Abstract
Transportation emissions, including carbon dioxide (CO2), nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM), are a significant contributor to air pollution and greenhouse gas emissions. In urbanizing cities, poor air quality due to transportation emissions, especially CO2 from vehicles, is a considerable challenge. CO2 is a greenhouse gas contributing to climate change and global temperature rise. Therefore, it is critical to figure out CO2 emissions from transportation. Travel activity data must be collected throughout time and space for emissions connected to traffic to develop temporal CO2 emission prediction models. However, emission prediction research from on-road vehicle fleets is limited, particularly in developing countries such as Malaysia. Therefore, a new modeling approach using ensemble machine learning techniques is proposed in this study, which combines lab-tested and fieldwork data collection to estimate CO2 emissions at a finer geographical scale. The approach aims to uncover dynamic vehicle behavior and CO2 predictions in Klang Valley urban city by identifying patterns in vehicle activities in road networks.
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References
Abas MA, Abidin S (2018) Development of Malaysian urban drive cycle using vehicle and engine parameters. Transp Res Part D: Transp Environ 63:388–403. https://doi.org/10.1016/j.trd.2018.05.015
Azeez O, Pradhan B, Shafri H, Shukla N, Lee C-W, Rizeei H (2019) Modeling of CO emissions from traffic vehicles using artificial neural networks. Applied Sciences 9(2). https://doi.org/10.3390/app9020313
Azmi M, Tokai A (2016) System dynamic modeling of CO2 emissions and pollutants from passenger cars in Malaysia, 2040. Environ Syst Decis 36(4):335–350. https://doi.org/10.1007/s10669-016-9612-7
Cai J, Xu K, Zhu Y, Hu F, Li L (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Applied Energy 262. https://doi.org/10.1016/j.apenergy.2020.114566
Dahiya N, Saini B, Chalak HD (2021) Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing. J King Saud University—Engineering Sci. https://doi.org/10.1016/j.jksues.2021.08.004
Dissanayake M, Nguyen H, Poologanathan K, Perampalam G, Upasiri I, Rajanayagam H, Suntharalingam T (2022) Prediction of shear capacity of steel channel sections using machine learning algorithms. Thin-Walled Structures 175:109152
Fameli KM, Assimakopoulos VD (2015) Development of a road transport emission inventory for Greece and the Greater Athens Area: effects of important parameters. Sci Total Environ 505:770–786. https://doi.org/10.1016/j.scitotenv.2014.10.015
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232
Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
GoogleMaps (2023) Google Maps. https://maps.app.goo.gl/3FFqCird7CmnyF3X7
Hirasen D, Pillay V, Viriri S, Gwetu M Skeletal Age Estimation from Hand Radiographs Using Ensemble Deep Learning. In: Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA (eds) Pattern recognition, Cham, 2021// 2021. Springer International Publishing, pp 173–183
Hu H, Lee G, Kim JH, Shin H (2020) Estimating micro-level on-road vehicle emissions using the k-means clustering method with GPS big data. Electronics 9(12). https://doi.org/10.3390/electronics9122151
Kutsev Bengisu Altug˘ SEK (2019) Predicting Tailpipe NOx Emission using Supervised Learning Algorithms. IEEE Xplore
Lasocki J (2021) The WLTC vs NEDC: A case study on the impacts of driving cycle on engine performance and fuel consumption. Int J Automot Mech Eng 18 (3):9071–9081. https://doi.org/10.15282/ijame.18.3.2021.19.0696
Li Z, Yim SH-L, Ho K-F (2020) High temporal resolution prediction of street-level PM2.5 and NOx concentrations using machine learning approach. J Clean Prod 268. https://doi.org/10.1016/j.jclepro.2020.121975
Lv Z, Wu L, Ma C, Sun L, Peng J, Yang L, Wei N, Zhang Q, Mao H (2023) Comparison of CO2, NOx, and VOCs emissions between CNG and E10 fueled light-duty vehicles. Sci Total Environ 858:159966
Ma J, Yu Z, Qu Y, Xu J, Cao Y (2020) Application of the XGBoost machine learning method in PM2.5 Prediction: A case study of Shanghai. Aerosol Air Qual Res 20 (1):128–138. https://doi.org/10.4209/aaqr.2019.08.0408
Moazami S, Noori R, Amiri BJ, Yeganeh B, Partani S, Safavi S (2016) Reliable prediction of carbon monoxide using developed support vector machine. Atmos Pollut Res 7(3):412–418. https://doi.org/10.1016/j.apr.2015.10.022
Nie P, Roccotelli M, Fanti MP, Ming Z, Li Z (2021) Prediction of home energy consumption based on gradient boosting regression tree. Energy Rep 7:1246–1255. https://doi.org/10.1016/j.egyr.2021.02.006
OpenStreetMap (2023) OpenStreetMap. https://www.openstreetmap.org/#map=15/3.0248/101.4318
Ramos A, Muñoz J, Andrés F, Armas O (2018) NOx emissions from diesel light duty vehicle tested under NEDC and real-word driving conditions. Transp Res Part D: Transp Environ 63:37–48. https://doi.org/10.1016/j.trd.2018.04.018
Sofwan NM, Latif MT (2021) Characteristics of the real-driving emissions from gasoline passenger vehicles in the Kuala Lumpur urban environment. Atmos Pollut Res 12(1):306–315. https://doi.org/10.1016/j.apr.2020.09.004
Suleiman A, Tight MR, Quinn AD (2016) Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM 10. Transp Res Part D: Transp Environ 49:301–312. https://doi.org/10.1016/j.trd.2016.10.010
Talib AM, Jasim DMN (2021) GIS-GPS based national air pollution monitoring system. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.05.445
Wang J, Wang R, Yin H, Wang Y, Wang H, He C, Liang J, He D, Yin H, He K (2022) Assessing heavy-duty vehicles (HDVs) on-road NO(x) emission in China from on-board diagnostics (OBD) remote report data. Sci Total Environ 846:157209. https://doi.org/10.1016/j.scitotenv.2022.157209
Wang S, Gao S, Li S, Feng K (2020) Strategizing the relation between urbanization and air pollution: Empirical evidence from global countries. J Cleaner Prod 243. https://doi.org/10.1016/j.jclepro.2019.118615
Wang S, Li Z, Tan J, Guo W, Li L A method for estimating carbon dioxide emissions based on low frequency GPS trajectories. In: 2017 Chinese Automation Congress (CAC), 2017. IEEE, pp 1960–1964
Wu Y, Yang Z, Lin B, Liu H, Wang R, Zhou B, Hao J (2012) Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of China. Energy Policy 48:537–550
Yousefi-Sahzabi A, Sasaki K, Djamaluddin I, Yousefi H, Sugai Y (2011) GIS modeling of CO2 emission sources and storage possibilities. Energy Procedia 4:2831–2838. https://doi.org/10.1016/j.egypro.2011.02.188
Zhao J, Zhang D, Gao D, Bao J, Jing X, Li M Investigation of methods for measuring fuel economy and emissions of heavy-duty hybrid-electric vehicles (HEVs). In: E3S Web of Conferences, 2022. EDP Sciences, p 01002
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Subramaniam, N., Yusof, N. (2024). Prediction of On-Road CO2 Emission in Urban Area Using State-of-The-Art Ensemble Machine Learning Model. In: Yadava, R.N., Ujang, M.U. (eds) Advances in Geoinformatics Technologies . Earth and Environmental Sciences Library. Springer, Cham. https://doi.org/10.1007/978-3-031-50848-6_7
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