Abstract
This article explores the trade-off between the maximization of traffic incident attendance and the minimization of CO\(_{2}\) emissions related to a real-world case study from Avenida Brasil, the main expressway of Rio de Janeiro, Brazil. We propose a mathematical multi-objective model including queuing theory to locate tows for incident servicing, having as constraints a maximum response time and a limited number of equipment. The model was applied to 3080 real incidents, occurred between March and June of 2018, and the results showed that when the maximum response time is restricted, the quantity of tows has to be raised to make sure that there is an adequate servicing. Therefore, the service level expected to be achieved in the answer to incidents is what most influences the results, regardless of the weight associated to the minimization of CO\(_{2}\) emissions.
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References
Abou-Senna H, Radwan E (2013) VISSIM/MOVES integration to investigate the effect of major key parameters on CO\(_2\) emissions. Transp Res Part D 21:39–46. https://doi.org/10.1016/j.trd.2013.02.003
Adler N, Hakkert A, Kornbluth J, Raviv T, Sher M (2014) Location-allocation models for traffic police patrol vehicles on an interurban network. Ann Oper Res 221:9–31. https://doi.org/10.1007/s10479-012-1275-2
Akdogan M, Bayindir Z, Iyigun C (2017) Location analysis of emergency vehicles using an approximate queueing model. Transp Res Procedia 22:430–439. https://doi.org/10.1016/j.trpro.2017.03.018
Avetisyan H, Miller-Hooks E, Melanta S, Qi B (2014) Effects of vehicle technologies, traffic volume changes, incidents and work zones on greenhouse gas emissions production. Transp Res Part D 26:10–19. https://doi.org/10.1016/j.trd.2013.10.005
Bai X, Zhou Z, Chin K, Huang B (2017) Evaluating lane reservation problems by carbon emission approach. Transp Res Part D 53:178–192. https://doi.org/10.1016/j.trd.2017.04.002
Barth M, Boriboonsomsin K (2008) Real-world carbon dioxide impacts of traffic congestion. Transp Res Rec 2058:163–171. https://doi.org/10.3141/2058-20
Bliemer M, Versteegt H, Castenmiller R (2004) INDY: a new analytical multiclass dynamic traffic assignment model. In: TRISTAN V conference proceedings, Guadeloupe
CALPUFF Modeling System: U.S. Environmental Protection Agency (EPA) (2008) (Online). https://www3.epa.gov/ttn/scram/7thconf/calpuff/Previous_SCRAM_CALPUFF_Posting_Reference.pdf. Accessed 20 Dec 2019
CET-Rio-Traffic Engineering Company of Rio de Janeiro: Dados sobre incidentes no ano de 2018, Rio de Janeiro-Brasil (2018) (online). https://www.arcgis.com/home/item.html?id=360955161c5843c7856b2668fa87786d. Accessed 2 Dec 2019
Chung Y, Cho H, Choi K (2013) Impacts of freeway accidents on CO\(_{2}\) emissions: a case study for Orange county, California, US. Transp Res Part D 24:120–126. https://doi.org/10.1016/j.trd.2013.06.005
CMEM-Comprehensive Modal Emissions Model (1996) National Cooperative Highway Research Program (NCHRP) and U.S. Environmental Protection Agency (EPA) (online). https://www.cert.ucr.edu/cmem. Accessed 20 Dec 2019
Department of Energy e Climate Change (DECC) (2014) 2012 UK greenhouse gas emissions (online). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/408045/2014_Final_UK_greenhouse_gas_emissions_national_statistics_1990-2012.pdf. Accessed 10 Dec 2019
FHWA (2004) Incident characteristics and impact on freeway traffic (online). https://pdfs.semanticscholar.org/b096/fe91a1c9a3e89e0449ca0c3ed6b7b6276ded.pdf. Accessed 10 Dec 2019
Fogliatti, M., Mattos, N.: Teoria de filas, 1, vol. 1, 1 edn. Editora Interciência, Rio de Janeiro (2007). ISBN 8571931577
Geroliminis N, Karlaftis M, Skabardonis A (2009) A spatial queuing model for the emergency vehicle districting and location problem. Transp Res Part B 43:798–811. https://doi.org/10.1016/j.trb.2009.01.006
Grote M, Williams I, Preston J, Kemp S (2016) Including congestion effects in urban road traffic CO2 emissions modelling: do local government authorities have the right options? Transp Res Part D 43:95–106. https://doi.org/10.1016/j.trd.2015.12.010
Haule H, Sando T, Lentz R, Chuan C, Alluri P (2019) Evaluating the impact and clearance duration of freeway incidents. Int J Transp Sci Technol 8:13–24. https://doi.org/10.1016/j.ijtst.2018.06.005
Hickman J (1999) Methodology for calculating transport emissions and energy consumption (online). https://trimis.ec.europa.eu/sites/default/files/project/documents/meet.pdf. Accessed 10 Dec 2019
Hojati A, Ferreira L, Washington S, Chales P, Shobeirinejad A (2016) Reprint of: Modelling the impact of traffic incidents on travel time reliability. Transp Res Part C 70:86–97. https://doi.org/10.1016/j.trc.2016.06.013
Jabali O, Woensel T, de Kok A (2012) Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod Oper Manag 21:1060–1074. https://doi.org/10.1111/j.1937-5956.2012.01338.x
Joo S, Oh C, Lee S, Lee G (2017) Assessing the impact of traffic crashes on near freeway air quality. Transp Res Part D 57:64–73. https://doi.org/10.1016/j.trd.2017.09.013
Ntziachristos L, Kouridis C, Gkatzoflias D, Samaras Z (2009) COPERT: a European Road Transport Emission Inventory Model. In: Athanasiadis IN, Rizzoli AE, Mitkas PA, Gómez JM (eds) Information technologies in environmental engineering. Environmental science and engineering. Springer, Berlin
Lin J, Yu D (2008) Traffic-related air quality assessment for open road tolling highway facility. J Environ Manag 88(4):962–969. https://doi.org/10.1016/j.jenvman.2007.05.005
Lou Y, Yin Y, Lawphongpanich S (2011) Freeway service patrol deployment planning for incident management and congestion mitigation. Transp Res Part C 19:283–295. https://doi.org/10.1016/j.trc.2010.05.014
Ma X, Ding C, Luan S, Wang Y, Wang Y (2017) Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Trans Intell Transp Syst 18:2303–2310. https://doi.org/10.1109/TITS.2016.2635719
Ozbay K, Iyigun C, Baykal-Gursoy M, Xiao W (2013) Probabilistic programming models for traffic incident management operations planning. Ann Oper Res 203:389–406. https://doi.org/10.1007/s10479-012-1174-6
Pal R, Bose I (2009) An optimization based approach for deployment of roadway incident response vehicles with reliability constraints. Eur J Oper Res 198:452–463. https://doi.org/10.1016/j.ejor.2008.09.010
Smit R, Poelman M, Schrijver J (2008) Improved road traffic emission inventories by adding mean speed distributions. Atmos Environ 42:916–926. https://doi.org/10.1016/j.atmosenv.2007.10.026
Smit R, Smokers R, Rabé E (2007) A new modelling approach for road traffic emissions: Versit+. Transp Res Part D 12:414–422. https://doi.org/10.1016/j.trd.2007.05.001
Sookun A, Boojhawon R, Rughooputh S (2014) Assessing greenhouse gas and related air pollutant emissions from road traffic counts: a case study for Mauritius. Transp Res Part D 32:35–47. https://doi.org/10.1016/j.trd.2014.06.005
TRB (2002) The congestion mitigation and air quality improvement program, assessing 10 years of experience. Transp Res Board 1:60–64. https://doi.org/10.17226/10350
TRB (2010) Highway Capacity Manual, vol 1, 5th edn. Transportation Research Board, Washington DC
Yin Y (2006) Optimal fleet allocation of freeway service patrols. Netw Spat Econ 6:221–234. https://doi.org/10.1007/s11067-006-9281-z
Yuan F, Cheu R (2003) Incident detection using support vector machines. Transp Res Part C 11:309–328. https://doi.org/10.1016/S0968-090X(03)00020-2
Zhang K, Batterman S, Dion F (2011) Vehicle emissions in congestion: comparison of work zone, rush hour and free-flow conditions. Atmos Environ 45:1929–1939. https://doi.org/10.1016/j.atmosenv.2011.01.030
Zhu S, Kim W, Chang G, Asce M, Rochon S (2018) Design and evaluation of operational strategies for deploying emergency response teams: dispatching or patrolling. J Transp Eng 140:04014021. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000670
Zou Y, Ye X, Henrickson K, Tang J, Wang Y (2006) Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach. Transp Res Part C 86:221–234. https://doi.org/10.1016/j.trc.2017.11.004
Acknowledgements
This work was partially supported by the National Council for Scientific and Technological Development-CNPq, under grants #309661/2019-6 and #307835/2017-0.
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Baltar, M., Abreu, V., Ribeiro, G. et al. Multi-objective model for the problem of locating tows for incident servicing on expressways. TOP 29, 58–77 (2021). https://doi.org/10.1007/s11750-020-00567-w
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DOI: https://doi.org/10.1007/s11750-020-00567-w
Keywords
- Incident servicing on expressways
- CO\(_{2}\) emissions
- Multi-objective modelling
- p-median problem
- Pareto optimality