. Inf. Sci. 2016, 30, 881–906. [Google Scholar] [CrossRef]
- Dalumpines, R.; Scott, D.M. Making mode detection transferable: Extracting activity and travel episodes from GPS data using the multinomial logit model and python. Transp. Plan. Technol. 2017, 5, 523–539. [Google Scholar] [CrossRef]
- Zheng, Y.; Wang, L.; Liu, L.; Xie, X. Learning Transportation Modes from Raw GPS Data. U.S. Patent US 8015144 B2, 26 February 2017. [Google Scholar]
- Liang, J.; Zhu, Q.; Zhu, M.; Li, M.; Li, X.; Wang, J.; You, S.; Zhang, Y. An enhanced transportation mode detection method based on GPS data. In Proceedings of the International Conference of Pioneering Computer Scientists, Engineers and Educators, Changsha, China, 22–24 September 2017. [Google Scholar]
- Zhu, Q.; Zhu, M.; Li, M.; Fu, M.; Huang, Z.; Gan, Q.; Zhou, Z. Identifying transportation modes from raw GPS data. In Communications in Computer and Information Science, Proceedings of the International Conference of Pioneering Computer Scientists, Engineers and Educators, Harbin, China, 20–22 August 2016; Springer: Singapore, 2016; Volume 623, pp. 395–409. [Google Scholar] [CrossRef]
- Mäenpää, H.; Lobov, A.; Lastra, J.L.M. Travel mode estimation for multi-modal journey planner. Transp. Res. Part C Emerg. Technol. 2017, 82, 273–289. [Google Scholar] [CrossRef]
- Bolbol, A.; Cheng, T.; Tsapakis, I.; Haworth, J. Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Comput. Environ. Urban Syst. 2012, 36, 526–537. [Google Scholar] [CrossRef]
- Xiao, Z.; Wang, Y.; Fu, K.; Wu, F. Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers. ISPRS Int. J. Geo-Inf. 2017, 6, 57. [Google Scholar] [CrossRef]
- Gurarie, E.; Bracis, C.; Delgado, M.; Meckley, T.D.; Kojola, I.; Wagner, C.M. What is the animal doing? Tools for exploring behavioural structure in animal movements. J. Anim. Ecol. 2016, 85, 69–84. [Google Scholar] [CrossRef] [PubMed]
- Geng, X.; Smith-Miles, K.; Wang, L.; Li, M.; Wu, Q. Context-aware fusion: A case study on fusion of gait and face for human identification in video. Pattern Recognit. 2010, 43, 3660–3673. [Google Scholar] [CrossRef]
- Brum-Bastos, V.S.; Long, J.A.; Demšar, U. Dynamic trajectory annotation for integrating environmental and movement data. In Proceedings of the Visually-Supported Computational Movement Analysis Workshop-AGILE, Helsinki, Finland, 14 June 2016. [Google Scholar]
Position Precision | Real-Time | Movement Information | |
---|---|---|---|
Passive Way | The precision of GPS data varies in a specific range and can be improved using automated quality algorithms. Data quality is ensured through standardized collection method. | High | Variable depending on application requirement |
Active Way | The precision of GPS data varies in an unknown range. Data quality can’t be guaranteed. | Variable depending on level of engagement | Variable depending on users’ behavior |
Motorial descriptors | 1. Speed (average, standard deviation, median value, skewness, approximate entropy, frequency) |
2. Acceleration (average, standard deviation, median value, skewness, approximate entropy, frequency) | |
3. Turning angle/azimuth/heading (average, standard deviation, median value, skewness, approximate entropy, frequency) | |
4. Distance (average, standard deviation, median value, skewness, approximate entropy, frequency) [32] | |
5. First passage-time [33] | |
Geometric descriptors | 6. Straightness (multi-scale) [34] |
7. Straightness index (multi-scale) [35] 8. Sinuosity/tortuosity of multi-scale [36,37,38,39] | |
9. Fractal dimension |
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Yang, X.; Stewart, K.; Tang, L.; Xie, Z.; Li, Q. A Review of GPS Trajectories Classification Based on Transportation Mode. Sensors 2018, 18, 3741. https://doi.org/10.3390/s18113741
Yang X, Stewart K, Tang L, Xie Z, Li Q. A Review of GPS Trajectories Classification Based on Transportation Mode. Sensors. 2018; 18(11):3741. https://doi.org/10.3390/s18113741
Chicago/Turabian StyleYang, Xue, Kathleen Stewart, Luliang Tang, Zhong Xie, and Qingquan Li. 2018. "A Review of GPS Trajectories Classification Based on Transportation Mode" Sensors 18, no. 11: 3741. https://doi.org/10.3390/s18113741