پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,573 |
| تعداد مقالات | 71,037 |
| تعداد مشاهده مقاله | 125,523,905 |
| تعداد دریافت فایل اصل مقاله | 98,783,972 |
Predicting the Fluctuation of Travel Time Reliability as a Result of Congestion Variations by Bagging-Based Regressors | ||
| Civil Engineering Infrastructures Journal | ||
| مقاله 5، دوره 57، شماره 1، شهریور 2024، صفحه 85-101 اصل مقاله (633.99 K) | ||
| نوع مقاله: Research Papers | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2023.349853.1878 | ||
| نویسندگان | ||
| Shahriar Afandizadeh1؛ Navid Amoei Khorshidi2؛ Hamid Mirzahossein* 3؛ Samim Shakoori2 | ||
| 1Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
| 2M.Sc., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
| 3Associate Professor, Department of Civil – Transportation Planning, Imam Khomeini International University, Qazvin, Iran. | ||
| چکیده | ||
| Travel time reliability affects the behavior of passengers in private or public transportation and can be seen as an important factor in the context of freight transportation. The main cause of travel time oscillation, known as travel time reliability, is congestion. Congestion is classified into two categories: recurring and nonrecurring. Recurring congestion, which is the topic of this study, is formed when supply surpasses capacity. Peak periods are good examples of recurring congestion. In this paper, by utilizing different bagging regressor methods, the effect of speed flow reduction, compared to Free Flow Speed (FFS) in terms of congestion was studied on the Planning Time Index (PTI) on a section of Interstate 64 in the United States (US). Then, by analyzing PTI changes based on congestion variation, it was revealed that when speed reduction surpasses 10%, travel time leaves its reliability. Also, when the congestion is somewhere around 0.7 to 0.75, the unreliability becomes severe. These findings were directly extracted from scatter plots drawn by bagging and bootstrapping samples which were used to improve the accuracy of PTI prediction. | ||
| کلیدواژهها | ||
| Bagging Regressor؛ Congestion؛ Machine Learning؛ Peak Period؛ Planning Time Index (PTI)؛ Travel Time Reliability | ||
| مراجع | ||
|
Abdulhafedh, A. (2022). “Comparison between common statistical modeling techniques used in research, including: Discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest”, Open Access Library Journal, 9(2), 1-19, https://doi.org/10.4236/oalib.1108414.
Adhistian, P. and Wibowo, P. (2022). “Prediction of subscriber VoLTE using Machine Learning and Deep Learning”, International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, https://doi.org/10.1109/ISITIA56226.2022.9855362.
Afandizadeh Zargari, Sh., Khorshidi, N.A., Mirzahossein, H. and Heidari, H. (2023). “Analyzing the effects of congestion on planning time index-grey models Vs random forest regression”, International Journal of Transportation Science and Technology, 12(2), 578-593, https://doi.org/10.1016/j.ijtst.2022.05.008.
Almejrb, R.M., Sallabi, O.M. and Mohamed, A.A. (2022). “Applying C atboost regression model for prediction of house prices”, International Conference on Engineering & MIS (ICEMIS), Istanbul, Turkey, https://doi.org/10.1109/ICEMIS56295.2022.9914345.
Ara, A., Maia, M., Macêdo, S. and Louzada, F. (2020). “Random machines regression approach: An ensemble support vector regression model with free kernel choice”, arXiv preprint arXiv, 2003, 12643, https://doi.org/10.48550/arXiv.2003.12643.
Arias-Castro, E. (2022). Principles of statistical analysis: Learning from randomized experiments, Cambridge University Press, England.
Chakrabarti, A. and Ghosh, J.K. (2011). “AIC, BIC and recent advances in model selection”, In: P.S. Bandyopadhyay, and M.R. Forster (Eds.), Philosophy of Statistics, (pp. 583-605), Netherlands: Elsevier, https://doi.org/10.1016/B978-0-444-51862-0.50018-6.
Chen, A., Ji, Z. and Recker, W. (2002). “Travel time reliability with risk-sensitive travelers”, Transportation Research Record, 1783(1), 27-33, https://doi.org/10.3141/1783-04.
Chen, M.-Y., Chiang, H.-S., and Yang, K.-J. (2022). “Constructing cooperative intelligent transport systems for travel time prediction with deep learning approaches”, IEEE Transactions on Intelligent Transportation Systems, 23(9), 16590-16599, https://doi.org/10.1109/TITS.2022.3148269.
Chen, Z. and Fan, W. (2019). “Data analytics approach for travel time reliability pattern analysis and prediction”, Journal of Modern Transportation, 27(4), 250-265, https://doi.org/10.1007/s40534-019-00195-6.
Chen, Z. and Fan, W.D. (2020). “Analyzing travel time distribution based on different travel time reliability patterns using probe vehicle data”, International Journal of Transportation Science and Technology, 9(1), 64-75, https://doi.org/10.1016/j.ijtst.2019.10.001.
Emam, E.B. and Al-Deek, H. (2006). “Using real-life dual-loop detector data to develop new methodology for estimating freeway travel time reliability”, Transportation Research Record, 1959(1), 140-150, https://doi.org/10.1177/0361198106195900116.
Gacto, M.J., Soto-Hidalgo, J.M., Alcalá-Fdez, J. and Alcalá, R. (2019). “Experimental study on 164 algorithms available in software tools for solving standard non-linear regression problems”, IEEE Access, 7, 108916-108939, https://doi.org/10.1109/ACCESS.2019.2933261
Gaver Jr, D.P. (1968). “Headstart strategies for combating congestion”, Transportation Science, 2(2), 172-181, https://doi.org/10.1287/trsc.2.2.172.
Ghader, S., Darzi, A. and Zhang, L. (2019). “Modeling effects of travel time reliability on mode choice using cumulative prospect theory”, Transportation Research Part C: Emerging Technologies, 108, 245-254, https://doi.org/10.1016/j.trc.2019.09.014.
Gu, Y., Fu, X., Liu, Z., Xu, X. and Chen, A. (2020). “Performance of transportation network under perturbations: Reliability, vulnerability, and resilience”, Transportation Research Part E: Logistics and Transportation Review, 133, 101809, https://doi.org/10.1016/j.tre.2019.11.003.
Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H. (2009). The elements of statistical learning: data mining, inference, and prediction, Springer, New York.
Hojati, A.T., Ferreira, L., Washington, S., Charles, P. and Shobeirinejad, A. (2016). “Reprint of: Modelling the impact of traffic incidents on travel time reliability”, Transportation research part C: emerging technologies, 70, 86-97, https://doi.org/10.1016/j.trc.2015.11.017.
Hoseinzadeh, N., Gu, Y., Han, L.D., Brakewood, C. and Freeze, P.B. (2021). “Estimating freeway level-of-service using crowdsourced data”, Informatics, 8(1), 17, https://doi.org/10.3390/informatics8010017.
Iida, Y. (1999). “Basic concepts and future directions of road network reliability analysis”, Journal of advanced transportation, 33(2), 125-134, https://doi.org/10.1002/atr.5670330203.
INRIX website: https://inrix.com/
Kadiyala, A. and Kumar, A. (2018). “Applications of python to evaluate the performance of bagging methods”, Environmental Progress & Sustainable Energy, 37(5), 1555-1559, https://doi.org/10.1002/ep.13018.
Kang, L. and Hansen, M. (2021). “Quantile regression-based estimation of dynamic statistical contingency fuel”, Transportation Science, 55(1), 257-273, https://doi.org/10.1287/trsc.2020.0997.
Khan, P.W., Park, S.-J., Lee, S.-J. and Byun, Y.-C. (2022). “Electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor”, Journal of Advanced Transportation, 2022, 8062932, https://doi.org/10.1155/2022/8062932.
Li, H., He, F., Lin, X., Wang, Y. and Li, M. (2019). “Travel time reliability measure based on predictability using the Lempel-Ziv algorithm”, Transportation Research Part C: Emerging Technologies, 101, 161-180, https://doi.org/10.1016/j.trc.2019.02.014.
Liu, H.X., Recker, W. and Chen, A. (2004). “Uncovering the contribution of travel time reliability to dynamic route choice using real-time loop data”, Transportation Research Part A: Policy and Practice, 38(6), 435-453, https://doi.org/10.1016/j.tra.2004.03.003.
Lyman, K. and Bertini, R.L. (2008). “Using travel time reliability measures to improve regional transportation planning and operations”, Transportation Research Record, 2046(1), 1-10, https://doi.org/10.3141/2046-01.
Mahmassani, H.S., Kim, J., Chen, Y., Stogios, Y., Brijmohan, A. and Vovsha, P. (2014). Incorporating reliability performance measures into operations and planning modeling tools, Transportation Research Board, Washington, DC.
Mastelini, S.M., Nakano, F.K., Vens, C. and de Leon Ferreira, A.C.P. (2022). “Online extra trees regressor”, IEEE Transactions on Neural Networks and Learning Systems”, 34(10), 6755-6767, https://doi.org/10.1109/TNNLS.2022.3212859.
Mazloumi, R., Abazari, S.R., Nafarieh, F., Aghsami, A. and Jolai, F. (2022). “Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms”, Neural Computing and Applications, 34(17), 14729-14743, https://doi.org/10.1007/s00521-022-07325-y.
Meng, M., Toan, T.D., Wong, Y.D. and Lam, S.H. (2022). “Short-term travel-time prediction using Support Vector Machine and Nearest Neighbor method”, Transportation Research Record, 2676(6), 353-365, https://doi.org/10.1177/03611981221074371.
Moghaddam, Z.R., Jeihani, M., Peeta, S. and Banerjee, S. (2019). “Comprehending the roles of traveler perception of travel time reliability on route choice behavior”, Travel Behaviour and Society, 16, 13-22, https://doi.org/10.1016/j.tbs.2019.03.002.
National Academies of Sciences, Engineering, and Medicine. (2013). Evaluating alternative operations strategies to improve travel time reliability, The National Academies Press, Washington, DC.
Nohekhan, A., Samimi, A. and Zahedian, S. (2022). “Spatial transferability of a daily activity type and duration MDCEV model”, Civil Engineering Infrastructures Journal, 55(1), 161-181, https://doi.org/10.22059/CEIJ.2021.313889.1721.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J. (2011). “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, 12, 2825-2830.
Saedi, R., Saeedmanesh, M., Zockaie, A., Saberi, M., Geroliminis, N. and Mahmassani, H.S. (2020). “Estimating network travel time reliability with network partitioning”, Transportation Research Part C: Emerging Technologies, 112, 46-61, https://doi.org/10.1016/j.trc.2020.01.013.
Samal, S.R. and Das, A.K. (2020). “Evaluation of traffic congestion parameters under heterogeneous traffic condition: A case study on Bhubaneswar City”, In: Transportation Research: Proceedings of CTRG 2017, (pp. 675-684), Springer Singapore, https://doi.org/10.1007/978-981-32-9042-6_53
Sarang, P. (2023). “Ensemble: Bagging and boosting: Improving decision tree performance by ensemble methods”, In: Thinking Data Science: A Data Science Practitioner’s Guide, (pp. 97-129), New York: Springer.
Shabbir, M., Chand, S. and Iqbal, F. (2022). “Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors”, Communications in Statistics-Simulation and Computation. 1-15, https://doi.org/10.1080/03610918.2022.2109675.
Szafranski, D. and Duan, B. (2022). “A workflow to integrate numerical simulation, machine learning regression and Bayesian inversion for induced seismicity study: Principles and a case study”, Pure and Applied Geophysics, 179(10), 3543-3568, https://doi.org/10.1007/s00024-022-03140-7.
Udayanga, S., Kato, T., Kojima, A. and Kubota, H. (2022). “Incorporating crowdsourced travel time data in congestion monitoring”, Journal of the Eastern Asia Society for Transportation Studies, 14, 246-263, https://doi.org/10.11175/easts.14.246.
Wang, Z., Goodchild, A. and McCormack, E. (2017). “A methodology for forecasting freeway travel time reliability using GPS data”, Transportation Research Procedia, 25, 842-852, https://doi.org/10.1016/j.trpro.2017.05.461.
Yazici, M.A., Kamga, C. and Mouskos, K.C. (2012). “Analysis of travel time reliability in New York city based on day-of-week and time-of-day periods”, Transportation Research Record, 2308(1), 83-95, https://doi.org/10.3141/2308-09.
Zang, Z., Xu, X., Yang, C., and Chen, A. (2018). “A closed-form estimation of the travel time percentile function for characterizing travel time reliability”, Transportation Research Part B: Methodological, 118, 228-247, https://doi.org/10.1016/j.trb.2018.10.012.
Zegeer, J.D., Nevers, B. and Kittelson, W. (2014). A framework for improving travel time reliability, Transportation Research Board, Washington, DC.
Zhan, C., Zheng, Y., Zhang, H. and Wen, Q. (2021). Random-forest-bagging broad learning system with applications for covid-19 pandemic”, IEEE Internet of Things Journal, 8(21), 15906-15918, https://doi.org/10.1109/JIOT.2021.3066575.
Zhang, X. and Chen, M. (2019). “Quantifying the impact of weather events on travel time and reliability”, Journal of Advanced Transportation, 2019, 8203081, https://doi.org/10.1155/2019/8203081.
Zhang, X., Zhao, M., Appiah, J. and Fontaine, M.D. (2021). “Improving interstate freeway travel time reliability analysis by clustering travel time distributions”, Transportation Research Record, 2675(10), 566-577, https://doi.org/10.1177/03611981211012002.
Zheng, F., Li, J., Van Zuylen, H., Liu, X. and Yang, H. (2018). “Urban travel time reliability at different traffic conditions”, Journal of Intelligent Transportation Systems, 22(2), 106-120, https://doi.org/10.1080/15472450.2017.1412829.
Zhou, Z.-H. (2012). Ensemble methods: Foundations and algorithms, CRC press, Florida.
Zhu, Z., Mardan, A., Zhu, S. and Yang, H. (2021). “Capturing the interaction between travel time reliability and route choice behavior based on the generalized Bayesian traffic model”, Transportation Research Part B: Methodological, 143, 48-64, https://doi.org/10.1016/j.trb.2020.11.005. | ||
|
آمار تعداد مشاهده مقاله: 764 تعداد دریافت فایل اصل مقاله: 564 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب