1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warde P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Software available from tensorflow.org. Accessed Jan 2019
2. Bantis T, Haworth J (2017) Who you are is how you travel: a framework for transportation mode detection using individual and environmental characteristics. Transp Res Part C Emerg Technol 80:286–309
3. Beaulieu A, Farooq B (2019) A dynamic mixed logit model with agent effect for pedestrian next location choice using ubiquitous Wi-Fi network data. Int J Transp Sci Technol 8(3):280–289
4. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory, pp 92–100
5. Chen C, Gong H, Lawson C, Bialostozky E (2010) Evaluating the feasibility of a passive travel survey collection in a complex urban environment: lessons learned from the New York city case study. Transp Res Part A Policy Pract 44(10):830–840