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- Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225-2236.
- Giuliano, G., & Dargay, J. (2006). Car ownership, travel and land use: a comparison of the US and Great Britain. Transportation Research Part A: Policy and Practice, 40(2), 106-124.
- Hand, D., Mannila, M., & Smyth, P. (2001). Principles of Data Mining. United States of America: The MIT Press.
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- Shafique, M. A., & Hato, E. (2015). Use of acceleration data for transportation mode prediction. Transportation, 42(1), 163-188.
- Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R., & Khovanova, N. (2015). Machine learning for predictive modelling based on small data in biomedical engineering. IFAC-PapersOnLine, 48(20), 469-474.
- Shakeel, N., Baig, F., & Saddiq, M. A. (2019). Modeling Commuter's Socio-demographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method. Transport Technic and Technology, 15(2), 1-7.
- Sharifi, F., & Burris, M. W. (2019). Application of machine learning to characterize uneconomical managed lane choice behaviour. Case Studies on Transport Policy, 7(4), 781-789.
- Sperry, B. R., Burris, M., & Woosnam, K. M. (2017). Investigating the impact of high- speed rail equipment visualization on mode choice models: Case study in central Texas. Case Studies on Transport Policy, 5(4), 560-572.
- Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
- United Nations Development Program. (2017). Pakistan National Human Development Report. Pakistan: UNDP.
- Weinberger, R., & Goetzke, F. (2019). Automobile ownership and mode choice: Learned or instrumentally rational?. Travel Behaviour and Society, 16, 153-160.
- Zahabi, S. A. H., Miranda-Moreno, L. F., Patterson, Z., & Barla, P. (2012). Evaluating the effects of land use and strategies for parking and transit supply on mode choice of downtown commuters. Journal of Transport and Land Use, 5(2), 103-119.
- Zaklouta, F., & Stanciulescu, B. (2012). Real-time traffic-sign recognition using tree classifiers. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1507-1514.
- Zhang, B. (2012). Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on Intelligent Transportation Systems, 14(1), 322-332.
- Zhang, R., Yao, E., & Liu, Z. (2017). School travel mode choice in Beijing, China. Journal of Transport Geography, 62, 98-110.
- Zhang, Y., & Ling, C. (2018). A strategy to apply machine learning to small datasets in materials science. Npj Computational Materials, 4(1), 1-8.
- Zhao, C. H., Zhang, B. L., He, J., & Lian, J. (2012). Recognition of driving postures by contourlet transform and random forests. IET Intelligent Transport Systems, 6(2), 161-168.
- Abelson, R. P. (1995). Statistics as Principled Argument. Psychology Press, New York, USA.
- Altares, P.S. et al. (2003). Elementary Statistics: A modern Approach. Rex Book Store Manila, Philippines, p. 13.
- Althoff, T., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, 547(7663), 336-339.
- 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.
- Aziz, A., Nawaz, M. S., Nadeem, M., & Afzal, L. (2018). Examining suitability of the integrated public transport system: A case study of Lahore. Transportation Research Part A: Policy and Practice, 117, 13-25.
- Belgiawan, P. F., Ilahi, A., & Axhausen, K. W. (2019). Influence of pricing on mode choice decision in Jakarta: A random regret minimization model. Case Studies on Transport Policy, 7(1), 87-95.
- Breiman, L. (2001). Random Forests. Machine Learning 45, 5-32.
- Buehler, R. (2011). Determinants of transport mode choice: a comparison of Germany and the USA. Journal of Transport Geography, 19(4), 644-657.
- Cumming, I., Weal, Z., Afzali, R., Rezaei, S., & Idris, A. O. (2019). The impacts of office relocation on commuting mode shift behaviour in the context of Transportation Demand Management (TDM). Case Studies on Transport Policy, 7(2), 346-356.
- Elhenawy, M., Rakha, H. A., & El-Shawarby, I. (2014). Enhanced modeling of driver stop- or-run actions at a yellow indication: Use of historical behavior and machine learning methods. Transportation Research Record, 2423(1), 24-34.
- Ermagun, A., & Samimi, A. (2015). Promoting active transportation modes in school trips. Transport Policy, 37, 203-211.
- Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265-294.
- Field, A. (2009). Discovering statistics using SPSS, Sage Publications Ltd.
- Gao, Y., Chen, X., Li, T., & Chen, F. (2017). Differences in pupils' school commute characteristics and mode choice based on the household registration system in China. Case Studies on Transport Policy, 5(4), 656-661.
- Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225-2236.
- Giuliano, G., & Dargay, J. (2006). Car ownership, travel and land use: a comparison of the US and Great Britain. Transportation Research Part A: Policy and Practice, 40(2), 106-124.
- Hand, D., Mannila, M., & Smyth, P. (2001). Principles of Data Mining. United States of America: The MIT Press.
- Hou, Y., Edara, P., & Sun, C. (2014). Traffic flow forecasting for urban work zones. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1761-1770.
- Hu, H., Xu, J., Shen, Q., Shi, F., & Chen, Y. (2018). Travel mode choices in small cities of China: A case study of Changting. Transportation Research Part D: Transport and Environment, 59, 361-374.
- Jahangiri, A., & Rakha, H. A. (2015). Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data. IEEE Transactions on Intelligent Transportation Systems, 1-12.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Application in R. New York: Springer.
- Lanzini, P., & Khan, S. A. (2017). Shedding light on the psychological and behavioral determinants of travel mode choice: A meta-analysis. Transportation Research Part F: Traffic Psychology and Behaviour, 48, 13- 27.
- Lo, S. H., van Breukelen, G. J., Peters, G. J. Y., & Kok, G. (2016). Commuting travel mode choice among office workers: Comparing an Extended Theory of Planned Behavior model between regions and organizational sectors. Travel Behaviour and Society, 4, 1- 10.
- Montini, L., Rieser-Schüssler, N., Horni, A., & Axhausen, K. W. (2014). Trip purpose identification from GPS tracks. Transportation Research Record, 2405(1), 16-23.
- Pakistan Population Census. (2017). Bureau of Statistic Govt. of Punjab, Pakistan.
- Pucher, J., & Buehler, R. (2006). Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies. Transport Policy, 13(3), 265-279.
- Rasouli, S., & Timmermans, H. J. (2014). Using ensembles of decision trees to predict transport mode choice decisions: Effects on predictive success and uncertainty estimates. European Journal of Transport and Infrastructure Research, 14(4), 412-424.
- Rebollo, J. J., & Balakrishnan, H. (2014). Characterization and prediction of air traffic delays. Transportation Research Part C: Emerging Technologies, 44, 231-241.
- Shafique, M. A., & Hato, E. (2015). Use of acceleration data for transportation mode prediction. Transportation, 42(1), 163-188.
- Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R., & Khovanova, N. (2015). Machine learning for predictive modelling based on small data in biomedical engineering. IFAC-PapersOnLine, 48(20), 469-474.
- Shakeel, N., Baig, F., & Saddiq, M. A. (2019). Modeling Commuter's Socio-demographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method. Transport Technic and Technology, 15(2), 1-7.
- Sharifi, F., & Burris, M. W. (2019). Application of machine learning to characterize uneconomical managed lane choice behaviour. Case Studies on Transport Policy, 7(4), 781-789.
- Sperry, B. R., Burris, M., & Woosnam, K. M. (2017). Investigating the impact of high- speed rail equipment visualization on mode choice models: Case study in central Texas. Case Studies on Transport Policy, 5(4), 560-572.
- Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
- United Nations Development Program. (2017). Pakistan National Human Development Report. Pakistan: UNDP.
- Weinberger, R., & Goetzke, F. (2019). Automobile ownership and mode choice: Learned or instrumentally rational?. Travel Behaviour and Society, 16, 153-160.
- Zahabi, S. A. H., Miranda-Moreno, L. F., Patterson, Z., & Barla, P. (2012). Evaluating the effects of land use and strategies for parking and transit supply on mode choice of downtown commuters. Journal of Transport and Land Use, 5(2), 103-119.
- Zaklouta, F., & Stanciulescu, B. (2012). Real-time traffic-sign recognition using tree classifiers. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1507-1514.
- Zhang, B. (2012). Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on Intelligent Transportation Systems, 14(1), 322-332.
- Zhang, R., Yao, E., & Liu, Z. (2017). School travel mode choice in Beijing, China. Journal of Transport Geography, 62, 98-110.
- Zhang, Y., & Ling, C. (2018). A strategy to apply machine learning to small datasets in materials science. Npj Computational Materials, 4(1), 1-8.
- Zhao, C. H., Zhang, B. L., He, J., & Lian, J. (2012). Recognition of driving postures by contourlet transform and random forests. IET Intelligent Transport Systems, 6(2), 161-168.
Cite this article
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APA : Tariq, F., & Shakeel, N. (2021). Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan. Global Social Sciences Review, VI(III), 106-118. https://doi.org/10.31703/gssr.2021(VI-III).12
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CHICAGO : Tariq, Fariha, and Nabeel Shakeel. 2021. "Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan." Global Social Sciences Review, VI (III): 106-118 doi: 10.31703/gssr.2021(VI-III).12
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HARVARD : TARIQ, F. & SHAKEEL, N. 2021. Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan. Global Social Sciences Review, VI, 106-118.
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MHRA : Tariq, Fariha, and Nabeel Shakeel. 2021. "Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan." Global Social Sciences Review, VI: 106-118
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MLA : Tariq, Fariha, and Nabeel Shakeel. "Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan." Global Social Sciences Review, VI.III (2021): 106-118 Print.
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OXFORD : Tariq, Fariha and Shakeel, Nabeel (2021), "Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan", Global Social Sciences Review, VI (III), 106-118
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TURABIAN : Tariq, Fariha, and Nabeel Shakeel. "Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan." Global Social Sciences Review VI, no. III (2021): 106-118. https://doi.org/10.31703/gssr.2021(VI-III).12