Abbruzzo, A., Ferrante, M., & Cantis, S. D. (2021). A pre-processing and network analysis of GPS tracking data. Spatial Economic Analysis, 16(2), 217–240.
Google Scholar
Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832–843.
MATH
Google Scholar
Allström, A., Kristoffersson, I., & Susilo, Y. (2017). Smartphone based based travel diary collection: Experiences from a field trial in Stockholm. Transportation Research Procedia, 26, 32–38.
Google Scholar
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. In GIS: Proceedings of the ACM international symposium on advances in geographic information systems.
Anderson, P., Hepworth, M., Kelly, B., & Metcalfe, R. (2007). What is Web 2.0 ? Ideas, technologies and implications for education by. Technology, 60(1), 64.
Google Scholar
Apple. (2016). Apple developers support resolution on network signal strength access. Retrieved January 1, 2019, from web.
Apple. (2019). Preventing unexpected shutdowns. Retrieved January 1, 2020, from web.
Apple. (2021). Car data integration on smartphones. Retrieved March 17, 2021, from web.
Aslak, U. (2019). Infostop, a Python package for detecting stop locations in mobility data. Retrieved November 26, 2019, from web.
Assemi, B., Jafarzadeh, H., Mesbah, M., & Hickman, M. (2018). Participants’ perceptions of smartphone travel surveys. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 338–348.
Google Scholar
Assemi, B., Safi, H., Mesbah, M., & Ferreira, L. (2016). Developing and validating a statistical model for travel mode identification on smartphones. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1920–1931.
Google Scholar
Auld, J., Williams, C., Mohammadian, A., & Nelson, P. (2009). An automated GPS-based prompted recall survey with learning algorithms. Transportation Letters, 1, 59–79.
Google Scholar
Baker, R. P., Bradburn, N. M., & Johnson, R. A. (1995). Computer-assisted personal interviewing: An experimental evaluation of data quality and cost. Journal of Official Statistics, 11(4), 413–431.
Google Scholar
Balaprakash, P., Salim, M., Uram, T. D., Vishwanath, V., & Wild, S. M. (2019). DeepHyper: Asynchronous hyperparameter search for deep neural networks. In Proceedings—25th IEEE international conference on high performance computing, HiPC 2018 (pp. 42–51).
Barandela, R., & Gasca, E. (2000). Decontamination of training samples for supervised pattern recognition methods. In F. J. Ferri, J. M. Iñesta, A. Amin, & P. Pudil (Eds.), Advances in pattern recognition (pp. 621–630). Springer.
MATH
Google Scholar
Beigman, E., & Klebanov, B. B. (2009). Learning with annotation noise. In Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: Volume 1, ACL ’09 (Vol. 1, pp. 280–287). Association for Computational Linguistics.
Bellman, R. (1957). Dynamic programming. Princeton University Press.
MATH
Google Scholar
Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. The MIT Press.
Google Scholar
Bierlaire, M., Chen, J., & Newman, J. (2013). A probabilistic map matching method for smartphone GPS data. Transportation Research Part C: Emerging Technologies, 26, 78–98.
Google Scholar
Blum, J. R., Greencorn, D. G., & Cooperstock, J. R. (2013). Smartphone sensor reliability for augmented reality applications. In K. Zheng, M. Li, & H. Jiang (Eds.), Mobile and ubiquitous systems: Computing, networking, and services (pp. 127–138). Springer.
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.
Google Scholar
Byon, Y. J., & Liang, S. (2014). Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18, 264–272.
Google Scholar
Calastri, C., Dit Sourd, R. C., & Hess, S. (2018). We want it all: Experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning. Transportation, 47, 175–201.
Google Scholar
Carpineti, C., Lomonaco, V., Bedogni, L., Felice, M. D., & Bononi, L. (2018). Custom dual transportation mode detection by smartphone devices exploiting sensor diversity. In Proceedings of the 14th workshop on context and activity modeling and recognition (IEEE COMOREA 2018).
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., del Millán, J. R., & Roggen, D. (2013). The opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 34(15), 2033–2042. Smart Approaches for Human Action Recognition.
Google Scholar
Chen, J., & Bierlaire, M. (2015). Probabilistic multimodal map matching with rich smartphone data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 19(2), 134–148.
Google Scholar
Christensen, L. (2013). The Role of Web Interviews as Part of a National Travel Survey. In J. Zmud, M. Lee-Gosselin, M. Munizaga, & J. A. Carrasco (Eds.), Transport Survey Methods (pp. 115–154). Emerald Group Publishing Limited. https://doi.org/10.1108/9781781902882-006.
Christiansen, H. (Author), & Warnecke, M-L. (Author). (2018). The Danish National Travel Survey - declaration of variables TU 2006-17, version 1. Dataset, DTU Management.
Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2017). A survey on activity detection and classification using wearable sensors. IEEE Sensors Journal, 17(2), 7742959.
Google Scholar
Cottrill, C., Pereira, F., Zhao, F., Dias, I., Lim, H., Ben-Akiva, M., & Zegras, P. (2013). Future mobility survey. Transportation Research Record: Journal of the Transportation Research Board, 2354, 59–67.
Google Scholar
Dabiri, S., & Heaslip, K. (2018). Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies, 86(November 2017), 360–371.
Google Scholar
Dabiri, S., Lu, C.-T., Heaslip, K., & Reddy, C. K. (2019). Semi-supervised deep learning approach for transportation mode identification using GPS trajectory data. IEEE Transactions on Knowledge and Data Engineering, 32, 1010–1023.
Google Scholar
Das, R. D., & Winter, S. (2016). Automated urban travel interpretation: A bottom-up approach for trajectory segmentation. Sensors (Switzerland), 16(11), 1962.
Google Scholar
Davidson, P., & Piché, R. (2017). A survey of selected indoor positioning methods for smartphones. IEEE Communications Surveys Tutorials, 19(2), 1347–1370.
Google Scholar
De Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the crowd: The privacy bounds of human mobility. Scientific Reports, 3, 1–5.
Google Scholar
Ectors, W., Reumers, S., Lee, W. D., Choi, K., Kochan, B., Janssens, D., Bellemans, T., & Wets, G. (2017). Developing an optimised activity type annotation method based on classification accuracy and entropy indices. Transportmetrica A: Transport Science, 13(8), 742–766.
Google Scholar
Ehsani, R., Buchanon, S., & Salyani, M. (2009). GPS Accuracy for Tree Scouting and Other Horticultural Uses. EDIS, 2009(2). Retrieved from https://journals.flvc.org/edis/article/view/117815.
Ek, A., Alexandrou, C., Delisle Nyström, C., Direito, A., Eriksson, U., Hammar, U., Henriksson, P., Maddison, R., Trolle Lagerros, Y., & Löf, M. (2018). The Smart City Active Mobile Phone Intervention (SCAMPI) study to promote physical activity through active transportation in healthy adults: A study protocol for a randomised controlled trial. BMC Public Health, 18, 1–11.
Google Scholar
Faouzi, N. E. E., Leung, H., & Kurian, A. (2011). Data fusion in intelligent transportation systems: Progress and challenges—A survey. Information Fusion, 12, 4–10.
Google Scholar
Feng, T., & Timmermans, H. J. (2015). Detecting activity type from GPS traces using spatial and temporal information. European Journal of Transport and Infrastructure Research, 15(4), 662–674.
Google Scholar
Gadziński, J. (2018). Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study. Transportation Research Part C: Emerging Technologies, 88(July 2017), 74–86.
Google Scholar
Garg, N. (2018). Mining bus stops from raw GPS data of bus trajectories. In 10th International conference on communication systems & networks (COMSNETS), Bengaluru, India (pp. 583–588). IEEE.
Geurs, K. T., Thomas, T., Bijlsma, M., & Douhou, S. (2015). Automatic trip and mode detection with move smarter: first results from the dutch mobile mobility panel. Transport Res Proc,. https://doi.org/10.1016/j.trpro.2015.12.022.
Article
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.
Google Scholar
Greaves, S., Ellison, A., Ellison, R., Rance, D., Standen, C., Rissel, C., & Crane, M. (2015). A web-based diary and companion smartphone app for travel/activity surveys. Transportation Research Procedia, 11, 297–310.
Google Scholar
Guidotti, R., Trasarti, R., & Nanni, M. (2015). TOSCA: Two-steps clustering algorithm for personal locations detection. In GIS: Proceedings of the ACM international symposium on advances in geographic information systems.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations Newsletter, 11(1), 10–18.
Google Scholar
Hariharan, R., & Toyama, K. (2004). Project lachesis: Parsing and modeling location histories. In M. J. Egenhofer, C. Freksa, & H. J. Miller (Eds.), Geographic information science (pp. 106–124). Springer.
Google Scholar
Hoseini-Tabatabaei, S. A., Gluhak, A., & Tafazolli, R. (2013). A survey on smartphone-based systems for opportunistic user context recognition. ACM Computing Surveys, 45(3), 1–51.
Google Scholar
Houston, D., Luong, T. T., & Boarnet, M. G. (2014). Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns. Transportation Research Part C: Emerging Technologies, 48, 97–108.
Google Scholar
Huang, J., Qiao, S., Yu, H., Qie, J., & Liu, C. (2014). Parallel map matching on massive vehicle GPS data using MapReduce. In Proceedings—2013 IEEE international conference on high performance computing and communications, HPCC 2013 and 2013 IEEE international conference on embedded and ubiquitous computing, EUC 2013 (pp. 1498–1503).
Hunter, T., Abbeel, P., & Bayen, A. (2014). The path inference filter: Model-based low-latency map matching of probe vehicle data. IEEE Transactions on Intelligent Transportation Systems, 15(2), 507–529.
Google Scholar
Iqbal, M. S., Choudhury, C. F., Wang, P., & González, M. C. (2014). Development of origin-destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40, 63–74.
Google Scholar
Jagadeesh, G. R., & Srikanthan, T. (2017). Online map-matching of noisy and sparse location data with hidden Markov and route choice models. IEEE Transactions on Intelligent Transportation Systems, 18, 2423–2434.
Google Scholar
Jahangiri, A., & Rakha, H. A. (2015). Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2406–2417.
Google Scholar
Jeon, K. E., She, J., Soonsawad, P., & Ng, P. C. (2018). BLE beacons for internet of things applications: Survey, challenges, and opportunities. IEEE Internet of Things Journal, 5(2), 811–828.
Google Scholar
Jiang, X., de Souza, E. N., Pesaranghader, A., Hu, B., Silver, D. L., & Matwin, S. (2017). TrajectoryNet: An embedded GPS trajectory representation for point-based classification using recurrent neural networks. Source code published on Github@https://github.com/wuhaotju/TrajectoryNet. Retrieved November 1, 2019, from web.
Kanarachos, S., Christopoulos, S. R. G., & Chroneos, A. (2018). Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity. Transportation Research Part C: Emerging Technologies, 95(March), 867–882.
Google Scholar
Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399.
Google Scholar
Kim, Y., Pereira, F. C., Zegras, P. C., & Ben-akiva, M. (2018). Activity recognition for a smartphone and web-based human mobility sensing system. IEEE Intelligent Systems, 33(August), 5–23.
Google Scholar
Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., & Laurila, J. (2010). Towards rich mobile phone datasets: Lausanne data collection campaign. Proc. ICPS, Berlin, 68, 7.
Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behaviour research: A review. Transport Reviews, 40, 1–24.
Google Scholar
Kubicka, M., Cela, A., Moulin, P., Mounier, H., & Niculescu, S. I. (2016). Dataset for testing and training map-matching methods [Data set]. 2015 IEEE Intelligent Vehicles Symposium (IV 2015), Seoul, South Korea. Zenodo. https://doi.org/10.5281/zenodo.57731.
Kubicka, M., Cela, A., Mounier, H., & Niculescu, S. I. (2018). Comparative study and application-oriented classification of vehicular map-matching methods. IEEE Intelligent Transportation Systems Magazine, 10(2), 150–166.
Google Scholar
Laurila, J. K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T. M. T., Dousse, O., Eberle, J., & Miettinen, M. (2013). From big smartphone data to worldwide research: The mobile data challenge. Pervasive and Mobile Computing, 9(6), 752–771.
Google Scholar
Li, C., Zegras, P. C., Zhao, F., Qin, Z., Shahid, A., Ben-Akiva, M., Pereira, F., & Zhao, J. (2017). Enabling bus transit service quality co-monitoring through smartphone-based platform. Transportation Research Record: Journal of the Transportation Research Board, 2649(1), 42–51.
Google Scholar
Li, H., & Wu, G. (2014). Map matching for taxi GPS data with extreme learning machine (Vol. 8933). Springer.
Google Scholar
Li, L., Quddus, M., & Zhao, L. (2013). High accuracy tightly-coupled integrity monitoring algorithm for map-matching. Transportation Research Part C: Emerging Technologies, 36, 13–26.
Google Scholar
Li, X., Zhang, X., Chen, K., & Feng, S. (2014). Measurement and analysis of energy consumption on android smartphones. In 2014 4th IEEE International conference on information science and technology (pp. 242–245).
Liao, L., Fox, D., & Kautz, H. (2007). Extracting places and activities from GPS traces using hierarchical conditional random fields. International Journal of Robotics Research, 26, 119–134.
Google Scholar
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., & Huang, Y. (2009). Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems—GIS ’09, (c) (p. 352).
Mäenpää, H., Lobov, A., & Martinez Lastra, J. L. (2017). Travel mode estimation for multi-modal journey planner. Transportation Research Part C: Emerging Technologies, 82, 273–289.
Google Scholar
Teng, C. M. (2001, May). A Comparison of Noise Handling Techniques. In Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (pp. 269-273).
Manwani, N., & Sastry, P. S. (2013). Noise tolerance under risk minimization. IEEE Transactions on Cybernetics, 43(3), 1146–1151.
Google Scholar
Martin, B. D., Addona, V., Wolfson, J., Adomavicius, G., & Fan, Y. (2017). Methods for real-time prediction of the mode of travel using smartphone-based GPS and accelerometer data. Sensors (Switzerland), 17(9), 2058.
Google Scholar
Montini, L., Rieser-Schüssler, N., Horni, A., & Axhausen, K. (2014). Trip purpose identification from GPS tracks. Transportation Research Record: Journal of the Transportation Research Board, 2405, 16–23.
Google Scholar
Nettleton, D. F., Orriols-Puig, A., & Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review, 33(4), 275–306.
Google Scholar
Newson, P., & Krumm, J. (2009). Hidden Markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems—GIS ’09 (pp. 336–343).
Nicholls, L., II., & Groves, R. M. (1986). The status of computer-assisted telephone interviewing: Part I—Introduction and impact on cost and timeliness of survey data. Journal of Official Statistics, 2(2), 93.
Google Scholar
Nitsche, P., Widhalm, P., Breuss, S., Brändle, N., & Maurer, P. (2014). Supporting large-scale travel surveys with smartphones—A practical approach. Transportation Research Part C: Emerging Technologies, 43, 212–221.
Google Scholar
Nurmi, P., & Koolwaaij, J. (2006). Identifying meaningful locations. In 2006 3rd Annual international conference on mobile and ubiquitous systems: Networking and services, MobiQuitous.
Oshin, T. O., Poslad, S., & Ma, A. (2012). Improving the energy-efficiency of GPS based location sensing smartphone applications. In Proceedings of the 11th IEEE international conference on trust, security and privacy in computing and communications, TrustCom-2012—11th IEEE international conference on ubiquitous computing and communications, IUCC-2012 (pp. 1698–1705).
Patterson, Z., & Fitzsimmons, K. (2016). Datamobile: Smartphone travel survey experiment. Transportation Research Record, 2594, 35–53.
Google Scholar
Patterson, Z., Fitzsimmons, K., Jackson, S., & Mukai, T. (2019). Itinerum: The open smartphone travel survey platform. SoftwareX, 10, 100230.
Google Scholar
Perrucci, G. P., Fitzek, F. H. P., & Widmer, J. (2011). Survey on energy consumption entities on the smartphone platform. In 2011 IEEE 73rd Vehicular technology conference (VTC Spring) (pp. 1–6).
Prelipcean, A. C., Gidofalvi, G., & Susilo, Y. O. (2016). Measures of transport mode segmentation of trajectories. International Journal of Geographical Information Science, 30(9), 1763–1784.
Google Scholar
Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2018). MEILI: A travel diary collection, annotation and automation system. Computers, Environment and Urban Systems, 70, 24–34.
Google Scholar
Primault, V., Boutet, A., Mokhtar, S. B., & Brunie, L. (2019). The long road to computational location privacy: A survey. IEEE Communications Surveys and Tutorials, 21(3), 8482357, 2772–2793.
Google Scholar
Quddus, M. A., Noland, R. B., & Ochieng, W. Y. (2006). A high accuracy fuzzy logic based map matching algorithm for road transport. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 10(3), 103–115.
MATH
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.
Google Scholar
Renso, C., Baglioni, M., de Macedo, J. A. F., Trasarti, R., & Wachowicz, M. (2013). How you move reveals who you are: Understanding human behavior by analyzing trajectory data. Knowledge and Information Systems, 37(2), 331–362.
Google Scholar
Rolnick, D., Veit, A., Belongie, S., & Shavit, N. (2018). Deep learning is robust to massive label noise. Retrieved November 14, 2019, from the arXiv database.
Rosvall, M., Axelsson, D., & Bergstrom, C. T. (2009). The map equation. European Physical Journal Special Topics, 178(1), 13–23.
Google Scholar
Schuessler, N., & Axhausen, K. W. (2009). Processing raw data from global positioning systems without additional information. Transportation Research Record, 2105(1), 28–36.
Google Scholar
Seidl, D. E., Jankowski, P., & Tsou, M. H. (2016). Privacy and spatial pattern preservation in masked GPS trajectory data. International Journal of Geographical Information Science, 30(4), 785–800.
Google Scholar
Semanjski, I., Gautama, S., Ahas, R., & Witlox, F. (2017). Spatial context mining approach for transport mode recognition from mobile sensed big data. Computers, Environment and Urban Systems, 66, 38–52.
Google Scholar
Shankari, K., Fürst, J., Fadel Argerich, M., Avramidis, E., & Zhang, J. (2020). MobilityNet: Towards a Public Dataset for Multi-modal Mobility Research. ICLR 2020 Workshop on Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/iclr2020/15.html.
Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS data-processing methods, Transport Reviews, 34:3, 316-334. https://doi.org/10.1080/01441647.2014.903530.
Sicotte, G., Morency, C., & Farooq, B. (2017). Comparison between trip and trip chain models: Evidence from Montreal commuter train corridor (No. CIRRELT-2017-35). CIRRELT, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport = Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation.
Silver, D. L., Yang, Q., & Li, L. (2013). Lifelong machine learning systems: Beyond learning algorithms. In 2013 AAAI spring symposium series. Citeseer.
Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381.
Google Scholar
Stopher, P. R., Shen, L., Liu, W., & Ahmed, A. (2015). The challenge of obtaining ground truth for GPS processing. Transportation Research Procedia, 11, 206–217. Transport Survey Methods: Embracing Behavioural and Technological Changes Selected contributions from the 10th International Conference on Transport Survey Methods 16–21 November 2014, Leura, Australia.
Thierry, B., Chaix, B., & Kestens, Y. (2013). Detecting activity locations from raw GPS data: A novel kernel-based algorithm. International Journal of Health Geographics, 12, 1–10.
Google Scholar
Thomas, T., Geurs, K. T., Koolwaaij, J., & Bijlsma, M. (2018). Automatic trip detection with the dutch mobile mobility panel: Towards reliable multiple-week trip registration for large samples. Journal of Urban Technology, 25, 1–19.
Google Scholar
Tietbohl, A., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008). A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the ACM symposium on applied computing.
Torre, F., Pitchford, D., Brown, P., & Terveen, L. (2012). Matching GPS traces to (possibly) incomplete map data. In Proceedings of the 20th international conference on advances in geographic information systems—SIGSPATIAL ’12 (p. 546).
Van Dijk, J. (2018). Identifying activity-travel points from GPS-data with multiple moving windows. Computers, Environment and Urban Systems, 70(September 2017), 84–101.
Google Scholar
Velasco-Gallego, C., & Lazakis, I. (2020). Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study. Ocean Engineering, 218, 108261.
Google Scholar
von Watzdorf, S., & Michahelles, F. (2010). Accuracy of positioning data on smartphones. In Proceedings of the 3rd international workshop on location and the web, LocWeb ’10, New York. Association for Computing Machinery.
Vuk, G., Bowman, J. L., Daly, A., & Hess, S. (2016). Impact of family in-home quality time on person travel demand. Transportation, 43(4), 705–724.
Google Scholar
Wang, D., Zhang, J., Cao, W., Li, J., & Zheng, Y. (2018). When will you arrive? Estimating travel time based on deep neural networks. In IJCAI.
Wang, L., Gjoreski, H., Ciliberto, M., Mekki, S., Valentin, S., & Roggen, D. (2019). Enabling reproducible research in sensor-based transportation mode recognition with the Sussex–Huawei dataset. IEEE Access, 7, 10870–10891.
Google Scholar
Wang, L., Jiao, L., Li, J., Gedeon, J., & Mühlhäuser, M. (2019). Moera: Mobility-agnostic online resource allocation for edge computing. IEEE Transactions on Mobile Computing, 18(8), 1843–1856.
Google Scholar
Wee, B. V., & Banister, D. (2016). How to write a literature review paper? Transport Reviews, 36(2), 278–288.
Google Scholar
Wei, H., Wang, Y., Forman, G., & Zhu, Y. (2013). Map matching: Comparison of approaches using sparse and noisy data. In Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL’13, New York (pp. 444–447). Association for Computing Machinery.
Wu, H., Chen, Z., Sun, W., Zheng, B., & Wang, W. (2017). Modeling trajectories with recurrent neural networks. In IJCAI International joint conference on artificial intelligence (pp. 3083–3090).
Xiang, L., Gao, M., & Wu, T. (2016). Extracting stops from noisy trajectories: A sequence oriented clustering approach. ISPRS International Journal of Geo-Information, 5, 29.
Google Scholar
Xiao, G., Cheng, Q., & Zhang, C. (2019). Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models. International Journal of Distributed Sensor Networks, 15, 1550147719844156.
Google Scholar
Xiao, G., Juan, Z., & Zhang, C. (2015). Travel mode detection based on GPS track data and Bayesian networks. Computers, Environment and Urban Systems, 54, 14–22.
Google Scholar
Xiao, G., Juan, Z., & Zhang, C. (2016). Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization. Transportation Research Part C: Emerging Technologies, 71, 447–463.
Google Scholar
Xiao, L., Li, Y., Han, G., Dai, H., & Poor, H. V. (2018). A secure mobile crowdsensing game with deep reinforcement learning. IEEE Transactions on Information Forensics and Security, 13(1), 35–47.
Google Scholar
Yazdizadeh, A., Patterson, Z., & Farooq, B. (2019). An automated approach from GPS traces to complete trip information. International Journal of Transportation Science and Technology, 8, 82–100.
Google Scholar
Yazdizadeh, A., Patterson, Z., & Farooq, B. (2019). Ensemble convolutional neural networks for mode inference in smartphone travel survey. IEEE Transactions on Intelligent Transportation Systems, 21, 2232–2239.
Google Scholar
Kalatian, A., & Farooq, B. (2020). A semi-supervised deep residual network for mode detection in Wi-Fi signals. Journal of Big Data Analytics in Transportation, 2(2), 167-180.
Yurur, O., Liu, C. H., Sheng, Z., Leung, V. C. M., Moreno, W., & Leung, K. K. (2016). Context-awareness for mobile sensing: A survey and future directions. IEEE Communications Surveys and Tutorials, 18(1), 68–93.
Google Scholar
Zhao, F., Ghorpade, A., Pereira, F. C., Zegras, C., & Ben-Akiva, M. (2015a). Stop detection in smartphone-based travel surveys. Transportation Research Procedia, 11(2010), 218–226.
Google Scholar
Zhao, F., Pereira, F. C., Ball, R., Kim, Y., Han, Y., Zegras, C., & Ben-Akiva, M. (2015b). Exploratory analysis of a smartphone-based travel survey in Singapore. Transportation Research Record, 2494(1), 45–56.
Google Scholar
Zheng, Y. (2015). Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 1–41.
Google Scholar
Zheng, Y., & Fu, H. (2011). Geolife GPS trajectory dataset—User guide. Technical Report November 31. Online. Retrieved July 19, 2008.
Zheng, Y., Zhang, L., Xie, X., & Ma, W.-Y. (2009). Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on world wide web—WWW ’09.
Zhou, R., Li, M., Wang, H., Song, X., Xie, W., & Lu, Z. (2017). An enhanced transportation mode detection method based on GPS data. Communications in Computer and Information Science, 727, 605–620.
Google Scholar
Zhou, X., Yu, W., & Sullivan, W. C. (2016). Making pervasive sensing possible: Effective travel mode sensing based on smartphones. Computers, Environment and Urban Systems, 58, 52–59.
Google Scholar
Zhu, Q., Zhu, M., Li, M., Fu, M., Huang, Z., Gan, Q., & Zhou, Z. (2016). Identifying transportation modes from raw GPS data. In Communications in computer and information science.
Zhu, X., Li, J., Liu, Z., Wang, S., & Yang, F. (2016). Learning transportation annotated mobility profiles from GPS data for context-aware mobile services. In Proceedings—2016 IEEE international conference on services computing, SCC 2016 (pp. 475–482).
Zmud, J., Lee-Gosselin, M., Carrasco, J. A., & Munizaga, M. A. (2013). Transport survey methods: Best practice for decision making. Emerald Group Publishing.
Google Scholar