[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, 2017. [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, 2016. [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, 2004. [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, 2001. [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, 2009. [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, 2011. [43] Tao Feng and Harry JP Timmermans. Transportation mode recognition using GPS and accelerometer data. Transportation Research Part C: Emerging Technologies, 37:118–130, 2013. [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, 2019. [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 https://www.stat.berkeley.edu/˜breiman/RandomForests/cc_home. 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, 2016. [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, 2011. [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, 2016. [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, 1958. [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/ publication/geolife-gps-trajectory-dataset-user-guide/. [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, 2015. [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 978-1-4615-5529-2. [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, 2018. [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: 2019-12-01. [180] Uber’s Hexagonal Hierarchical Spatial Index. https://eng.uber.com/h3/. Accessed: 2019-12-01. [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, 2009. [186] Sudarshan S Chawathe. Beacon placement for indoor localization using Bluetooth. In 11th International IEEE Conference on Intelligent Transportation Systems, pages 980–985. IEEE, 2008. [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, 2015. [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, 2009. [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 0925-2312. [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.