Transportation mode detection – an in-depth review of applicability and reliability

References

• Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., & Vaisman, A. (2007). A model for enriching trajectories with semantic geographical information. Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, Seattle, Washington, pp. 22:1–22:8.
   Google Scholar
• Andrews, R., Diederich, J., & Tickle, A. B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based Systems, 8(6), 373–389. doi: 10.1016/0950-7051(96)81920-4
   Web of Science ®Google Scholar
• Andrienko, G., Andrienko, N., & Wrobel, S. (2007). Visual analytics tools for analysis of movement data. SIGKDD Exploration Newsletter, 9(2), 38–46. doi: 10.1145/1345448.1345455
   Google Scholar
• Apple. (2015). Motion activity. Retrieved June 8, 2015, from https://developer.apple.com/library/ios/documentation/CoreMotion/Reference/CMMotionActivityManager_class/
   Google Scholar
• Axhausen, K. W., Schönfelder, S., Wolf, J., Oliveira, M., & Samaga, U. (2003). 80 weeks of GPS-traces: Approaches to enriching the trip information. 83rd Transportation research board meeting, Washington, DC.
   Google Scholar
• Banko, M., & Brill, E. (2001). Scaling to very very large corpora for natural language disambiguation. In Proceedings of the 39th annual meeting on association for computational linguistics (pp. 26–33). Stroudsburg, PA: Association for Computational Linguistics. doi:10.3115/1073012.1073017
   Google Scholar
• Biljecki, F., Hugo, L., & van Oosterom, P. (2013). Transportation mode-based segmentation and classification of movement trajectories. International Journal of Geographical Information Science, 27(2), 385–407. doi: 10.1080/13658816.2012.692791
   Web of Science ®Google Scholar
• Bohte, W., & Maat, K. (2009). Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies, 17(3), 285–297. doi: 10.1016/j.trc.2008.11.004
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Bricka, S., & Bhat, C. R. (2006). Comparative analysis of global positioning system-based and travel survey-based data. Transportation Research Record: Journal of the Transportation Research Board, 1972, 9–20. doi: 10.3141/1972-04
   Google Scholar
• Chung, E. H., & Shalaby, A. (2005). A trip reconstruction tool for gps-based personal travel surveys. Transportation Planning and Technology, 28(5), 381–401. doi: 10.1080/03081060500322599
   Web of Science ®Google Scholar
• Das, R. D., Ronald, N., & Winter, S. (2014). Clustering based transfer detection with fuzzy activity recognition from smart-phone GPS trajectories. 2014 IEEE 17th international conference on intelligent transportation systems (ITSC), Qingdao, China, Vol. 17, pp. 3138–3143.
   Google Scholar
• Draijer, G., Kalfs, N., & Perdok, J. (2000). Global positioning system as data collection method for travel research. Transportation Research Record: Journal of the Transportation Research Board, 1719, 147–153. doi: 10.3141/1719-19
   Google Scholar
• Drchal, J., Certicky, M., & Jakob, M. (2015). Fully agent-based simulation model of multimodal mobility in European cities. Proceedings of the 4th international conference on models and technologies for intelligent transportation systems, Budapest, Hungary, pp. 229–236.
   Google Scholar
• Elkan, C., Berenji, H., Chandrasekaran, B., De Silva, C., Attikiouzel, Y., Dubois, D., & Yuan, B. (1994). The paradoxical success of fuzzy logic. IEEE Expert, 9(4), 3–49. doi: 10.1109/64.336150
   Google Scholar
• Figo, D., Diniz, P. C., Ferreira, D. R., & Cardoso, J. a. M. (2010). Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing, 14(7), 645–662. doi: 10.1007/s00779-010-0293-9
   Web of Science ®Google Scholar
• Gong, L., Morikawa, T., Yamamoto, T., & Sato, H. (2014). Deriving personal trip data from GPS data: A literature review on the existing methodologies. Procedia – Social and Behavioral Sciences, 138, 557–565. doi: 10.1016/j.sbspro.2014.07.239
   Google Scholar
• Google. (2015). Activities. Retrieved June 8, 2015, from https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognition
   Google Scholar
• Hägerstrand, T. (1962). Geographic measurements of migration. Swedish data. In J. Sutter (Ed.), Human displacements (pp. 61–83). Paris: Hachette.
   Google Scholar
• Hägerstrand, T. (1970). What about people in regional science? Papers in Regional Science, 24(1), 7–24. doi: 10.1111/j.1435-5597.1970.tb01464.x
   Google Scholar
• Haklay, M., & Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18. doi: 10.1109/MPRV.2008.80
   Web of Science ®Google Scholar
• Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1), 1–12. doi: 10.1021/ci0342472
   PubMedGoogle Scholar
• Hemminki, S., Nurmi, P., & Tarkoma, S. (2013). Accelerometer-based transportation mode detection on smartphones. Proceedings of the 11th ACM conference on embedded networked sensor systems, Rome, Italy, Vol. 13, pp. 13:1–13:14.
   Google Scholar
• Jin, Y., Von Seelen, W., & Sendhoff, B. (1999). On generating fc 3 fuzzy rule systems from data using evolution strategies. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(6), 829–845. doi: 10.1109/3477.809036
   PubMed Web of Science ®Google Scholar
• Klepeis, N. E., Tsang, A. M., & Behar, J. V. (1996). Analysis of the National Human Activity Pattern Survey (NHAPS) respondents from a standpoint of exposure assessment. US EPA, Las Vegas, NV.
   Google Scholar
• Krause, J., Perer, A., & Bertini, E. (2016). Using visual analytics to interpret predictive machine learning models. arXiv preprint arXiv:1606.05685.
   Google Scholar
• Krumm, J., & Horvitz, E. (2006). Predestination: Inferring destinations from partial trajectories. Proceedings of the 8th international conference on ubiquitous computing, California, USA, pp. 243–260. Berlin:Springer-Verlag.
   Google Scholar
• MacEachren, A. M. (1995). How maps work: Representation, visualization, and design. London: Guilford Press.
   Google Scholar
• Manzoni, V., Maniloff, D., Kloeckl, K., & Ratti, C. (2010). Transportation mode identification and real-time CO2 emission estimation using smartphones (Technical Report). Massachusetts Institute of Technology.
   Google Scholar
• Montini, L., Rieser-Schüssler, N., & Axhausen, K. W. (2014). Personalisation in multi-day GPS and accelerometer data processing. Proceedings of the 14th Swiss Transport Research Conference (STRC), Ascona, Switzerland.
   Google Scholar
• Noland, R. B. (2003). Traffic fatalities and injuries: The effect of changes in infrastructure and other trends. Accident Analysis & Prevention, 35(4), 599–611. doi: 10.1016/S0001-4575(02)00040-4
   PubMed Web of Science ®Google Scholar
• Ogle, J., Guensler, R., & Elango, V. (2005). Georgia's commute Atlanta value pricing program: Recruitment methods and travel diary response rates. Journal of the Transportation Research Board, 1931(1), 28–37. doi: 10.3141/1931-04
   Google Scholar
• Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008). A clustering-based approach for discovering interesting places in trajectories. Proceedings of the 2008 ACM symposium on applied computing, Fortaleza, Ceara, Brazil, pp. 863–868.
   Google Scholar
• Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., & Yan, Z. (2013). Semantic trajectories modeling and analysis. ACM Computing Surveys, 45(4), 42:1–42:32. doi: 10.1145/2501654.2501656
   Web of Science ®Google Scholar
• Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2014). Mobility collector. Journal of Location Based Services, 8(4), 229–255. doi: 10.1080/17489725.2014.973917
   Google Scholar
• Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2015). Comparative framework for activity-travel diary collection systems. Proceedings of the 4th international conference on models and technologies for Intelligent Transportation Systems, pp. 251–258.
   Google Scholar
• Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2016). Measures of transport mode segmentation of trajectories. International Journal of Geographical Information Science, 30(9), 1763–1784. doi:10.1080/13658816.2015.1137297 doi: 10.1080/13658816.2015.1137297
   Web of Science ®Google Scholar
• Prelipcean, A. C., Schmid, F., & Shirabe, T. (2015). A space time alarm. In Progress in Location-Based Services 2014 (pp. 187–198). Springer International.
   Google Scholar
• Rasmussen, T. K., Ingvardson, J. B., Halldórsdóttir, K., & Nielsen, O. A. (2013). Using wearable GPS devices in travel surveys: A case study in the Greater Copenhagen Area. In Proceedings from the annual transport conference at Aalborg University.
   Google Scholar
• Rasmussen, T. K., Ingvardson, J. B., Halldórsdóttir, K., & Nielsen, O. A. (2015). Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area. Computers, Environment and Urban Systems, 54, 301–313. doi: 10.1016/j.compenvurbsys.2015.04.001
   Web of Science ®Google Scholar
• Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., & Srivastava, M. (2010). Using mobile phones to determine transportation modes. ACM Transanctions on Sensor Networks, 6(2), 13:1–13:27.
   Web of Science ®Google Scholar
• Rocha, J., Oliveira, G., Alvares, L., Bogorny, V., & Times, V. (2010). DB-SMoT: A direction-based spatio-temporal clustering method. Intelligent systems, 2010 5th IEEE international conference, London, UK, pp. 114–119.
   Google Scholar
• Schönfelder, S., Axhausen, K. W., Antille, N., & Bierlaire, M. (2002). Exploring the potentials of automatically collected GPS data for travel behaviour analysis – a Swedish data source. GITechnologien für Verkehr und Logistik, https://infoscience.epfl.ch/record/117085?ln=en
   Google Scholar
• Schüssler, N., & Axhausen, K. W. (2009). Processing raw data from global positioning systems without additional information. Journal of the Transportation Research Board, 2105, 28–36. doi: 10.3141/2105-04
   Web of Science ®Google Scholar
• Schüssler, N., Montini, L., & Dobler, C. (2011). Improving post-processing routines for GPS observations using prompted-recall data. 9th International conference on survey methods in transport, Termas de Puyehue, Chile.
   Google Scholar
• Shah, R. C., Wan, C. y., Lu, H., & Nachman, L. (2014). Classifying the mode of transportation on mobile phones using GIS information. Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. New York, NY: ACM.
   Google Scholar
• Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS data-processing methods. Transport Reviews, 34(3), 316–334. doi: 10.1080/01441647.2014.903530
   Web of Science ®Google Scholar
• Stenneth, L., Wolfson, O., Yu, P. S., & Xu, B. (2011). Transportation mode detection using mobile phones and GIS information. Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, Chicago, IL, pp. 54–63.
   Google Scholar
• Stopher, P., FitzGerald, C., & Zhang, J. (2008). Search for a global positioning system device to measure person travel. Transportation Research Part C: Emerging Technologies, 16(3), 350–369. doi: 10.1016/j.trc.2007.10.002
   Web of Science ®Google Scholar
• Tsui, S. Y. A., & Shalaby, A. S. (2006). Enhanced system for link and mode identification for personal travel surveys based on global positioning systems. Transportation Research Record: Journal of the Transportation Research Board, 16(1972), 38–45. doi: 10.3141/1972-07
   Google Scholar
• Van Hulse, J., Khoshgoftaar, T. M., & Napolitano, A. (2007). Experimental perspectives on learning from imbalanced data. Proceedings of the 24th international conference on machine learning, Corvallis, OR, pp. 935–942.
   Google Scholar
• Wang, S., Chen, C., & Ma, J. (2010). Accelerometer based transportation mode recognition on mobile phones. Proceedings paper - APWCS '10 Proceedings of the 2010 Asia-Pacific Conference on Wearable Computing Systems, pp. 44–46.
   Google Scholar
• Weiser, M., & Brown, J. S. (1996). Designing calm technology. PowerGrid Journal, 1(1), 75–85.
   Google Scholar
• Weiser, M., & Brown, J. S. (1997). The coming age of calm technology. In Beyond calculation (pp. 75–85). New York, NY: Copernicus.
   Google Scholar
• Wolf, J. (2000). Using GPS data loggers to replace travel diaries in the collection of travel data (Dissertation). Georgia Institute of Technology, School of Civil and Environmental Engineering.
   Google Scholar
• Wolf, J., Oliveira, M., & Thompson, M. (2003). The impact of trip underreporting on VMT and travel time estimates: Preliminary findings from the California statewide household travel survey GPS study. Transportation Research Record, 1854, 189–198. doi: 10.3141/1854-21
   Google Scholar
• Xie, K., Deng, K., & Zhou, X. (2009). From trajectories to activities: A spatio-temporal join approach. Proceedings of the 2009 international workshop on location based social networks, Seattle, WA, pp. 25–32. ACM.
   Google Scholar
• Xu, H., & Mannor, S. (2012). Robustness and generalization. Machine Learning, 86(3), 391–423. doi: 10.1007/s10994-011-5268-1
   Web of Science ®Google Scholar
• Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., & Aberer, K. (2011). SeMiTri: A framework for semantic annotation of heterogeneous trajectories. Proceedings of the 14th international conference on extending database technology, pp. 259–270. http://dl.acm.org/citation.cfm?id=2733015
   Google Scholar
• Yu, M. C., Lin, C. J., Yu, T., Chang, E. Y., & Wang, S. (2014). Big data small footprint: The design of a low-power classifier for detecting transportation modes. The Proceedings of the VLDB Endowment, 7, 1429–1440. doi: 10.14778/2733004.2733015
   Google Scholar
• Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178(13), 2751–2779. doi: 10.1016/j.ins.2008.02.012
   Web of Science ®Google Scholar
• Zheng, Y., Chen, Y., Li, Q., Xie, X., & Ma, W. Y. (2010). Understanding transportation modes based on GPS data for web applications. ACM Transactions on the Web, 4(1), 1:1–1:36. doi: 10.1145/1658373.1658374
   Web of Science ®Google Scholar
• Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008). Learning transportation mode from raw gps data for geographic applications on the web. Proceedings of the 17th international conference on World Wide Web, Beijing, China, pp. 247–256.
   Google Scholar
• Zheng, Y., Xie, X., & Ma, W. Y. (2009). GeoLife 2.0: A location-based social networking service. MDM 2009, IEEE Computer Society, Taipei, Taiwan.
   Google Scholar
• Zhu, X., Vondrick, C., Ramanan, D., & Fowlkes, C. (2012). Do we need more training data or better models for object detection? Proceedings of the 23rd British Machine Vision Conference (BMVC), Guildford, UK, pp. 1–11.
   Google Scholar
• Zimowski, M., Tourangeau, R., Ghadialy, R., & Pedlow, S. (1997). Nonresponse in household travel surveys. Report to the Federal Highway Administration.
   Google Scholar