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Traveler Mobility and Activity Pattern Inference UsingPersonal Smartphone Applications and ArtificialIntelligence Methods


Traveler Mobility and Activity Pattern Inference UsingPersonal Smartphone Applications and ArtificialIntelligence Methods

Yazdizadeh, Ali (2019) Traveler Mobility and Activity Pattern Inference UsingPersonal Smartphone Applications and ArtificialIntelligence Methods. PhD thesis, Concordia University.

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Recent advances in communication technologies have enabled researchers to collect travel data from location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode, purpose, etc.) of people’s travel. This thesis investigates the application of artificial intelligence methods to infer mode of transport, trip purpose and transit itinerary from traveler trajectories gathered by smartphones. Supervised, Random Forest models are used to detect mode, purpose and transit itinerary of trips. Deep learning models, in particular, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are also employed to infer mode of transport and trip purpose. The research also explores the use of Generative Adversarial Networks (GANs), as a semi-supervised learning approach, to classify trip mode. Moreover, we investigate the application of multi-task learning to simultaneously infer mode and purpose.

The research uses several different data sources. Trip trajectory data was collected by the MTL Trajet smartphone Travel Survey App, in 2016. Also, other complementary datasets, such as locational
data from social media, land-use, General Transit Feed Specification (GTFS), and elevation data are exploited to infer trip information.

Mode of transport can be inferred with Random Forest models, ensemble CNN models, and RNN approaches with an accuracy of 87%, 91%, and 86%, respectively. The Random Forest and
multi-task RNN models to infer trip purpose achieve an accuracy of 71% and 78%, respectively. Also, the Random Forest transit itinerary inference model can predict used transit itineraries with an accuracy of 81%. While further improvement is required to enhance the performance of the developed artificial intelligence models on smartphone data, the results of the research indicate the capability of smartphone-based travel surveys as a complementary (and potentially replacement) surveying tool to household travel surveys.

Divisions:Concordia University > Faculty of Arts and Science > Geography, Planning and Environment
Item Type:Thesis (PhD)
Authors:Yazdizadeh, Ali
Institution:Concordia University
Degree Name:Ph. D.
Program:Geography, Urban & Environmental Studies
Date:3 February 2019
Thesis Supervisor(s):Patterson, Zachary and Farooq, Bilal
Keywords:Smartphone travel survey, GPS trajectory, Household travel survey, Trip information, Mode of transport inference, Purpose of trip inference, Transit itinerary inference, Machine learning, Random Forest, Recurrent Neural Networks, Convolutional Neural Networks
ID Code:981881
Deposited On:27 Oct 2022 13:50
Last Modified:27 Oct 2022 13:50


[1] Machine learning glossary — google developers, . URL https://developers.
google.com/machine-learning/glossary. Accessed: 2020-04-20.
[2] J. Ortuzar and L. Willumsen. Modelling Transport. John Wiley and Sons, Chichester, fourth
edition, 2011.
[3] Li Shen and Peter R Stopher. Review of GPS travel survey and GPS data-processing methods.
Transport Reviews, 34(3):316–334, 2014.
[4] Jun Wu, Chengsheng Jiang, Douglas Houston, Dean Baker, and Ralph Delfino. Automated
time activity classification based on global positioning system GPS tracking data. Environmental
Health, 10(1):101, 2011.
[5] Antonin Danalet and Nicole A Mathys. The potential of smartphone data for national travel
surveys. In 17th Swiss transport research conference, Monte Verit`a/Ascona, pages 17–19,
[6] Jean Louise Wolf. Using GPS data loggers to replace travel diaries in the collection of
travel data. PhD thesis, School of Civil and Environmental Engineering, Georgia Institute of
Technology, 2000.
[7] Francisco Pereira, Carlos Carrion, Fang Zhao, Caitlin D Cottrill, Chris Zegras, and Moshe
Ben-Akiva. The future mobility survey: Overview and preliminary evaluation. In Proceedings
of the Eastern Asia Society for Transportation Studies, volume 9, pages 1–9, 2013.
[8] Zachary Patterson, Kyle Fitzsimmons, Stewart Jackson, and Takeshi Mukai. Itinerum: The
open smartphone travel survey platform. SoftwareX, 10:100230, 2019.
[9] Leah Flake, Michelle Lee, Kevin Hathaway, and Elizabeth Greene. Use of smartphone panels
for viable and cost-effective GPS data collection for small and medium planning agencies.
Transportation Research Record, 2643(1):160–165, 2017.
[10] Fang Zhao, Francisco Cˆamara Pereira, Rudi Ball, Youngsung Kim, Yafei Han, Christopher
Zegras, and Moshe Ben-Akiva. Exploratory analysis of a smartphone-based travel survey in
Singapore. Transportation Research Record, 2494(1):45–56, 2015.
[11] David T Hartgen and Elizabeth San Jose. Costs and trip rates of recent household travel
surveys. Hartgen Group, Charlotte, NC, USA, 2009.
[12] Cambridge Systematics. Travel Demand Forecasting: Parameters and Techniques, volume
716. Transportation Research Board, 2012.
[13] Lara Montini, Sebastian Prost, Johann Schrammel, Nadine Rieser-Sch¨ussler, and Kay W
Axhausen. Comparison of travel diaries generated from smartphone data and dedicated GPS
devices. Transportation Research Procedia, 11:227–241, 2015.
[14] Na Ta, Mei-Po Kwan, Yanwei Chai, and Zhilin Liu. Gendered space-time constraints, activity
participation and household structure: A case study using a GPS-based activity survey in
suburban beijing, china. Tijdschrift voor economische en sociale geografie, 107(5):505–521,
[15] Zhenzhen Wang, Sylvia Y He, and Yee Leung. Applying mobile phone data to travel behaviour
research: A literature review. Travel Behaviour and Society, 11:141–155, 2018.
[16] Jerald Jariyasunant, Raja Sengupta, and Joan Walker. Overcoming battery life problems of
smartphones when creating automated travel diaries. Technical Report No. UCTC-FR-2014-
05, 2014.
[17] Gregory Bucci, Tom Morton, et al. Cell phone data and travel behavior research: symposium
summary report. Technical report, United States. Federal Highway Administration, 2014.
[18] Zachary Patterson and Kyle Fitzsimmon. MTL Trajet. Technical Report 2017-2, Concordia
University, TRIP Lab, Montreal, Canada, July 2017.
[19] Peter R Stopher, Li Shen, Wen Liu, and Asif Ahmed. The challenge of obtaining ground
truth for GPS processing. Transportation Research Procedia, 11:206–217, 2015.
[20] Scott Krig. Ground truth data, content, metrics, and analysis. In Computer Vision Metrics,
pages 247–271. Springer, 2016.
[21] Jean Wolf, Marcelo Oliveira, and Miriam Thompson. Impact of underreporting on mileage
and travel time estimates: Results from global positioning system-enhanced household travel
survey. Transportation Research Record: Journal of the Transportation Research Board,
(1854):189–198, 2003.
[22] D Pearson. A comparison of trip determination methods in GPS-enhanced household travel
surveys. In 84th annual meeting of the Transportation Research Board, Washington, DC,
[23] John L Bowman, Mark Bradley, Joe Castiglione, and Supin L Yoder. Making advanced travel
forecasting models affordable through model transferability. In the 93rd Annual Meeting of
Transportation Research Board, Washington, DC, 2014.
[24] John L Bowman and Moshe E Ben-Akiva. Activity-based disaggregate travel demand model
system with activity schedules. Transportation Research Part A: Policy and Practice, 35(1):
1–28, 2001.
[25] Patricia L Mokhtarian and Ilan Salomon. How derived is the demand for travel? some
conceptual and measurement considerations. Transportation Research Part A, 35(695):719,
[26] Michael Patriksson. The traffic assignment problem: models and methods. Courier Dover
Publications, 2015.
[27] Yin Lou, Chengyang Zhang, Yu Zheng, Xing Xie,WeiWang, and Yan Huang. Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the 17th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems, pages 352–361. ACM,
[28] Ehsan Mazloumi, Mahmoud Mesbah, Avi Ceder, Sara Moridpour, and Graham Currie. Efficient
transit schedule design of timing points: a comparison of ant colony and genetic algorithms.
Transportation Research Part B: Methodological, 46(1):217–234, 2012.
[29] Jerry CN Ng and Paul M Sarjeant. Use of direct data entry for travel surveys. Transportation
Research Record, (1412), 1993.
[30] K Habib, Joffre Swait, and Sarah Salem. Investigating structural changes in commuting mode
choice preferences with repeated cross-sectional travel survey data: the contexts of Greater
Toronto and Hamilton (GTHA) area. In 13th International Conference on Travel Behaviour
Research, Toronto, volume 1520, 2012.
[31] Ying Long and Jean-Claude Thill. Combining smart card data and household travel survey to
analyze jobs–housing relationships in beijing. Computers, Environment and Urban Systems,
53:19–35, 2015.
[32] Selmer Bringsjord and Naveen Sundar Govindarajulu. Artificial intelligence. In Edward N.
Zalta, editor, The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford
University, summer 2020 edition, 2020.
[33] Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning, volume
1. MIT press, Cambridge, 2016.
[34] Md Zahangir Alom, TarekMTaha, Christopher Yakopcic, StefanWestberg, Paheding Sidike,
Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, and Vijayan K Asari. The
history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv
preprint arXiv:1803.01164, 2018.
[35] Rina Dechter. Learning while searching in constraint-satisfaction problems. University of
California, Computer Science Department, Cognitive Systems., 1986.
[36] Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. Semi-supervised learning. IEEE
Transactions on Neural Networks, 20(3):542–542, 2009.
[37] Mohsen Rezaie, Zachary Patterson, Jia Yuan Yu, and Ali Yazdizadeh. Semi-supervised travel
mode detection from smartphone data. In 2017 International Smart Cities Conference (ISC2),
pages 1–8. IEEE, 2017.
[38] Min-Ling Zhang and Zhi-Hua Zhou. A review on multi-label learning algorithms. IEEE
Transactions on Knowledge and Data Engineering, 26(8):1819–1837, 2013.
[39] Sina Dabiri and Kevin Heaslip. Inferring transportation modes from GPS trajectories using a
convolutional neural network. Transportation Research Part C: Emerging Technologies, 86:
360–371, 2018.
[40] Zhibin Xiao, Yang Wang, Kun Fu, and Fan Wu. Identifying different transportation modes
from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of
Geo-Information, 6(2):57, 2017.
[41] Mogeng Yin, Madeleine Sheehan, Sidney Feygin, Jean-Franc¸ois Paiement, and Alexei Pozdnoukhov.
A generative model of urban activities from cellular data. IEEE Transactions on
Intelligent Transportation Systems, 19(6):1682–1696, 2017.
[42] Leon Stenneth, Ouri Wolfson, Philip S Yu, and Bo Xu. Transportation mode detection using
mobile phones and GIS information. In Proceedings of the 19th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems, pages 54–63. ACM,
[43] Tao Feng and Harry JP Timmermans. Transportation mode recognition using GPS and accelerometer
data. Transportation Research Part C: Emerging Technologies, 37:118–130,
[44] Hamid Reza Eftekhari and Mehdi Ghatee. An inference engine for smartphones to preprocess
data and detect stationary and transportation modes. Transportation Research Part C:
Emerging Technologies, 69:313–327, 2016.
[45] Arash Jahangiri and Hesham Rakha. Developing a support vector machine (SVM) classifier
for transportation mode identification by using mobile phone sensor data. In 93rd Annual
Meeting of Transportation Research Board, number 14-1442, 2014.
[46] Peter R Stopher. The travel survey toolkit: where to from here? In Transport survey methods:
Keeping up with a changing world, pages 15–46. Emerald Group Publishing Limited, 2009.
[47] Wendy Bohte and Kees Maat. 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, 2009.
[48] Filip Biljecki, Hugo Ledoux, and Peter van Oosterom. Transportation mode-based segmentation
and classification of movement trajectories. International Journal of Geographical
Information Science, 27(2):385–407, 2013.
[49] Nadine Schuessler and Kay Axhausen. Processing raw data from global positioning systems
without additional information. Transportation Research Record: Journal of the Transportation
Research Board, (2105):28–36, 2009.
[50] Ron Dalumpines and Darren M Scott. Making mode detection transferable: extracting activity
and travel episodes from GPS data using the multinomial logit model and python.
Transportation Planning and Technology, 40(5):523–539, 2017.
[51] S. Dabiri, C. Lu, K. Heaslip, and C. K. Reddy. Semi-supervised deep learning approach for
transportation mode identification using gps trajectory data. IEEE Transactions on Knowledge
and Data Engineering, 32(5):1010–1023, 2020.
[52] A Santos, N McGuckin, HY Nakamoto, D Gray, and S Liss. Summary of travel trends: 2009
national household travel survey, us department of transportation, federal highway administration.
Technical report, FHWA-PL-11022, Jun, 2011.
[53] L’enquˆete origine-destination 2018. Technical report, l’Autorit´e r´egionale de transport
m´etropolitain, 2019. URL https://www.artm.quebec/enqueteod/.
[54] Guangnian Xiao, Zhicai Juan, and Jingxin Gao. Travel mode detection based on neural
networks and particle swarm optimization. Information, 6(3):522–535, 2015.
[55] Philippe Nitsche, Peter Widhalm, Simon Breuss, Norbert Br¨andle, and Peter Maurer. Supporting
large-scale travel surveys with smartphones–a practical approach. Transportation
Research Part C: Emerging Technologies, 43:212–221, 2014.
[56] Bao Wang, Linjie Gao, and Zhicai Juan. 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, 2018.
[57] Behrang Assemi, Hamid Safi, Mahmoud Mesbah, and Luis Ferreira. Developing and validating
a statistical model for travel mode identification on smartphones. IEEE Transactions
on Intelligent Transportation Systems, 17(7):1920–1931, 2016.
[58] Yuki Endo, Hiroyuki Toda, Kyosuke Nishida, and Akihisa Kawanobe. Deep feature extraction
from trajectories for transportation mode estimation. In Pacific-Asia Conference on
Knowledge Discovery and Data Mining, pages 54–66. Springer, 2016.
[59] Paola A Gonzalez, Jeremy S Weinstein, Sean J Barbeau, Miguel A Labrador, Philip L Winters,
Nevine L Georggi, and R Perez. Automating mode detection for travel behaviour analysis
by using global positioning systems-enabled mobile phones and neural networks. IET
Intelligent Transport Systems, 4(1):37–49, 2010.
[60] Fei Yang, Zhenxing Yao, and Peter J Jin. GPS and acceleration data in multimode trip
data recognition based on wavelet transform modulus maximum algorithm. Transportation
Research Record, 2526(1):90–98, 2015.
[61] Young-Ji Byon and Steve Liang. 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, 18
(3):264–272, 2014.
[62] Guangnian Xiao, Juan Zhicai, and Gao Jingxin. Inferring trip ends from GPS data based on
smartphones in shanghai. In 94th Annual Meeting of Transportation Research Board, number
15-2454, 2015.
[63] Zahra Ansari Lari and Amir Golroo. Automated transportation mode detection using smart
phone applications via machine learning: Case study mega city of Tehran. In Proceedings
of 94th Annual Meeting of Transportation Research Board, Washington, DC, USA, pages
11–15, 2015.
[64] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, and Wei-Ying Ma. Understanding mobility
based on GPS data. In Proceedings of the 10th International Conference on Ubiquitous
Computing, pages 312–321. ACM, 2008.
[65] Lijuan Zhang, Sagi Dalyot, Daniel Eggert, and Monika Sester. Multi-stage approach to
travel-mode segmentation and classification of GPS traces. International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences:[Geospatial Data Infrastructure:
From Data Acquisition And Updating To Smarter Services] 38-4 (2011), Nr.
W25, 38(W25):87–93, 2011.
[66] Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, and Mani Srivastava.
Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks
(TOSN), 6(2):13, 2010.
[67] Elton F de S Soares, Kate Revoredo, Fernanda Bai˜ao, Carlos A de MS Quintella, and Carlos
Alberto V Campos. A combined solution for real-time travel mode detection and trip purpose
prediction. IEEE Transactions on Intelligent Transportation Systems, 20(12):4655–4664,
[68] Joseph Broach, 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.
[69] Lei Gong, Takayuki Morikawa, Toshiyuki Yamamoto, and Hitomi Sato. Deriving personal trip data from GPS data: a literature review on the existing methodologies. Procedia-Social
and Behavioral Sciences, 138:557–565, 2014.
[70] Lei GONG, Toshiyuki Yamamoto, and Takayuki Morikawa. Comparison of activity type
identification from mobile phone GPS data using various machine learning methods. Asian
Transport Studies, 4(1):114–128, 2016.
[71] Jean Wolf, Randall Guensler, and William Bachman. 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):125–134, 2001.
[72] Huiying Zhao, Dalin Qian, Ying Lv, Bo Zhang, and Rongyu Liang. Development of a global
positioning system data-based trip-purpose inference method for hazardous materials transportation
management. Journal of Intelligent Transportation Systems, pages 1–16, 2019.
[73] Kay W Axhausen, Stefan Sch¨onfelder, J Wolf, M Oliveira, and Ute Samaga. 80 weeks
of GPS-traces: approaches to enriching the trip information. Arbeitsberichte Verkehrs-und
Raumplanung, 178, 2003.
[74] Patrick McGowen and Michael McNally. Evaluating the potential to predict activity types
from gps and GIS data. In 86th Annual Meeting of Transportation Research Board, number
07-3199, 2007.
[75] Zhongwei Deng and Minhe Ji. Deriving rules for trip purpose identification from GPS travel
survey data and land use data: A machine learning approach. In Traffic and Transportation
Studies 2010, pages 768–777. 2010.
[76] Marcelo Oliveira, Peter Vovsha, JeanWolf, and Michael Mitchell. 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, 2014.
[77] Youngsung Kim, Francisco C Pereira, Fang Zhao, Ajinkya Ghorpade, P Christopher Zegras,
and Moshe Ben-Akiva. Activity recognition for a smartphone and web based travel survey.
arXiv preprint arXiv:1502.03634, 2015.
[78] Zack Zhu, Ulf Blanke, and Gerhard Tr¨oster. Inferring travel purpose from crowd-augmented
human mobility data. In Proceedings of the First International Conference on IoT in Urban
Space, pages 44–49. Institute for Computer Sciences, Social-Informatics and Telecommunications
Engineering, 2014.
[79] Alireza Ermagun, Yingling Fan, Julian Wolfson, Gediminas Adomavicius, and Kirti Das.
Real-time trip purpose prediction using online location-based search and discovery services.
Transportation Research Part C: Emerging Technologies, 77:96–112, 2017.
[80] Zong Fang, Lv Jian-yu, Tang Jin-jun, Wang Xiao, and Gao Fei. Identifying activities and
trips with GPS data. IET Intelligent Transport Systems, 12(8):884–890, 2018.
[81] Foursquare for developers: Venue response. https://developer.foursquare.
com/docs/responses/venue, 2017. [Accessed on June 12, 2017].
[82] Seyed Amir H Zahabi, Ajang Ajzachi, and Zachary Patterson. Transit trip itinerary inference
with gtfs and smartphone data. Transportation Research Record, 2652(1):59–69, 2017.
[83] Filipe Rodrigues, Kristian Henrickson, and Francisco C Pereira. Multi-output gaussian processes
for crowdsourced traffic data imputation. IEEE Transactions on Intelligent Transportation
Systems, 20(2):594–603, 2018.
[84] Leo Breiman. Random forests. Machine learning, 45(1):5–32, 2001.
[85] Kellie J Archer and Ryan V Kimes. Empirical characterization of random forest variable
importance measures. Computational Statistics & Data Analysis, 52(4):2249–2260, 2008.
[86] Thomas G Dietterich. An experimental comparison of three methods for constructing ensembles
of decision trees: Bagging, boosting, and randomization. Machine learning, 40(2):
139–157, 2000.
[87] Andy Liaw, Matthew Wiener, et al. Classification and regression by randomforest. R news,
2(3):18–22, 2002. URL http://CRAN.R-project.org/doc/Rnews/.
[88] Leo Breiman and Adele Cutler. Random forests-classification description, 2007. URL
htm. [Accessed on April 23, 2020].
[89] Leo Breiman. Manual on setting up, using, and understanding random forests v3. 1. Statistics
Department University of California Berkeley, CA, USA, 1:58, 2002.
[90] Vincent Dumoulin and Francesco Visin. A guide to convolution arithmetic for deep learning.
arXiv preprint arXiv:1603.07285, 2016.
[91] Zachary C Lipton, John Berkowitz, and Charles Elkan. A critical review of recurrent neural
networks for sequence learning. arXiv preprint arXiv:1506.00019, 2015.
[92] Daniel Quang and Xiaohui Xie. DanQ: a hybrid convolutional and recurrent deep neural
network for quantifying the function of DNA sequences. Nucleic Acids Research, 44(11):
e107, 2016.
[93] Bing Xu, NaiyanWang, Tianqi Chen, and Mu Li. Empirical evaluation of rectified activations
in convolutional network. arXiv preprint arXiv:1505.00853, 2015.
[94] J¨urgen Schmidhuber. Learning complex, extended sequences using the principle of history
compression. Neural Computation, 4(2):234–242, 1992.
[95] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil
Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in
Neural Information Processing Systems, pages 2672–2680, 2014.
[96] Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. InfoGAN:
Interpretable Representation Learning by Information Maximizing Generative Adversarial
Nets. In Advances in Neural Information Processing Systems, pages 2172–2180,
[97] Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele,
and Honglak Lee. Generative adversarial text to image synthesis. arXiv preprint
arXiv:1605.05396, 3:1681–1690, 2016.
[98] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation
with conditional adversarial networks. arXiv preprint arXiv:1611.07004, 2016.
[99] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and
Xi Chen. Improved techniques for training GANs. In Advances in Neural Information Processing
Systems, pages 2234–2242, 2016.
[100] Jost Tobias Springenberg. Unsupervised and Semi-supervised Learning with Categorical
Generative Adversarial Networks. arXiv preprint arXiv:1511.06390, 2015.
[101] Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets. arXiv preprint
arXiv:1411.1784, 2014.
[102] Ian Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv preprint
arXiv:1701.00160, 2016.
[103] Melvin Wong, Bilal Farooq, and Guillaume-Alexandre Bilodeau. Discriminative conditional
restricted boltzmann machine for discrete choice and latent variable modelling. Journal of
choice modelling, 29:152–168, 2018.
[104] Caitlin Cottrill, Francisco Pereira, Fang Zhao, Inˇes Dias, Hock Lim, Moshe Ben-Akiva, and
P Zegras. Future mobility survey: Experience in developing a smartphone-based travel survey
in Singapore. Transportation Research Record: Journal of the Transportation Research
Board, (2354):59–67, 2013.
[105] P Stopher, Y Zhang, J Zhang, and B Halling. Results of an evaluation of travelsmart in South
Australia. In Australasian Transport Research Forum (ATRF), 32nd, 2009, Auckland, New
Zealand, volume 32, 2009.
[106] Heping Zhang and Burton Singer. Recursive partitioning and applications. Springer Science
& Business Media, 2010.
[107] Lara Montini, Nadine Rieser-Sch¨ussler, Andreas Horni, and Kay W Axhausen. Trip purpose
identification from GPS tracks. Transportation Research Record, 2405(1):16–23, 2014.
[108] Martin Catala et al. Expanding the google transit feed specification to support operations and
planning [summary]. Technical report, Florida. Dept. of Transportation. Research Center,
[109] Zachary Patterson and Kyle Fitzsimmons. Datamobile: Smartphone travel survey experiment.
Transportation Research Record: Journal of the Transportation Research Board,
(2594):35–43, 2016.
[110] Fang Zhao, Ajinkya Ghorpade, Francisco Cˆamara Pereira, Christopher Zegras, and Moshe
Ben-Akiva. Stop detection in smartphone-based travel surveys. Transportation Research
Procedia, 11:218–226, 2015.
[111] Guangnian Xiao, Zhicai Juan, and Chunqin Zhang. Detecting trip purposes from smartphonebased
travel surveys with artificial neural networks and particle swarm optimization. Transportation
Research Part C: Emerging Technologies, 71:447–463, 2016.
[112] Elizabeth Greene, Leah Flake, Kevin Hathaway, and Michael Geilich. A seven-day
smartphone-based GPS household travel survey in indiana. In 95th Annual Meeting of Transportation
Research Board, number 16-6274, 2016.
[113] Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. Data Mining: Practical
machine learning tools and techniques. Morgan Kaufmann, 2016.
[114] Milad Ghasri, Taha Hossein Rashidi, and S Travis Waller. Developing a disaggregate travel
demand system of models using data mining techniques. Transportation Research Part A:
Policy and Practice, 105:138–153, 2017.
[115] Chaoran Zhou, Hongfei Jia, Zhicai Juan, Xuemei Fu, and Guangnian Xiao. 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, 2017.
[116] Muhammad Awais Shafique and Eiji Hato. Use of acceleration data for transportation mode
prediction. Transportation, 42(1):163–188, 2015.
[117] Muhammad Awais Shafique and Eiji Hato. Travel mode detection with varying smartphone
data collection frequencies. Sensors, 16(5):716, 2016.
[118] Thanos Bantis and James Haworth. Who you are is how you travel: A framework for transportation
mode detection using individual and environmental characteristics. Transportation
Research Part C: Emerging Technologies, 80:286–309, 2017.
[119] Ziheng Lin, Mogeng Yin, Sidney Feygin, Madeleine Sheehan, Jean-Francois Paiement, and
Alexei Pozdnoukhov. Deep generative models of urban mobility. IEEE Transactions on
Intelligent Transportation Systems, 2017.
[120] Timothy Sohn, Alex Varshavsky, Anthony LaMarca, Mike Y Chen, Tanzeem Choudhury,
Ian Smith, Sunny Consolvo, Jeffrey Hightower, William G Griswold, and Eyal De Lara.
Mobility detection using everyday GSM traces. In International Conference on Ubiquitous
Computing, pages 212–224. Springer, 2006.
[121] LinlinWu, Biao Yang, and Peng Jing. Travel mode detection based on GPS raw data collected
by smartphones: a systematic review of the existing methodologies. Information, 7(4):67,
[122] Zhanbo Sun and Xuegang Jeff Ban. Vehicle classification using GPS data. Transportation
Research Part C: Emerging Technologies, 37:102–117, 2013.
[123] Hao Wang, GaoJun Liu, Jianyong Duan, and Lei Zhang. Detecting transportation modes
using deep neural network. IEICE Transactions on Information and Systems, 100(5):1132–
1135, 2017.
[124] Taha H Rashidi, Alireza Abbasi, Mojtaba Maghrebi, Samiul Hasan, and Travis S Waller.
Exploring the capacity of social media data for modelling travel behaviour: Opportunities
and challenges. Transportation Research Part C: Emerging Technologies, 75:197–211, 2017.
[125] Defu Lian and Xing Xie. Collaborative activity recognition via check-in history. In Proceedings
of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social
Networks, pages 45–48. ACM, 2011.
[126] Samiul Hasan and Satish V Ukkusuri. Urban activity pattern classification using topic models
from online geo-location data. Transportation Research Part C: Emerging Technologies, 44:
363–381, 2014.
[127] Samiul Hasan and Satish V Ukkusuri. Location contexts of user check-ins to model urban
geo life-style patterns. PLoS One, 10(5):e0124819, 2015.
[128] Jae Hyun Lee, Adam W Davis, Seo Youn Yoon, and Konstadinos G Goulias. Activity space
estimation with longitudinal observations of social media data. Transportation, 43(6):955–
977, 2016.
[129] Zhenhua Zhang, Qing He, and Shanjiang Zhu. Potentials of using social media to infer the
longitudinal travel behavior: A sequential model-based clustering method. Transportation
Research Part C: Emerging Technologies, 85:396–414, 2017.
[130] Yongmei Lu and Yu Liu. Pervasive location acquisition technologies: Opportunities and
challenges for geospatial studies. Computers, Environment and Urban Systems, 36(2):105–
108, 2012.
[131] OpenTripPlanner Bibliography. http://docs.opentripplanner.org/en/
latest/Bibliography/, 2017. [Accessed on July 18, 2017].
[132] Ville de Montreal. MTL Trajet. https://ville.montreal.qc.ca/mtltrajet/
en/, 2017. [Accessed on June 15, 2017].
[133] MAMROT. Localisation des immeubles 2011. Ministere des Affaires Municipales, des
Regions et de l’Occupations du Territoire (MAMROT), 2011.
[134] Meredith Kimberly Cebelak. Transportation planning via location-based social networking
data: exploring many-to-many connections. PhD thesis, University of Texas at Austin,
August 2015.
[135] Foursquare for Developers: Venue Categories. https://developer.foursquare.
com/docs/venues/categories, 2017. [Accessed on June 12, 2017].
[136] Get Elevations - MSDN. https://msdn.microsoft.com/en-us/library/
jj158961.aspx, 2017. [Accessed on October 20, 2017].
[137] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria, 2014. URL http://www.R-project.org/.
[138] S Skiena. Dijkstra’s algorithm. Implementing Discrete Mathematics: Combinatorics and
Graph Theory with Mathematica, Reading, MA: Addison-Wesley, pages 225–227, 1990.
[139] Richard Bellman. On a routing problem. Quarterly of Applied Mathematics, 16(1):87–90,
[140] Bradley S. Stewart and Chelsea C. White. Multiobjective a*. J. ACM, 38(4):775–814, October
1991. ISSN 0004-5411.
[141] J. L. P´erez De la Cruz, J. L. P´erez De la Cruz, L. Mandow, and L. Mandow. A new approach
to multiobjective a* search. In Proceedings of the 19th International Joint Conference on
Artificial Intelligence, IJCAI’05, page 218–223, San Francisco, CA, USA, 2005. Morgan
Kaufmann Publishers Inc.
[142] OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org . https:
//www.openstreetmap.org, 2017.
[143] Charu C Aggarwal. Data mining: the textbook. Springer, 2015.
[144] Yu Zheng, Hao Fu, Xing Xie,Wei-Ying Ma, and Quannan Li. Geolife GPS trajectory dataset
- User Guide, 2011. URL https://www.microsoft.com/en-us/research/
[145] Yoshua Bengio. Deep learning of representations: Looking forward. In International Conference
on Statistical Language and Speech Processing, pages 1–37. Springer, 2013.
[146] Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting
Liu, Xingxing Wang, Gang Wang, Jianfei Cai, and Tsuhan Chen. Recent advances in convolutional
neural networks. Pattern Recognition, 77:354 – 377, 2018. ISSN 0031-3203.
[147] Mostafa Elhoushi, Jacques Georgy, Aboelmagd Noureldin, and Michael J Korenberg. A
survey on approaches of motion mode recognition using sensors. IEEE Transactions on
Intelligent Transportation Systems, 18(7):1662–1686, 2017.
[148] German Castignani, Rapha¨el Frank, and Thomas Engel. Driver behavior profiling using
smartphones. In 16th International IEEE Conference on Intelligent Transportation Systems
(ITSC), pages 552–557. IEEE, 2013.
[149] Cheng Ju, Aur´elien Bibaut, and Mark van der Laan. The relative performance of ensemble
methods with deep convolutional neural networks for image classification. Journal of Applied
Statistics, 45(15):2800–2818, 2018.
[150] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. ImageNet Classification with Deep
Convolutional Neural Networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger,
editors, Advances in Neural Information Processing Systems 25, pages 1097–1105.
Curran Associates, Inc., 2012.
[151] Rich Caruana, Steve Lawrence, and C Lee Giles. Overfitting in neural nets: Backpropagation,
conjugate gradient, and early stopping. In Advances in Neural Information Processing
Systems, pages 402–408, 2001.
[152] Kaiming He and Jian Sun. Convolutional neural networks at constrained time cost. In Proceedings
of the IEEE conference on computer vision and pattern recognition, pages 5353–
5360, 2015.
[153] Ali Yazdizadeh, Zachary Patterson, and Bilal Farooq. An automated approach from gps
traces to complete trip information. International Journal of Transportation Science and
Technology, 8(1):82 – 100, 2019. ISSN 2046-0430.
[154] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):
436–444, 2015.
[155] Arash Kalatian and Bilal Farooq. Mobility mode detection using wifi signals. In 2018 IEEE
International Smart Cities Conference (ISC2), pages 1–7. IEEE, 2018.
[156] Augustus Odena. Semi-supervised learning with Generative Adversarial Networks. arXiv
preprint arXiv:1606.01583, 2016.
[157] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning
with deep convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434,
[158] Mohamed Zaki, Tarek Sayed, and Khaled Shaaban. Use of drivers’ jerk profiles in computer
vision-based traffic safety evaluations. Transportation Research Record: Journal of the
Transportation Research Board, (2434):103–112, 2014.
[159] Ross Girshick, Forrest Iandola, Trevor Darrell, and Jitendra Malik. Deformable part models
are convolutional neural networks. In Proceedings of the IEEE conference on Computer
Vision and Pattern Recognition, pages 437–446, 2015.
[160] David Warde-Farley and Ian Goodfellow. 11 adversarial perturbations of deep neural networks.
Perturbations, Optimization, and Statistics, MIT Press, 2016.
[161] Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. Rectifier nonlinearities improve neural
network acoustic models. In Proceedings of the 30 th International Conference on Machine
Learning, Atlanta, Georgia, USA, volume 30, page 3, 2013.
[162] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training
by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
[163] Tatjana Chavdarova and Franc¸ois Fleuret. SGAN: An alternative training of generative adversarial
networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, pages 9407–9415, 2018.
[164] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint
arXiv:1706.05098, 2017.
[165] Rich Caruana. Multitask Learning, pages 95–133. Springer US, Boston, MA, 1998. ISBN
[166] A. Yazdizadeh, Z. Patterson, and B. Farooq. Ensemble Convolutional Neural Networks for
Mode Inference in Smartphone Travel Survey. IEEE Transactions on Intelligent Transportation
Systems, pages 1–8, 2019.
[167] Toan H Vu, Le Dung, and Jia-Ching Wang. Transportation mode detection on mobile devices
using recurrent nets. In Proceedings of the 24th ACM International Conference on
Multimedia, pages 392–396. ACM, 2016.
[168] Sebastian Otte, Dirk Krechel, Marcus Liwicki, and Andreas Dengel. Local feature based
online mode detection with recurrent neural networks. In 2012 International Conference on
Frontiers in Handwriting Recognition, pages 533–537. IEEE, 2012.
[169] Matteo Simoncini, Leonardo Taccari, Francesco Sambo, Luca Bravi, Samuele Salti, and
Alessandro Lori. Vehicle classification from low-frequency GPS data with recurrent neural
networks. Transportation Research Part C: Emerging Technologies, 91:176–191, 2018.
[170] Hongbin Liu and Ickjai Lee. End-to-end trajectory transportation mode classification using
Bi-LSTM recurrent neural network. In 12th International Conference on Intelligent Systems
and Knowledge Engineering (ISKE), pages 1–5. IEEE, 2017.
[171] Yoshua Bengio, Patrice Simard, Paolo Frasconi, et al. Learning long-term dependencies with
gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157–166, 1994.
[172] Rahul Dey and Fathi M Salemt. Gate-variants of Gated Recurrent Unit (GRU) Neural Networks.
In IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS),
pages 1597–1600. IEEE, 2017.
[173] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical
evaluation of ated recurrent neural networks on sequence modeling. arXiv preprint
arXiv:1412.3555, 2014.
[174] Cheng Guo and Felix Berkhahn. Entity embeddings of categorical variables. arXiv preprint
arXiv:1604.06737, 2016.
[175] Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. Multi-task learning of pairwise
sequence classification tasks over disparate label spaces. arXiv preprint arXiv:1802.09913,
[176] Leigh Lane, Ann Hartell, Teresa Townsend, and Ann Steedly. Defining community context
in transportation project planning and development process. Technical Report of NCHRP
Project 25-25 Task 69, 2011.
[177] Godwin Badu-Marfo, Bilal Farooq, and Zachary Patterson. Perturbation methods for protection
of sensitive location data: Smartphone travel survey case study. Transportation Research
Record, page 0361198119855999, 2019.
[178] Varun Pandey, Andreas Kipf, Dimitri Vorona, Tobias M¨uhlbauer, Thomas Neumann, and
Alfons Kemper. High-performance geospatial analytics in hyperspace. In Proceedings of the
2016 International Conference on Management of Data, pages 2145–2148. ACM, 2016.
[179] S2 Geometry Library. https://github.com/google/s2geometry, . Accessed:
[180] Uber’s Hexagonal Hierarchical Spatial Index. https://eng.uber.com/h3/. Accessed:
[181] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word
representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
[182] Ying Liu, Xiufang Shi, Shibo He, and Zhiguo Shi. Prospective positioning architecture and
technologies in 5G networks. IEEE Network, 31(6):115–121, 2017.
[183] ZF Li, L Yu, Y Gao, Y Wu, D Gong, and G Song. Extraction method of temporal and spatial
characteristics of residents’ trips based on cellular signaling data. Transportation Research,
2(1):51–57, 2016.
[184] D Mitchell. New traffic data sources–an overview. Technical report, Bureau of Infrastructure,
Transport and Regional Economics, Canberra, ACT, Australia, 2014.
[185] Jie Yang and Yingying Chen. Indoor localization using improved RSS-based lateration methods.
In IEEE Global Telecommunications Conference (GLOBECOM), pages 1–6. IEEE,
[186] Sudarshan S Chawathe. Beacon placement for indoor localization using Bluetooth. In 11th
International IEEE Conference on Intelligent Transportation Systems, pages 980–985. IEEE,
[187] Stephen Greaves, Adrian Ellison, Richard Ellison, Dean Rance, Chris Standen, Chris Rissel,
and Melanie Crane. A web-based diary and companion smartphone app for travel/activity
surveys. Transportation Research Procedia, 11:297–310, 2015.
[188] AJ Richardson, ES Ampt, and AH Meyburg. Nonresponse issues in household travel surveys.
In Transportation Research Board Conference Proceedings, volume 10, pages 79–114, 1996.
[189] Mike Schuster and Kuldip K Paliwal. Bidirectional recurrent neural networks. IEEE Transactions
on Signal Processing, 45(11):2673–2681, 1997.
[190] Xiangang Li and Xihong Wu. Constructing long short-term memory based deep recurrent
neural networks for large vocabulary speech recognition. In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), pages 4520–4524. IEEE, 2015.
[191] Yoshua Bengio et al. Learning deep architectures for AI. Foundations and trends in Machine
Learning, 2(1):1–127, 2009.
[192] Abdel-rahman Mohamed, George E Dahl, Geoffrey Hinton, et al. Acoustic modeling using
deep belief networks. IEEE Transactions on Audio, Speech & Language Processing, 20(1):
14–22, 2012.
[193] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep
recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), pages 6645–6649. IEEE, 2013.
[194] Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. How to construct
deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.
[195] Ian Goodfellow, Honglak Lee, Quoc V Le, Andrew Saxe, and Andrew Y Ng. Measuring
invariances in deep networks. In Advances in Neural Information Processing Systems, pages
646–654, 2009.
[196] Jianshu Chen and Li Deng. A new method for learning deep recurrent neural networks. arXiv
preprint arXiv:1311.6091, 2013.
[197] Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent. Modeling temporal
dependencies in high-dimensional sequences: Application to polyphonic music generation
and transcription. arXiv preprint arXiv:1206.6392, 2, 2012.
[198] Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks
are universal approximators. Neural Networks, 2(5):359–366, 1989.
[199] Tapani Raiko, Harri Valpola, and Yann LeCun. Deep learning made easier by linear transformations
in perceptrons. In Artificial Intelligence and Statistics, pages 924–932, 2012.
[200] Pedro HO Pinheiro and Ronan Collobert. Recurrent convolutional neural networks for scene
labeling. In 31st International Conference on Machine Learning (ICML), 2014.
[201] Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint
arXiv:1308.0850, 2013.
[202] Vladim´ır Boˇza, Broˇna Brejov´a, and Tom´aˇs Vinaˇr. DeepNano: deep recurrent neural networks
for base calling in MinION nanopore reads. PLoS One, 12(6):e0178751, 2017.
[203] Kyunghyun Cho, Bart van Merrienboer, C¸ aglar G¨ulc¸ehre, Fethi Bougares, Holger Schwenk,
and Yoshua Bengio. Learning phrase representations using RNN encoder-decoder for statistical
machine translation. CoRR, abs/1406.1078, 2014.
[204] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural
networks. In Advances in Neural Information Processing Systems, pages 3104–3112, 2014.
[205] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by
jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
[206] Jordan B Pollack. Recursive distributed representations. Artificial Intelligence, 46(1-2):77–
105, 1990.
[207] Richard Socher, Cliff C Lin, Chris Manning, and Andrew Y Ng. Parsing natural scenes and
natural language with recursive neural networks. In Proceedings of the 28th International
Conference on Machine Learning (ICML-11), pages 129–136, 2011.
[208] L´eon Bottou. From machine learning to machine reasoning. Machine Learning, 94(2):133–
149, 2014.
[209] Tsungnan Lin, Bill G Horne, Peter Tino, and C Lee Giles. Learning long-term dependencies
in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7(6):1329–
1338, 1996.
[210] Michael C Mozer. Induction of multiscale temporal structure. In Advances in Neural Information
Processing Systems, pages 275–282, 1992.
[211] Salah El Hihi and Yoshua Bengio. Hierarchical recurrent neural networks for long-term
dependencies. In Advances in Neural Information Processing Systems, pages 493–499, 1996.
[212] Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term memory. Neural Computation, 9
(8):1735–1780, 1997.
[213] Felix A Gers, J¨urgen Schmidhuber, and Fred Cummins. Learning to forget: Continual prediction
with lstm. Neural computation, 12(10):2451–2471, 2000.
[214] Ryan Kiros, Ruslan Salakhutdinov, and Richard S. Zemel. Unifying visual-semantic embeddings
with multimodal neural language models. CoRR, abs/1411.2539, 2014.
[215] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural
image caption generator. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition, pages 3156–3164, 2015.
[216] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov,
Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning, pages 2048–2057,
[217] Alex Graves, Marcus Liwicki, Santiago Fern´andez, Roman Bertolami, Horst Bunke, and
J¨urgen Schmidhuber. A novel connectionist system for unconstrained handwriting recognition.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5):855–868,
[218] Alex Graves and J¨urgen Schmidhuber. Offline handwriting recognition with multidimensional
recurrent neural networks. In Advances in Neural Information Processing Systems,
pages 545–552, 2009.
[219] Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton.
Grammar as a foreign language. In Advances in Neural Information Processing Systems,
pages 2773–2781, 2015.
[220] Alex Graves and J¨urgen Schmidhuber. Framewise phoneme classification with bidirectional
LSTM and other neural network architectures. Neural Networks, 18(5-6):602–610, 2005.
[221] Ahmed Elsheikh, Soumaya Yacout, and Mohamed-Salah Ouali. Bidirectional handshaking
lstm for remaining useful life prediction. Neurocomputing, 323:148 – 156, 2019. ISSN
[222] Albert Zeyer, Patrick Doetsch, Paul Voigtlaender, Ralf Schl¨uter, and Hermann Ney. A
comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech
recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pages 2462–2466. IEEE, 2017.
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