- Adnan M, Pereira FC, Azevedo CML, , et al, . (2016). SimMobility: A multi-scale integrated agent-based simulation platform. In: 95th Annual meeting of the transportation research board. Forthcoming in Transportation Research Board 95th Annual Meeting, Washington, United States. 10–14 Jan 2016.
Paper not yet in RePEc: Add citation now
- Balmer M, Meister K, Rieser M, , et al, . (2008) Agent-based simulation of travel demand: Structure and computational performance of MATSIM-T. Arbeitsberichte Verkehrs-und Raumplanung, 504. Structure.
Paper not yet in RePEc: Add citation now
- 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.
Paper not yet in RePEc: Add citation now
Bowman J, Moshe B-A, (2001) Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice 35(1): 1–20.
- Breiman L, (2001) Random forests. Machine Learning 45(1): 5–32.
Paper not yet in RePEc: Add citation now
Chen C, Gong H, Lawson C, , et al, . (2010) Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York city case study. Transportation Research Part A: Policy and Practice 44(10): 830–840.
Diao M, Zhu Y, Ferreira J Jr, , et al, . (2016) Inferring individual daily activities from mobile phone traces: A Boston example. Environment and Planning B: Planning and Design 43(5): 920–940.
- Ermagun A, Fan Y, Wolfson J, , et al, . (2017) Real-time trip purpose prediction using online location-based search and discovery services. Transportation Research Part C: Emerging Technologies 77: 96–112.
Paper not yet in RePEc: Add citation now
- Feng T, Timmermans HJ, (2015) Detecting activity type from GPS traces using spatial and temporal information. European Journal of Transport and Infrastructure Research 15(4): 662–674.
Paper not yet in RePEc: Add citation now
- Gong L, Morikawa T, Yamamoto T, , et al, . (2014) Deriving personal trip data from GPS data: A literature review on the existing methodologies. Procedia Social and Behavioral Sciences 138: 557–565.
Paper not yet in RePEc: Add citation now
- Habib KMN, (2007) Modeling activity generation processes. PhD Thesis, University of Toronto, Canada.
Paper not yet in RePEc: Add citation now
- Lafferty J, McCallum A, Pereira FC, (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, June 2001, pp. 282–289. Williams College, Massachusetts, USA.
Paper not yet in RePEc: Add citation now
- Liao L, Fox D, Kautz H, (2007) Extracting places and activities from GPS traces using hierarchical conditional random fields. The International Journal of Robotics Research 26(1): 119–134.
Paper not yet in RePEc: Add citation now
- Liaw A, Wiener M, (2002) Classification and regression by random forest. R News 2(3): 18–22.
Paper not yet in RePEc: Add citation now
- Montini L, Rieser-Schüssler N, Horni A, , et al, . (2014) Trip purpose identification from GPS tracks. Transportation Research Record: Journal of the Transportation Research Board 2405(1): 16–23.
Paper not yet in RePEc: Add citation now
- Nguyen MH, Armoogum J, Madre JL, , et al, . (2020) Reviewing trip purpose imputation in GPS-based travel surveys. Journal of Traffic and Transportation Engineering (English Edition) 7(4): 395–412.
Paper not yet in RePEc: Add citation now
- Nocedal J, Wright S, (2006) Numerical Optimization. Berlin: Springer Science & Business Media.
Paper not yet in RePEc: Add citation now
- Oliveira M, Vovsha P, Wolf J, , et al, . (2014) Evaluation of two methods for identifying trip purpose in GPS-based household travel surveys. Transportation Research Record: Journal of the Transportation Research Board 2405: 33–41.
Paper not yet in RePEc: Add citation now
- Pereira F, Carrion C, Zhao F, , et al, . (2013) The future mobility survey: Overview and preliminary evaluation. Proceedings of the Eastern Asia Society for Transportation Studies 9: 1–13.
Paper not yet in RePEc: Add citation now
- Rabiner L, (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2): 257–286.
Paper not yet in RePEc: Add citation now
- Salvini P, Miller EJ, (2005) ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics 5(2): 217–234.
Paper not yet in RePEc: Add citation now
- Song Y, Morency LP, Davis R, (2013) Action recognition by hierarchical sequence summarization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Juan, PR, USA, 17–19 June 1997, pp. 3562–3569. New York: ACM.
Paper not yet in RePEc: Add citation now
- 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.
Paper not yet in RePEc: Add citation now
- Sutton C, McCallum A, (2006) An introduction to conditional random fields for relational learning. Introduction to Statistical Relational Learning. Vol. 2. Cambridge: MIT Press.
Paper not yet in RePEc: Add citation now
- Trevor Hastie (2020) Ridge regularization: An essential concept in data science. Technometrics 62(4): 426–433.
Paper not yet in RePEc: Add citation now
- Vail DL, Lafferty JD, Veloso MM, (2007) Feature selection in conditional random fields for activity recognition. In: IEEE/RSJ international conference on intelligent robots and systems, San Diego, CA, USA, Oct . 2007, pp. 3379–3384. New York: IEEE.
Paper not yet in RePEc: Add citation now
- Van Der Maaten L, Welling M, Saul L, (2011). Hidden-unit conditional random fields. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, Fort Lauderdale, FL, USA, April 2011, pp. 479–488. Microtome Publishing, Brookline, MA.
Paper not yet in RePEc: Add citation now
- Wagner P, Wegener M, (2007) Urban land use, transport and environment models. disP 43(170): 45–56.
Paper not yet in RePEc: Add citation now
- Weidner T, Knudson B, Picado R, , et al, . (2009) Sensitivity testing with the Oregon statewide integrated model. Transportation Research Record: Journal of the Transportation Research Board 2133(2133): 109–122.
Paper not yet in RePEc: Add citation now
- Wijffels J, Okazaki N, (2007) Conditional random fields for labelling sequential data in natural language processing based on CRFsuite: A fast implementation of Conditional Random Fields (CRFs). R package version 0.1, https://github.com/bnosac/crfsuite. https://github.com/bnosac/crfsuite .
Paper not yet in RePEc: Add citation now
- Wolf J, Schönfelder S, Samaga U, , et al, . (2004) Eighty weeks of global positioning system traces: Approaches to enriching trip information. Transportation Research Record: Journal of the Transportation 1870(1): 46–54.
Paper not yet in RePEc: Add citation now
- Wu J, Jiang C, Houston D, , et al, . (2011) Automated time activity classification based on global positioning system (GPS) tracking data. Environmental Health 10(101): 22082316.
Paper not yet in RePEc: Add citation now
- Zhu Y, (2018) Extract the spatiotemporal distribution of transit trips from smart card transaction data: A comparison between Shanghai and Singapore. In: Shen Z and Li M (eds) Big Data Support of Urban Planning and Management: The Experience in China. Cham: Springer, pp.297–315.
Paper not yet in RePEc: Add citation now
Zhu Y, (2020) Estimating the activity types of transit travelers using smart card transaction data: A case study of Singapore. Transportation 47: 2703–2730.
Zhu Y, Diao M, (2016) The impacts of urban mass rapid transit lines on the density and mobility of high-income households: A case study of Singapore. Transport Policy 51: 70–80.