Machine learning applications in activity-travel behaviour research: a review

References

• Abdulazim, T., Abdelgawad, H., Habib, K., & Abdulhai, B. (2013). Using smartphones and sensor technologies to automate collection of travel data. Transportation Research Record: Journal of the Transportation Research Board, 2383, 44–52. doi: 10.3141/2383-06
   Web of Science ®Google Scholar
• Allahviranloo, M., & Recker, W. (2013). Daily activity pattern recognition by using support vector machines with multiple classes. Transportation Research Part B: Methodological, 58, 16–43. doi: 10.1016/j.trb.2013.09.008
   Web of Science ®Google Scholar
• Alpaydin, E. (2010). Introduction to machine learning (2nd edn). Cambridge: The MIT Press. doi: 10.1007/978-1-62703-748-8_7
   Google Scholar
• Arentze, T., Hofman, F., & Timmermans, H. (2003). Reinduction of albatross decision rules with pooled activity-travel diary data and an extended set of land use and cost-related condition states. Transportation Research Record: Journal of the Transportation Research Board, 1831, 230–239. doi: 10.3141/1831-26
   Google Scholar
• Arentze, T., Hofman, F., van Mourik, H., & Timmermans, H. (2002). Spatial transferability of the albatross model system: Empirical evidence from two case studies. Transportation Research Record: Journal of the Transportation Research Board, 1805(1), 1–7. doi: 10.3141/1805-01
   Web of Science ®Google Scholar
• Arentze, T., & Timmermans, H. (2000). ALBATROSS: A learning based transportation oriented simulation system. Eindhoven: Eindhoven University of Technology.
   Google Scholar
• Arentze, T., & Timmermans, H. J. P. (2004). A learning-based transportation oriented simulation system. Transportation Research Part B: Methodological, 38(7), 613–633.
   Web of Science ®Google Scholar
• Arentze, T. A., & Timmermans, H. J. P. (2005). Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics. Transportation, 32, 321–340.
   Web of Science ®Google Scholar
• Assi, K. J., Nahiduzzaman, K. M., Ratrout, N. T., & Aldosary, A. S. (2018). Mode choice behavior of high school goers: Evaluating logistic regression and MLP neural networks. Case Studies on Transport Policy, 6(2), 225–230. doi: 10.1016/j.cstp.2018.04.006
   Web of Science ®Google Scholar
• Beckman, J. D., & Goulias, K. G. (2008). Immigration, residential location, car ownership, and commuting behavior: A multivariate latent class analysis from California. Transportation, 35(5), 655–671. doi: 10.1007/s11116-008-9172-x
   Web of Science ®Google Scholar
• Bishop, C. M. (2006). Pattern recognition and Machine learning. New York: Springer.
   Google Scholar
• Bolbol, A., Cheng, T., Tsapakis, I., & Haworth, J. (2012). Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems, 36(6), 526–537. doi: 10.1016/j.compenvurbsys.2012.06.001
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Cantarella, G. E., & de Luca, S. (2005). Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models. Transportation Research Part C: Emerging Technologies, 13(2), 121–155. doi: 10.1016/j.trc.2005.04.002
   Web of Science ®Google Scholar
• Charypar, D., & Nagel, K. (2005). Generating complete all-day activity plans with genetic algorithms. Transportation, 32(4), 369–397. doi: 10.1007/s11116-004-8287-y
   Web of Science ®Google Scholar
• Chen, N., & Akar, G. (2016). Effects of neighborhood types & socio-demographics on activity space. Journal of Transport Geography, 54, 112–121. doi: 10.1016/j.jtrangeo.2016.05.017
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. doi: 10.1023/A:1022627411411
   Web of Science ®Google Scholar
• Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314. doi: 10.1007/BF02551274
   Google Scholar
• Doherty, S. T., & Mohammadian, A. (2003). Application of artificial neural network models to activity scheduling time horizon. Transportation Research Record: Journal of the Transportation Research Board, 1854(1), 43–49. doi: 10.3141/1854-05
   Google Scholar
• Feng, T., & Timmermans, H. J. P. (2016). Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data. Transportation Planning and Technology, 39(2), 180–194. doi:10.1080/03081060.2015.1127540
   Web of Science ®Google Scholar
• Ghasri, M., Hossein Rashidi, T., & Waller, S. T. T. (2017). Developing a disaggregate travel demand system of models using data mining techniques. Transportation Research Part A: Policy and Practice, 105, 138–153. doi: 10.1016/j.tra.2017.08.020
   Web of Science ®Google Scholar
• Gogate, V., Dechter, R., Bidyuk, B., Rindt, C., & Marca, J. (2005). Modeling transportation routines using hybrid dynamic mixed networks. In Proceedings of the conference on uncertainty in artificial intelligence. Edinburgh, Scotland. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary? doi=10.1.1.76.7561
   Google Scholar
• Golshani, N., Shabanpour, R., Mahmoudifard, S. M., Derrible, S., & Mohammadian, A. (2018). Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model. Travel Behaviour and Society, 10, 21–32. doi: 10.1016/j.tbs.2017.09.003
   Web of Science ®Google Scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
   Google Scholar
• Hafezi, M. H., Liu, L., & Millward, H. (2017). A time-use activity-pattern recognition model for activity-based travel demand modeling. Transportation, 1–26. doi: 10.1007/s11116-017-9840-9
   Web of Science ®Google Scholar
• Hagenauer, J., & Helbich, M. (2017). A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, 273–282. doi:10.1016/j.eswa.2017.01.057
   Web of Science ®Google Scholar
• Hensher, D. A., & Ton, T. T. (2000). A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice. Transportation Research Part E: Logistics and Transportation Review, 36(3), 155–172. doi: 10.1016/S1366-5545(99)00030-7
   Web of Science ®Google Scholar
• Hussain, H. D., Mohammed, A. M., Salman, A. D., Rahmat, R. A. B. O. K., & Borhan, M. N. (2017). Analysis of transportation mode choice using a comparison of artificial neural network and multinomial logit models. ARPN Journal of Engineering and Applied Sciences, 12(5), 1483–1493.
   Google Scholar
• Janssens, D., Wets, G., Brijs, T., Vanhoof, K., Arentze, T., & Timmermans, H. (2006). Integrating Bayesian networks and decision trees in a sequential rule-based transportation model. European Journal of Operational Research, 175(1), 16–34. doi: 10.1016/j.ejor.2005.03.022
   Web of Science ®Google Scholar
• Janssens, Davy, Wets, G., Brijs, T., Vanhoof, K., Timmermans, H., & Arentze, T. (2004). Improving the performance of a multi-agent Rule-based model for activity. In 83rd Annual Meeting of the transportation research Board (pp. 1–17). Washingto, DC.
   Google Scholar
• Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399. doi: 10.1016/j.trc.2010.10.004
   Web of Science ®Google Scholar
• Kato, K., Matsumoto, S., & Sano, K. (2002). Microsimulation for commuters’ mode and discretionary activities by using neural networks. Traffic And Transportation Studies (2002), 1290–1297. doi: 10.1061/40630(255)178
   Google Scholar
• Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine learning for predictive data analytics: Algorithms, worked examples, and case studies (First). Cambridge: MIT Press.
   Google Scholar
• Kingston, G. B., Maier, H. R., & Lambert, M. F. (2006). A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological prediction. Mathematical and Computer Modelling, 44(5–6), 499–512. doi: 10.1016/j.mcm.2006.01.008
   Google Scholar
• Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: The MIT Press.
   Google Scholar
• Li, X., Li, H., & Xu, X. (2018). A Bayesian network modeling for departure time choice: A case study of Beijing subway. Promet – Traffic & Transportation, 30(5), 579–587.
   Web of Science ®Google Scholar
• Li, Z., Wang, W., Yang, C., & Ragland, D. R. (2013). Bicycle commuting market analysis using attitudinal market segmentation approach. Transportation Research Part A: Policy and Practice, 47, 56–68. doi: 10.1016/j.tra.2012.10.017
   Web of Science ®Google Scholar
• Lin, H. Z., Lo, H. P., & Chen, X. J. (2009). Lifestyle classifications with and without activity-travel patterns. Transportation Research Part A: Policy and Practice, 43(6), 626–638. doi: 10.1016/j.tra.2009.04.002
   Web of Science ®Google Scholar
• Lindner, A., Pitombo, C. S., & Cunha, A. L. (2017). Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data. Travel Behaviour and Society, 6, 100–109. doi:10.1016/j.tbs.2016.08.003
   Web of Science ®Google Scholar
• Ma, T. (2015). Bayesian networks for multimodal mode choice behavior modelling: A case study for the cross border workers of Luxembourg. Transportation Research Procedia, 10, 870–880. doi: 10.1016/j.trpro.2015.09.040
   Google Scholar
• Ma, T., Chow, J. Y. J., & Xu, J. (2017). Causal structure learning for travel mode choice using structural restrictions and model averaging algorithm. Transportmetrica A: Transport Science, 13(4), 299–325. doi: 10.1080/23249935.2016.1265019
   Web of Science ®Google Scholar
• Ma, T. Y., & Klein, S. (2018). Bayesian networks for constrained location choice modeling using structural restrictions and model averaging. European Journal of Transport and Infrastructure Research, 18(1), 91–111.
   Web of Science ®Google Scholar
• Ma, X., Wu, Y. J., Wang, Y., Chen, F., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies, 36, 1–12. doi: 10.1016/j.trc.2013.07.010
   Web of Science ®Google Scholar
• Mäenpää, H., Lobov, A., & Martinez Lastra, J. L. (2017). Travel mode estimation for multi-modal journey planner. Transportation Research Part C: Emerging Technologies, 82, 273–289. doi:10.1016/j.trc.2017.06.021
   Web of Science ®Google Scholar
• Miller, E. J., & Roorda, M. J. (2003). Prototype model of household activity-travel scheduling. Transportation Research Record: Journal of the Transportation Research Board, 1831(1), 114–121. doi: 10.3141/1831-13
   Google Scholar
• Mitchell, T. M. (1997). Machine learning. New York: McGraw Hill.
   Google Scholar
• Mohammadian, A., & Miller, E. J. (2002). Nested logit models and artificial neural networks for predicting household automobile choices: Comparison of performance. Transportation Research Record: Journal of the Transportation Research Board, 1807(1), 92–100. doi: 10.3141/1807-12
   Web of Science ®Google Scholar
• Mohammadian, A., & Zhang, Y. (2007). Investigating transferability of national household travel survey data. Transportation Research Record: Journal of the Transportation Research Board, 1993, 67–79. doi: 10.3141/1993-10
   Web of Science ®Google Scholar
• Moons, E., Wets, G., & Aerts, M. (2007). Nonlinear models for determining mode choice. Progress in Artificial Intelligence, 4874, 183–194. doi: 10.1007/978-3-540-77002-2_16
   Google Scholar
• Moons, E., Wets, G. P. M., Aerts, M., Arentze, T., & Timmermans, H. J. P. (2005). The impact of simplification in a sequential rule-based model of activity-scheduling behavior. Environment and Planning A: Economy and Space, 37(3), 551–568. doi: 10.1068/a36167
   Web of Science ®Google Scholar
• Mozolin, M., Thill, J.-C., & Lynn Usery, E. (2000). Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation. Transportation Research Part B: Methodological, 34(1), 53–73. doi: 10.1016/S0191-2615(99)00014-4
   Web of Science ®Google Scholar
• Omrani, H., Charif, O., Gerber, Philippe, Awasthi, A., & Trigano, P. (2013). Prediction of individual travel mode with evidential neural network model. Transportation Research Record: Journal of the Transportation Research Board, 2399(1), 1–8. doi: 10.3141/2399-01
   Web of Science ®Google Scholar
• Paredes, M., Hemberg, E., O’Reilly, U. -M., & Zegras, C. (2017). Machine learning or discrete choice models for car ownership demand estimation and prediction?. 5th IEEE International conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - proceedings (pp. 780–785). Napoli: IEEE.
   Google Scholar
• Pirra, M., & Diana, M. (2016). Classification of tours in the U.S. National household travel survey through clustering techniques. Journal of Transportation Engineering, 142(6), 04016021. doi: 10.1061/(ASCE)TE.1943-5436.0000845
   Google Scholar
• Pitombo, C. S., Bertocini, B. V., & Kawamoto, E. (2008). An application of exploratory multivariate data analysis techniques in a peer study of land use influence on individual destination choices. In Proceedings of the 11th IEEE international conference on computational science and engineering, CSE Workshops 2008 (pp. 59–64). doi: 10.1109/CSEW.2008.20
   Google Scholar
• Pitombo, C. S., Kawamoto, E., & Sousa, A. J. (2011). An exploratory analysis of relationships between socioeconomic, land use, activity participation variables and travel patterns. Transport Policy, 18(2), 347–357. doi: 10.1016/j.tranpol.2010.10.010
   Web of Science ®Google Scholar
• Přibyl, O., & Goulias, K. (2005). Simulation of daily activity patterns incorporating interactions within households: Algorithm overview and performance. Transportation Research Record: Journal of the Transportation Research Board, 1926, 135–141. doi: 10.3141/1926-16
   Google Scholar
• Pronello, C., & Camusso, C. (2011). Travellers’ profiles definition using statistical multivariate analysis of attitudinal variables. Journal of Transport Geography, 19(6), 1294–1308. doi: 10.1016/j.jtrangeo.2011.06.009
   Web of Science ®Google Scholar
• Rakha, T., Rose, C. M., & Reinhart, C. F. (2014). A framework for modeling occupancy schedules and local trips based on activity based surveys. In 2014 ASHRAE/IBPSA-USA building simulation conference (pp. 433–440). Atalanta, GA.
   Google Scholar
• Rasouli, S., & Timmermans, H. (2014). Activity-based models of travel demand: Promises, progress and prospects. International Journal of Urban Sciences, 18(1), 31–60. doi: 10.1080/12265934.2013.835118
   Google Scholar
• Rodrigues, J. G. P., Pereira, J. P., & Aguiar, A. (2017). Impact of crowdsourced data quality on travel pattern estimation. Proceedings of the first ACM workshop on Mobile Crowdsensing Systems and Applications - CrowdSenSys ’17 (pp. 38–43). New York, NY: ACM.
   Google Scholar
• Rojas, R. (1996). Neural networks: A systematic introduction. Berlin: Springer-Verlag.
   Google Scholar
• Roorda, M., Miller, E. J., & Kruchten, N. (2006). Incorporating within-household interactions into mode choice model with genetic algorithm for parameter estimation. Transportation Research Record. doi:10.3141/1985-19
   Google Scholar
• Russell, S. J., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed). Upper Saddle River, NJ: Pearson.
   Google Scholar
• Shen, C.-W., & Kuo, P.-F. (2015). Variable selection of travel demand models for paratransit service: A data mining approach. In CICTP 2015 (pp. 1568–1579). Reston, VA: American Society of Civil Engineers. doi: 10.1061/9780784479292.144
   Google Scholar
• Shmueli, D., Salomon, I., & Shefer, D. (1996). Neural network analysis of travel behavior: Evaluating tools for prediction. Transportation Research Part C: Emerging Technologies, 4(3), 151–166. doi: 10.1016/S0968-090X(96)00007-1
   Web of Science ®Google Scholar
• Stenneth, L., Wolfson, O., Yu, P. S., & Xu, B. (2011). Transportation mode detection using mobile phones and GIS information. Proceedings of the 19th ACM SIGSPATIAL International conference on advances in geographic information systems (pp. 54–63). Chicago: ACM Press.
   Google Scholar
• Sun, B., & Park, B. B. (2017). Route choice modeling with support vector machine. Transportation Research Procedia, 25, 1806–1814. doi:10.1016/j.trpro.2017.05.151
   Google Scholar
• Tang, L., Xiong, C., & Zhang, L. (2018). Spatial transferability of neural network models in travel demand modeling. Journal of Computing in Civil Engineering, 32, 3. doi: 10.1061/(ASCE)CP.1943-5487.0000752
   Web of Science ®Google Scholar
• Tavares, A. R., & Bazzan, A. L. C. (2012). Reinforcement learning for route choice in an abstract traffic scenario. In VI Workshop-Escola de Sistemas de Agentes, seus Ambientes e aplicações (WESAAC) (pp. 141–153.
   Google Scholar
• Thill, J.-C., & Wheeler, A. (2000). Tree induction of spatial choice behavior. Transportation Research Record: Journal of the Transportation Research Board, 1719(1), 250–258. doi: 10.3141/1719-33
   Google Scholar
• Vanhulsel, M., Janssens, D., Wets, G., & Vanhoof, K. (2009). Simulation of sequential data: An enhanced reinforcement learning approach. Expert Systems with Applications, 36(4), 8032–8039. doi: 10.1016/j.eswa.2008.10.056
   Web of Science ®Google Scholar
• Verhoeven, M., Arentze, T., Timmermans, H., & van der Waerden, P. (2007). Simulating the influence of life trajectory events on transport mode behavior in an agent-based system. 2007 IEEE Intelligent Transportation Systems Conference, 107–112. doi: 10.1109/ITSC.2007.4357815
   Google Scholar
• Wang, B., Gao, L., & Juan, Z. (2018). 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. doi:10.1109/TITS.2017.2723523
   Web of Science ®Google Scholar
• Wang, L., Ma, W., Fan, Y., & Zuo, Z. (2017). Trip chain extraction using smartphone-collected trajectory data. Transportmetrica B, 1–20. doi:10.1080/21680566.2017.1386599
   Web of Science ®Google Scholar
• Wang, Q., Sun, H., & Zhang, Q. (2017). A Bayesian network model on the public bicycle choice behavior of residents: A case study of Xi ’ an. Mathematical Problems in Engineering, 2017(4), 1–13.
   Google Scholar
• Watkins, C. J. C. H., & Dayan, P. (1992). Technical note: Q-learning. Machine Learning, 8(3/4), 279–292. doi: 10.1023/A:1022676722315
   Web of Science ®Google Scholar
• Wei, F., Ma, S., & Jia, N. (2014). A Day-to-Day route choice model based on reinforcement learning. Mathematical Problems in Engineering, 2014, 1–19. doi: 10.1155/2014/646548
   Web of Science ®Google Scholar
• Weng, J., Tu, Q., Yuan, R., Lin, P., & Chen, Z. (2018). Modeling mode choice Behaviors for Public transport commuters in Beijing. Journal of Urban Planning and Development, 144(3), 1–9. doi: 10.1061/(ASCE)UP.1943-5444.0000459
   Web of Science ®Google Scholar
• Wets, G., Vanhoof, K., Arentze, T., & Timmermans, H. (2000). Identifying decision structures underlying activity patterns: An exploration of data mining algorithms. Transportation Research Record: Journal of the Transportation Research Board, 1718(00), 1–9. doi: 10.3141/1718-01
   Web of Science ®Google Scholar
• Witayangkurn, A., Horanont, T., Ono, N., Sekimoto, Y., & Shibasaki, R. (2013). Trip reconstruction and transportation mode extraction on Low data rate GPS data from Mobile Phone. In S. Geertman, J. Stillwell, & F. Toppen (Eds.), Proceedings of the international conference on computers in Urban planning and Urban management planning support systems for sustainable Urban development (pp. 1–19). Utrecht: Springer-Verlag.
   Google Scholar
• Witten, I. H., Frank, E., & Hall, M.A. (2011). Data mining: Practical Machine learning tools and techniques (third). Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Wolpert, D. H. (1996). The lack of A Priori Distinctions between learning algorithms. Neural Computation, 8(7), 1341–1390. doi: 10.1162/neco.1996.8.7.1341
   Web of Science ®Google Scholar
• Xie, C., Lu, J., & Parkany, E. (2003). Work travel mode choice modeling with data mining: Decision trees and neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1854(03), 50–61. doi: 10.3141/1854-06
   Google Scholar
• Yamamoto, T., Kitamura, R., & Fujii, J. (2002). Drivers’ route choice behavior: Analysis by data mining algorithms. Transportation Research Record: Journal of the Transportation Research Board, 1807(02), 59–66. doi: 10.3141/1807-08
   Google Scholar
• Yang, S., Deng, W., Deng, Q., & Fu, P. (2016). The research on prediction models for urban family member trip generation. KSCE Journal of Civil Engineering, 20(7), 2910–2919. doi: 10.1007/s12205-016-0806-9
   Web of Science ®Google Scholar
• Yang, M., Tang, D., Ding, H., Wang, W., Luo, T., & Luo, S. (2014). Evaluating staggered working hours using a multi-agent-based Q-learning model. Transport, 29(3), 296–306. doi: 10.3846/16484142.2014.953997
   Web of Science ®Google Scholar
• Yang, M., Yang, Y., Wang, W., Ding, H., & Chen, J. (2014). Multiagent-based simulation of temporal-spatial characteristics of activity-travel patterns using interactive reinforcement learning. Mathematical Problems in Engineering, 2014, 1–11. doi: 10.1155/2014/951367
   Web of Science ®Google Scholar
• Yang, F., Yao, Z., Cheng, Y., Ran, B., & Yang, D. (2016). Multimode trip information detection using personal trajectory data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 20(5), 449–460. doi: 10.1080/15472450.2016.1151791
   Web of Science ®Google Scholar
• Yeh, I. C., & Cheng, W. L. (2010). First and second order sensitivity analysis of MLP. Neurocomputing, 73(10–12), 2225–2233. doi: 10.1016/j.neucom.2010.01.011
   Web of Science ®Google Scholar
• Zhang, Y., & Xie, Y. (2008). Travel mode choice modeling with support vector machines. Transportation Research Record: Journal of the Transportation Research Board, 2076(1), 141–150. doi: 10.3141/2076-16
   Web of Science ®Google Scholar
• Zhang, z., & Xu, J.-M. (2005). A dynamic route guidance arithmetic based on reinforcement learning. In Proceedings of the fourth international conference on machine learning and cybernetics (pp. 3607–3611). Guangzhou: Springer. doi: 10.1109/icmlc.2005.1527567
   Google Scholar
• Zhao, D., & Shao, C. (2010). Application of wavelet neural networks for trip chaining recognition. In Proceedings - 2010 6th international conference on natural computation, ICNC 2010 (pp. 172–175). Hiroshima: IEEE. doi: 10.1109/ICNC.2010.5582979
   Google Scholar
• Zhou, X., Yu, W., & Sullivan, W. C. (2016). Making pervasive sensing possible: Effective travel mode sensing based on smartphones. Computers, Environment and Urban Systems, 58, 52–59. doi: 10.1016/j.compenvurbsys.2016.03.001
   Web of Science ®Google Scholar
• Zhu, Z., Chen, X., Xiong, C., & Zhang, L. (2018). A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice. Transportation, 45(5), 1499–1522. doi: 10.1007/s11116-017-9770-6
   Web of Science ®Google Scholar
• Zhu, X., Li, J., Liu, Z., Wang, S., & Yang, F. (2016). Learning transportation annotated mobility profiles from GPS data for context-aware mobile services. In Proceedings - 2016 IEEE International conference on services computing, SCC (pp. 475–482). San Francisco: IEEE.
   Google Scholar