Page 68 - Proceeding of Atrans Young Researcher's Forum 2019_Neat
P. 68
“Transportation for A Better Life:
Smart Mobility for Now and Then”
23 August 2019, Bangkok, Thailand
Res. Rec. J. Transp. Res. Board, vol. 2594, no. 1, pp. Nested Equations,” Surv. Rev., vol. 23, no. 176, pp.
35–43, Jan. 2016. 88–93, Apr. 1975.
[11] W. Bohte and K. Maat, “Deriving and [23] M. H. Nguyen, T. T. Ha, S. S. Tu, and T. C.
validating trip purposes and travel modes for multi- Nguyen, “Impediments to the bus rapid transit
day GPS-based travel surveys: A large-scale implementation in developing countries – a typical
application in the Netherlands,” Transp. Res. Part C evidence from Hanoi,” Int. J. Urban Sci., vol. 0, no.
Emerg. Technol., vol. 17, no. 3, pp. 285–297, Jun. 0, pp. 1–20, Feb. 2019.
2009. [24] M. H. Nguyen, T. T. Ha, T. L. Le, and T. C.
[12] H. Gong, C. Chen, E. Bialostozky, and C. T. Nguyen, “Challenges to Development of Bus
Lawson, “A GPS/GIS method for travel mode System Evidence from a Comparative Analysis of
detection in New York City,” Comput. Environ. Surveys in Hanoi,” in Transportation for a Better
Urban Syst., vol. 36, no. 2, pp. 131–139, Mar. 2012. Life: Mobility and Road Safety Managements,
[13] N. Schuessler and K. Axhausen, “Processing Bangkok, Thailand, 2017, pp. 1–10.
Raw Data from Global Positioning Systems Without [25] T. K. Rasmussen, J. B. Ingvardson, K.
Additional Information,” Transp. Res. Rec. J. Halldórsdóttir, and O. A. Nielsen, “Improved
Transp. Res. Board, vol. 2105, pp. 28–36, Oct. 2009. methods to deduct trip legs and mode from travel
[14] S. Tsui and A. Shalaby, “Enhanced System for surveys using wearable GPS devices: A case study
Link and Mode Identification for Personal Travel from the Greater Copenhagen area,” Comput.
Surveys Based on Global Positioning Systems,” Environ. Urban Syst., vol. 54, pp. 301–313, Nov.
Transp. Res. Rec. J. Transp. Res. Board, vol. 1972, 2015.
pp. 38–45, Jan. 2006. [26] A. Nour, B. Hellinga, and J. Casello,
[15] R. Dalumpines and D. M. Scott, “Making mode “Classification of automobile and transit trips from
detection transferable: extracting activity and travel Smartphone data: Enhancing accuracy using spatial
episodes from GPS data using the multinomial logit statistics and GIS,” J. Transp. Geogr., vol. 51, no.
model and Python,” Transp. Plan. Technol., vol. 40, Supplement C, pp. 36–44, Feb. 2016.
no. 5, pp. 523–539, Jul. 2017.
[16] T. Feng and H. J. P. Timmermans, “Comparison
of advanced imputation algorithms for detection of
transportation mode and activity episode using GPS
data,” Transp. Plan. Technol., vol. 39, no. 2, pp.
180–194, Feb. 2016.
[17] S. Dabiri and K. Heaslip, “Inferring
transportation modes from GPS trajectories using a
convolutional neural network,” Transp. Res. Part C
Emerg. Technol., vol. 86, pp. 360–371, Jan. 2018.
[18] H. Safi, B. Assemi, M. Mesbah, and L. Ferreira,
“Trip Detection with Smartphone-Assisted
Collection of Travel Data,” Transp. Res. Rec. J.
Transp. Res. Board, vol. 2594, pp. 18–26, Jan. 2016.
[19] A. Nour, “Automating and Optimizing a
Transportation Mode Classification Model for use
on Smartphone Data,” Doctoral Thesis, University
of Waterloo, Canada, 2015.
[20] M. H. Nguyen, J. Armoogum, and C. Garcia,
“Mode-Based Comparison of Data in Mobility
Surveys using GPS and Telephone,” presented at the
98th TRB Annual Meeting, Washington, D.C., 2019.
[21] P. Stopher, C. FitzGerald, and J. Zhang, “Search
for a global positioning system device to measure
person travel,” Transp. Res. Part C Emerg. Technol.,
vol. 16, no. 3, pp. 350–369, Jun. 2008.
[22] T. Vincenty, “Direct and Inverse Solutions of
Geodesics on the Ellipsoid with Application of
43