Estimating the amount of fuel consumption and air pollution caused by the traffic of buses rapid transit using agent-based modeling
Subject Areas :
Urban Transportation
rahman noormohammadi
1
,
seyed mohammadali khatami firoozabadi
2
,
akbar alamtabriz
3
,
Reza Ehtesham Rasi
4
,
amir daneshvar
5
1 - PhD student, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran and Visiting Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. *(Corresponding Author)
3 - Professor, Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
4 - Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
5 - Assistant Professor, Department of Information Technology Management, Electronic Branch, Islamic Azad University, Tehran, Iran.
Received: 2023-03-06
Accepted : 2023-05-03
Published : 2023-08-23
Keywords:
Air pollution,
NetLogo,
Gasoline,
Agent-based modeling,
Rapid Buses,
Abstract :
Background and Objective: The increased traffic has been followed by many problems in metropolitans, the key of which is air pollution and excessive fuel consumption. Paying attention to public transportation, particularly the bus rapid transit (BRT) system is one of the measures that may be taken, since besides reducing social expenses, it may be very effective in declining air pollution. The main objective of the present research is to study the fuel consumption rate and the emissions rate of various air pollutants including CO2, CH4, and N2O gases in various scenarios of BRT system.
Material and Methodology: Since traffic and congestion phenomena are complex and dynamic, it is very difficult and sometimes impossible to model them with common mathematical models. To this end, agent-based technologies, highly compatible with these characteristics, can be utilized. In the current research, BRT system’s performance, the fuel consumption rate, and the amount of air pollutants production are estimated using agent-based modeling. This study emphasizes what changes should be made in effective parameters such as bus speed and bus stop time at stations, as well as bus dispatch timing in order to control fuel consumption and reduce pollution factors. This research uses NetLogo software to code the model and run its simulation and considers three different scenarios in line one of BRT system in Tehran (Iran).
Findings: following the analysis and comparison of different scenarios, suggestions are made to decline fuel consumption and air pollutants, such as minor changes in the parameters of bus stop times at stations as well as changes in the dispatch time of buses from the terminal in order to reduce fuel consumption and air pollution rates. The results indicate that one of the improved situations was related to the situation of increasing the bus dispatch time parameter and in the bridge scenario, CO2, CH4, and N2O emissions are 1458.6, 1.122, and 11.781, respectively, in one hour of peak passenger time.
Discussion and Conclusion: According to the results, achieving the goal of reducing fuel consumption and air pollution rates is more suitable in the bridge scenario compared to the other two scenarios. Furthermore, if possible, it is suggested to build bridges at intersections with high traffic, or put the smart traffic light system on the agenda.
References:
Barca, S. Energy, property, and the industrial revolution narrative. Ecol. Econ. 2011, 70, 1309–1315. [CrossRef]
Manisalidis, I.; Stavropoulou, E.; Stavropoulos, A.; Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health 2020, 8, 14. [CrossRef] [PubMed]
Moya, Diego et al (2021), Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India’s industry sector, Applied Energy, 274, 1-26.
Asgari M. Potential to reduce pollutant emissions from personal rides In big cities with cars with new technologies Master’s (thesis). Iran. Faculty of Civil Engineering Sharif University; 2011. (In Persian)
Ashrafi Kh., Shafipour M., Kamalan H. Estimating temporal and seasonal variation of ventilation coefficients, International Journal of Environmental Research,2009.
USEPA (2009). Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. Available at: www.epa.gov/climatechange/index.html.
Zegras, P.C. (2007). As if Kyoto Mattered: The Clean Development Mechanism and Transportation. Energy Policy, 35: 5136– 5150.
USEPA (2008). Climate Leaders, Greenhouse Gas Inventory Protocol Core Module Guidance. Direct Emissions from Mobile Combustion Sources. Available at: www.epa.gov/Climate Leaders Office of Air and Radiation.
Golzar Shahri, Ahmed et al. (2014), improving the performance of the bus network in Yazd city with the aim of improving service quality and reducing costs, 15th International Conference on Transportation and Traffic Engineering, Tehran. (In Persian)
Etisam, Hadi, Rouhi, Amir (2014), investigating the causes of traffic congestion on rainy and snowy days and providing solutions to reduce it, 15th International Conference on Transportation and Traffic Engineering, Tehran, Traffic Transportation Organization. (In Persian)
Sachs J, Meng Y, Giarola S, Hawkes A. An agent-based model for energy investment decisions in the residential sector. Energy 2019;172: 752–68
Motsadi Zarandi, Saeed et al. (2012), Investigation of the effect of the high-speed bus system on the emission of greenhouse gases in Tehran, Environmental Sciences, 9 (4), 1-12. (In Persian)
Ehsani, Mehrsa and Ahmadi, Abbas and Fadaei, Daoud, (2012), Modeling vehicle fuel consumption and carbon dioxide emissions in road transport with an emphasis on the effect of renewable energies, the third international conference on new approaches to energy conservation, Tehran. (In Persian)
Rezaei Aghamirlou, Mohammadreza et al. (2013), measuring the effect of the implementation of the high-speed bus system on air pollution, Traffic Management Studies, 23, 45-70. (In Persian)
Nasrollahi, Zahra, Poshdozbashi, Hanieh (2015), estimation of air pollution caused by public transport vehicles in the city of Yazd, Environmental Science and Technology, 22 (2), 15-29. (In Persian)
Ashrafi, Khosrow et al. (2017), investigating the effects of the expansion of high-speed city buses on traffic and air pollution using the EMME/2 model f) IVE case study: line number 10 of the return route of Azad University to Azadi Square), research in environmental health, 4 (3), 165-184. (In Persian)
Kamal Nasir, Mostofa (2014), Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems, Hindawi Publishing Corporation e Scientific World Journal, 1-13.
Mizdrak, Anja et al, (2019), Potential of active transport to improve health, reduce healthcare costs, and reduce greenhouse gas emissions: A modelling study, PLOS ONE, 1-17.
Wen, Hung-Ta et al (2021), Features Importance Analysis of Diesel Vehicles’ NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model, International Journal of Environmental Research and Public Health, 18, 1-28.
Duan, Linfeng et al (2021), Impacts of reducing air pollutants and CO2 emissions in urban road transport through 2035 in Chongqing, China, Environmental Science and Ecotechnology, 8, 1-12.
Filigrana, Paola, et al (2022), Health benefits from cleaner vehicles and increased active transportation in Seattle, Washington, Journal of Exposure Science & Environmental Epidemiology, 32:538–544.
Bonabeau E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3): 7280-7287.
Homayounfar, M., Bagher-Salimi, S., Nahavandi, B., Izadi Sheijani, K. (2018). Agentbased Simulation of National Oil Products Distribution Company’s Supply Network in the Framework of a Complex Adaptive System in Order to Achieve an Optimal Inventory Level. Industrial Management Journal, 10(4), 607-630.
Azar, Adel, et al (2021), Modeling Steel Supply Chain and Estimating Its Consumption through ABM Methodology, Industrial Management Perspective, 11( 41), pp, 33-51.
Rand, W., Rust, R. T., & Kim, M. (2018). Complex systems: marketing’s new frontier. AMS Review, 8(3–4), 111–127.
Sargent, R. G. (2007). Verification and validation of simulation models. Simulation conference, Washington, DC, USA.
Bezazan Fatemeh, Khosravani, Neda (2016), measuring the amount of carbon dioxide emissions by different production sectors and households due to energy consumption in Iran (environmental data-output approach), environmental economics and natural resources bi-quarterly, first year, number 1, pages 1-25. (In Persian)
National Climate Change Office (2010). Iran Second National Communication to UNFCC, December 2010, Department of Environment.
_||_
Barca, S. Energy, property, and the industrial revolution narrative. Ecol. Econ. 2011, 70, 1309–1315. [CrossRef]
Manisalidis, I.; Stavropoulou, E.; Stavropoulos, A.; Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health 2020, 8, 14. [CrossRef] [PubMed]
Moya, Diego et al (2021), Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India’s industry sector, Applied Energy, 274, 1-26.
Asgari M. Potential to reduce pollutant emissions from personal rides In big cities with cars with new technologies Master’s (thesis). Iran. Faculty of Civil Engineering Sharif University; 2011. (In Persian)
Ashrafi Kh., Shafipour M., Kamalan H. Estimating temporal and seasonal variation of ventilation coefficients, International Journal of Environmental Research,2009.
USEPA (2009). Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. Available at: www.epa.gov/climatechange/index.html.
Zegras, P.C. (2007). As if Kyoto Mattered: The Clean Development Mechanism and Transportation. Energy Policy, 35: 5136– 5150.
USEPA (2008). Climate Leaders, Greenhouse Gas Inventory Protocol Core Module Guidance. Direct Emissions from Mobile Combustion Sources. Available at: www.epa.gov/Climate Leaders Office of Air and Radiation.
Golzar Shahri, Ahmed et al. (2014), improving the performance of the bus network in Yazd city with the aim of improving service quality and reducing costs, 15th International Conference on Transportation and Traffic Engineering, Tehran. (In Persian)
Etisam, Hadi, Rouhi, Amir (2014), investigating the causes of traffic congestion on rainy and snowy days and providing solutions to reduce it, 15th International Conference on Transportation and Traffic Engineering, Tehran, Traffic Transportation Organization. (In Persian)
Sachs J, Meng Y, Giarola S, Hawkes A. An agent-based model for energy investment decisions in the residential sector. Energy 2019;172: 752–68
Motsadi Zarandi, Saeed et al. (2012), Investigation of the effect of the high-speed bus system on the emission of greenhouse gases in Tehran, Environmental Sciences, 9 (4), 1-12. (In Persian)
Ehsani, Mehrsa and Ahmadi, Abbas and Fadaei, Daoud, (2012), Modeling vehicle fuel consumption and carbon dioxide emissions in road transport with an emphasis on the effect of renewable energies, the third international conference on new approaches to energy conservation, Tehran. (In Persian)
Rezaei Aghamirlou, Mohammadreza et al. (2013), measuring the effect of the implementation of the high-speed bus system on air pollution, Traffic Management Studies, 23, 45-70. (In Persian)
Nasrollahi, Zahra, Poshdozbashi, Hanieh (2015), estimation of air pollution caused by public transport vehicles in the city of Yazd, Environmental Science and Technology, 22 (2), 15-29. (In Persian)
Ashrafi, Khosrow et al. (2017), investigating the effects of the expansion of high-speed city buses on traffic and air pollution using the EMME/2 model f) IVE case study: line number 10 of the return route of Azad University to Azadi Square), research in environmental health, 4 (3), 165-184. (In Persian)
Kamal Nasir, Mostofa (2014), Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems, Hindawi Publishing Corporation e Scientific World Journal, 1-13.
Mizdrak, Anja et al, (2019), Potential of active transport to improve health, reduce healthcare costs, and reduce greenhouse gas emissions: A modelling study, PLOS ONE, 1-17.
Wen, Hung-Ta et al (2021), Features Importance Analysis of Diesel Vehicles’ NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model, International Journal of Environmental Research and Public Health, 18, 1-28.
Duan, Linfeng et al (2021), Impacts of reducing air pollutants and CO2 emissions in urban road transport through 2035 in Chongqing, China, Environmental Science and Ecotechnology, 8, 1-12.
Filigrana, Paola, et al (2022), Health benefits from cleaner vehicles and increased active transportation in Seattle, Washington, Journal of Exposure Science & Environmental Epidemiology, 32:538–544.
Bonabeau E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3): 7280-7287.
Homayounfar, M., Bagher-Salimi, S., Nahavandi, B., Izadi Sheijani, K. (2018). Agentbased Simulation of National Oil Products Distribution Company’s Supply Network in the Framework of a Complex Adaptive System in Order to Achieve an Optimal Inventory Level. Industrial Management Journal, 10(4), 607-630.
Azar, Adel, et al (2021), Modeling Steel Supply Chain and Estimating Its Consumption through ABM Methodology, Industrial Management Perspective, 11( 41), pp, 33-51.
Rand, W., Rust, R. T., & Kim, M. (2018). Complex systems: marketing’s new frontier. AMS Review, 8(3–4), 111–127.
Sargent, R. G. (2007). Verification and validation of simulation models. Simulation conference, Washington, DC, USA.
Bezazan Fatemeh, Khosravani, Neda (2016), measuring the amount of carbon dioxide emissions by different production sectors and households due to energy consumption in Iran (environmental data-output approach), environmental economics and natural resources bi-quarterly, first year, number 1, pages 1-25. (In Persian)
National Climate Change Office (2010). Iran Second National Communication to UNFCC, December 2010, Department of Environment.