2000–2001 California Statewide Household Travel Survey. Final Report. NuStats, Austin, Tex (2002).
2010-2012 California Household Travel Survey. Final Report Version 1.0. NuStats, Austin, Tex, (2013).
2010–2012 Minneapolis – St. Paul Travel Behavior Inventory. Twin Cities Metropolitan Council, (2012).
2011 Atlanta, Georgia, Regional Travel Survey. Final Report. NuStats, Austin, Tex, (2011).
2012–2013 Delaware Valley Household Travel Survey. Delaware Valley Regional Planning Commission, (2013).
2014 Southern Nevada Household Travel Survey. Final Report. Westat, Rockville, Md, (2015).
2017 Puget Sound Regional Travel Study. Draft Final Report. RSG, (2017).
Abilene Urban Transportation Study. Summary Report: 2010-11 Regional Household Activity/Travel Survey. ETC Institute, (2011a).
Airsage. https://www.airsage.com/, (2020).
Axhausen, K. W., Schönfelder, S., Wolf, J., Oliveira, M., & Samaga, U.. Eighty weeks of GPS-traces: approaches to enriching the trip information. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., (2003).
Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M. and Puchinger, J. Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transportation Research Part C: Emerging Technologies, 101, pp.254-275. (2019).
Battelle. Global Positioning Systems for Personal Travel Surveys: Lexington Area Travel Data Collection Test. Final Report. FHWA, U.S. Department of Transportation, (1997).
Bengio, Y.. Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127, (2009).
Birant, D., & Kut, A.. ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering. 60(1), 208-221, (2007).
Bohte, W., & Maat, K..nDeriving 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, (2009).
Breiman, L.. Bagging predictors. Machine learning, 24(2), 123-140, (1996).
Breyer, N., Gundlegård, D. and Rydergren, C.. Travel mode classification of intercity trips using cellular network data. Transportation Research Procedia, 52, pp.211-218. (2021).
Broach, Joseph, Jennifer Dill, and Nathan Winslow McNeil. Travel mode imputation using GPS and accelerometer data from a multi-day travel survey. Journal of Transport Geography 78: 194-204, (2019).
Brunauer, R., Hufnagl, M., Rehrl, K., & Wagner, A.. Motion pattern analysis enabling accurate travel mode detection from GPS data only. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) pp. 404-411. IEEE, (2013).
Burkhard, O., Becker, H., Weibel, R. and Axhausen, K.W.. On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data. Transportation Research Part C: Emerging Technologies, 114, pp.99-117. (2020).
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P.. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357, (2002).
Chen, W., Ji, M., & Wang, J.. T-DBSCAN: A spatiotemporal density clustering for GPS trajectory segmentation. International Journal of Online Engineering (iJOE). 10(6), 19-24, (2014).
Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y.. Xgboost: extreme gradient boosting. R package version 0.4-2, 1-4, (2015).
Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M.. The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies. 68, 285-299, (2016a).
Chen, Tianqi, and Carlos Guestrin. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (2016b).
Chicago Regional Household Travel Inventory. Draft Final Report. NuStats, Austin, Tex., and GeoStats, Atlanta, Ga, (2007).
Chu, X.. A guidebook for using automatic passenger counter data for national transit database (NTD) reporting (No. NCTR778-03, FDOT BDK85 977-04). National Center for Transit Research (US) (2010).
Cortes, C., & Vapnik, V.. Support-vector networks. Machine learning, 20(3), 273-297, (1995).
Cui, Z., Ke, R., Pu, Z., & Wang, Y.. Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143, (2018).
Dabiri, S. and Heaslip, K.. Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation research part C: emerging technologies, 86, pp.360-371. (2018).
Du, J., & Aultman-Hall, L.. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues. Transportation Research Part A: Policy and Practice, 41(3), 220-232, (2007).
Eagle, N., M. Macy and R. Claxton. Network Diversity and Economic Development. Science Vol. 328, No. 5981, pp. 1029-1031. (2010).
El Paso Urban Transportation Study. Summary Report: 2010-11 Regional Household Activity/Travel Survey. ETC Institute, (2011b).
Ester, M., Kriegel, H. P., Sander, J., & Xu, X.. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd. Vol. 96, No. 34, pp. 226-231, (1996).
Fekih, M., Bellemans, T., Smoreda, Z., Bonnel, P., Furno, A. and Galland, S.. A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France). Transportation, pp.1-32. (2020).
Frias-Martinez, V., J. Virseda, A. Rubio and E. Frias-Martinez. Towards Large Scale Technology Impact Analyses: Automatic Residential Localization from Mobile Phone-Call Data. Proceedings of the 4th ACM/IEEE international conference on information and communication technologies and development, ACM. (2010).
Gong, H., Chen, C., Bialostozky, E., & Lawson, C. T.. A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 2012. 36(2), 131-139, (2012).
Gong, L., Morikawa, T., Yamamoto, T., & Sato, H.. Deriving personal trip data from GPS data: A literature review on the existing methodologies. Procedia-Social and Behavioral Sciences. 138, 557-565, (2014).
Gong, L., Sato, H., Yamamoto, T., Miwa, T., & Morikawa, T.. Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines. Journal of Modern Transportation. 23(3), 202-213, (2015).
Gong, L., Yamamoto, T., & Morikawa, T.. Identification of activity stop locations in GPS trajectories by DBSCAN-TE method combined with support vector machines. Transportation Research Procedia. 32, 146-154, (2018).
Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L.. Understanding individual human mobility patterns. Nature, 453(7196), 779-782, (2008).
Haghani, Ali, Masoud Hamedi, and Kaveh Farokhi Sadabadi. I-95 Corridor coalition vehicle probe project: Validation of INRIX data. I-95 Corridor Coalition 9, (2009).
Hecht-Nielsen, R.. Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press, (1992).
HERE. https://www.here.com/, (2020)
Highway Performance Monitoring System, Federal Higway Administration. https://www.fhwa.dot.gov/policyinformation/hpms.cfm, (2020).
Horak, Ray. Telecommunications and data communications handbook. John Wiley & Sons, (2007).
Houston-Galveston Area Council of Governments. Draft Summary Report: 2008-09 Regional Household Activity/Travel Survey. ETC Institute, (2009).
Hu, Patricia S., and Timothy R. Reuscher. Summary of travel trends: 2001 national household travel survey. (2004).
Huang, H., Cheng, Y. and Weibel, R.. Transport mode detection based on mobile phone network data: A systematic review. Transportation Research Part C: Emerging Technologies, 101, pp.297-312. (2019).
INRIX Traffic. http://www.inrix.com/, (2020).
In-The-Moment Travel Study. Revised Report. RSG, (2015a).
Jenks, G. F.. The data model concept in statistical mapping. International yearbook of cartography, 7, 186-190, (1967).
Kang, C., Liu, Y., Ma, X., & Wu, L.. Towards estimating urban population distributions from mobile call data. Journal of Urban Technology, 19(4), 3-21, (2012a).
Kang, C., Ma, X., Tong, D., & Liu, Y.. Intra-urban human mobility patterns: An urban morphology perspective. Physica A: Statistical Mechanics and its Applications, 391(4), 1702-1717, (2012b).
Kansas City Regional Travel Survey. Final Report. NuStats, Austin, Tex, (2004).
Landmark, A.D., Arnesen, P., Södersten, C.J. and Hjelkrem, O.A.. Mobile phone data in transportation research: methods for benchmarking against other data sources. Transportation, pp.1-23. (2021).
Lapham, Susan J. 1995 American Travel Survey: An Overview of the Survey Design and Methodology. (1995).
Liaw, A., & Wiener, M.. Classification and regression by randomForest. R news, 2(3), 18-22, (2002).
McGowen, P., & McNally, M.. Evaluating the potential to predict activity types from GPS and GIS data. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., (2007).
Michael Kearns and Leslie G. Valiant. Learning Boolean formulae or finite automata isas hard as factoring. Technical Report TR-14-88, Harvard University Aiken Computation Laboratory, August (1988).
Michael Kearns and Leslie G. Valiant. Cryptographic limitations on learning Boolean formu-lae and finite automata. Journal of the Association for Computing Machinery, 41(1):67–95, (1994)
Mid-Region Council of Governments 2013 Household Travel Survey. Final Report. Westat, Rockville, Md, (2014).
National Capital Region Transportation Planning Board, Metropolitan Washington Council of Governments. 2007/2008 TPB Household Travel Survey Technical Documentation, (2010).
Nguyen, M. H., and Armoogum, J.. Hierarchical process of travel mode imputation from GPS data in a motorcycle-dependent area. Travel behaviour and society, 21, 109-120 (2020).\
Nitsche, P., Widhalm, P., Breuss, S., Brändle, N., & Maurer, P.. Supporting large-scale travel surveys with smartphones–A practical approach. Transportation Research Part C: Emerging Technologies, 43, 212-221. (2014).
Ojah, M. and Pearson, D. F.. 2006 Austin/San Antonio GPS-Enhanced Household Travel Survey. Technical Summary. Texas Department of Transportation, (2008).
Osuna, E., Freund, R., & Girosit, F.. Training support vector machines: an application to face detection. In Proceedings of IEEE computer society conference on computer vision and pattern recognition (pp. 130-136). IEEE, (1997).
Pan, Y., Darzi, A., Kabiri, A., Zhao, G., Luo, W., Xiong, C., and Zhang, L.. Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States. Scientific Reports, 10(1), 1-9 (2020).
Pappalardo, L., F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti and A.-L. Barabási. Returners and Explorers Dichotomy in Human Mobility. Nature communications. Vol. 6,pp. 8166. (2015).
Patterson, Z., & Fitzsimmons, K.. Datamobile: Smartphone travel survey experiment. Transportation Research Record: Journal of the Transportation Research Board. 2594(1), 35-43, (2016).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J.. Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830, (2011).
Peterson, L. E.. K-nearest neighbor. Scholarpedia, 4(2), 1883, (2009).
Puget Sound Regional Travel Study. Report: Spring 2014 Household Travel Survey. RSG, (2014).
Puget Sound Regional Travel Study. Report: 2015 Household Travel Survey. RSG, (2015b).
Quinlan, J. R.. Induction of decision trees. Machine learning, 1(1), 81-106, (1986).
Safi, H., Assemi, B., Mesbah, M., Fereira, L., and Hickman, M.. Design and implementation of a smartphone-based system for personal travel survey: Case study from New Zealand. Transportation Research Record: Journal of the Transportation Research Board. vol. 2526, pp. 99–107, (2015).
Schmidhuber, J.. Deep learning in neural networks: An overview. Neural networks, 61, 85-117, (2015).
Schönfelder, Stefan, et al. Exploring the potentials of automatically collected GPS data for travel behaviour analysis: A Swedish data source. Arbeitsberichte Verkehrs-und Raumplanung 124 (2002).
Schrank, D., Eisele, B., & Lomax, T.. 2014 Urban mobility report: powered by Inrix Traffic Data (No. SWUTC/15/161302-1), (2015).
Schuessler, N., & Axhausen, K. W.. Processing raw data from global positioning systems without additional information. Transportation Research Record: Journal of the Transportation Research Board. 2105(1), 28-36, (2009).
Shafique, M. A., & Hato, E.. Travel mode detection with varying smartphone data collection frequencies. Sensors, 16(5), 716, (2016).
Song, C., T. Koren, P. Wang and A.-L. Barabási. Modelling the Scaling Properties of Human Mobility. Nature Physics. Vol. 6, No. 10, pp. 818. (2010a).
Song, C., Z. Qu, N. Blumm and A.-L. Barabási. Limits of Predictability in Human Mobility. Science. Vol. 327, No. 5968, pp. 1018-102. (2010b).
Soto, V., V. Frias-Martinez, J. Virseda and E. Frias-Martinez. Prediction of Socioeconomic Levels Using Cell Phone Records. International Conference on User Modeling, Adaptation, and Personalization, Springer. (2010).
Stenneth, Leon, et al. Transportation mode detection using mobile phones and GIS information. Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. (2011).
Stopher, P. R., Jiang, Q., & FitzGerald, C.. Processing GPS data from travel surveys. 2nd international colloqium on the behavioural foundations of integrated land-use and transportation models: frameworks, models and applications. Toronto, (2005).
Stopher, P., FitzGerald, C., & Xu, M.. Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation. 34(6), 723-741 (2007).
Stopher, P., FitzGerald, C., & Zhang, J.. Search for a global positioning system device to measure person travel. Transportation Research Part C: Emerging Technologies. 16(3), 350-369, (2008).
Suykens, J. A., & Vandewalle, J.. Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300, (1999).
Tsui, S. Y. A., & Shalaby, A. S.. 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. 1972(1), 38-45, (2006).
U.S. Department of Transportation, Federal Highway Administration, 2017 National Household Travel Survey. Retrieved from: http://nhts.ornl.gov. (2017)
U.S. DOT Bureau of Transportation Statistics National Transit Map. https://www.bts.gov/content/national-transit-map, (2020).
U.S. Department of Transportation, Bureau of Transportation Statistics, Transportation Statistics Annual Report 2020. Washington, DC. https://doi.org/10.21949/1520449. (2020).
Wang, L. (Ed.).. Support vector machines: theory and applications (Vol. 177). Springer Science & Business Media, (2005).
Wang, B., Gao, L., & Juan, Z.. Travel mode detection using GPS data and socioeconomic attributes based on a random forest classifier. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1547-1558, (2017).
Wang, F., & Chen, C.. On data processing required to derive mobility patterns from passively-generated mobile phone data. Transportation Research Part C: Emerging Technologies. 87, 58-74, (2018).
Wang, F., Wang, J., Cao, J., Chen, C., & Ban, X. J.. Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example. Transportation Research Part C: Emerging Technologies. 105, 183-202, (2019).
Wichita Falls Urban Transportation Study. Summary Report: 2010-11 Regional Household Activity/Travel Survey. ETC Institute, (2011c).
Wolf, J., Guensler, R., & Bachman, W.. Elimination of the travel diary: Experiment to derive trip purpose from global positioning system travel data. Transportation Research Record: Journal of the Transportation Research Board. 1768(1), 125-134 (2001).
Wolf, J.. Applications of new technologies in travel surveys. Travel survey methods: Quality and future directions. pp. 531-544. Emerald Group Publishing Limited (2006).
Wolf, J., and M. Lee. Synthesis of and Statistics for Recent GPS-Enhanced Travel Surveys. Proc., International Conference on Survey Methods in Transport: Harmonization and Data Comparability, International Steering Committee for Travel Survey Conferences. Annecy, France (2008).
Xiao, G., Juan, Z., and Zhang, C.. Travel mode detection based on GPS track data and Bayesian networks. Computers, Environment and Urban Systems 54: 14-22, (2015).
Xiong, C., Shahabi, M., Zhao, J., Yin, Y., Zhou, X., and Zhang, L.. An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems. Transportation Research Part C: Emerging Technologies (2019).
Xiong, C., Hu, S., Yang, M., Luo, W., and Zhang, L.. Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections. Proceedings of the National Academy of Sciences, 117(44), 27087-27089 (2020a).
Xiong, C., Hu, S., Yang, M., Younes, H., Luo, W., Ghader, S. and Zhang, L.. Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA. Journal of the Royal Society Interface, 17(173), p.20200344. (2020b).
Yao, Z., Zhou, J., Jin, P. J., & Yang, F.. Trip End Identification based on Spatial-Temporal Clustering Algorithm using Smartphone GPS Data (No. 19-01097), Presented at 98th Annual Meeting of the Transportation Research Board, Washington, D.C., (2019).
Ye, Y., Zheng, Y., Chen, Y., Feng, J., & Xie, X.. Mining individual life pattern based on location history. 2009 tenth international conference on mobile data management: Systems, services and middleware. pp. 1-10, (2009).
Zhang, L., and K. Viswanathan. The on-line travel survey manual: A dynamic document for transportation professionals. Transportation Research Board, viewed 17, (2013).
Zhang, L., Sepehr G., Michael L. P., Chenfeng X., Aref D., Mofeng Y., Qianqian S., AliAkbar K., and Songhua H.. An interactive COVID-19 mobility impact and social distancing analysis platform. medRxiv (2020).
Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., & Terveen, L.. Discovering personally meaningful places: An interactive clustering approach. ACM Transactions on Information Systems (TOIS). 25(3), 12, (2007).
Zhou, C., Jia, H., Juan, Z., Fu, X., & Xiao, G.. A data-driven method for trip ends identification using large-scale smartphone-based GPS tracking data. IEEE Transactions on Intelligent Transportation Systems. 18(8), 2096-2110, (2016).
Çolak, S., A. Lima and M. C. González. Understanding Congested Travel in Urban Areas. Nature communications. Vol. 7, pp. 10793. (2016).