Evaluating Impacts of Traffic Incidents on CO 2 Emissions in Express Roads
51
In view of the needs for further studies on the subject, we recommend to apply the
methodology throughout the Rio de Janeiro city, not only in an express road as carried
out in this chapter, as well as to apply it to other areas in Brazil and in developing
countries that suffer from the loss of quality of life of the urban population due to
the high CO 2 emissions.
Acknowledgements This work was partially supported by the National Council for Scientific and
Technological Development (CNPq), under grant #307835/2017-0. This work was supported by
Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro, under grants
#233926. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior—Brasil (CAPES)—Finance Code 001. We would like to thank CET-Rio for
providing the database for this research.
References
1. 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: Trans Environ 21:39–46. https://
doi.org/10.1016/j.trd.2013.02.003
2. Adler N, Hakkert AS, Kornbluth J, Raviv T, Sher M (2013) Location-allocation models for
traffic police patrol vehicles on an interurban network. Ann Oper Res 221(1):9–31. https://doi.
org/10.1007/s10479-012-1275-2
3. Anderson JM, Kalra N, Stanley KD, Sorensen P, Samaras C, Oluwatola OA (2016) Autonomous
vehicle technology: a guide for policymakers. RAND Corporation, RR-443-2-RC, Santa
Monica, Calif. Available at: http://www.rand.org/pubs/research_reports/RR443-2.html
4. Bai X, Zhou Z, Chin K-S, Huang B (2017) Evaluating lane reservation problems by carbon
emission approach. Trans Res Part D: Trans Environ 53:178–192. https://doi.org/10.1016/j.trd.
2017.04.002
5. Baltar M, Abreu V, Ribeiro G, Bahiense L (2020) Multi-objective model for the problem of
locating tows for incident servicing on expressways. TOP. https://doi.org/10.1007/s11750-02000567-w
6. Barth M, Boriboonsomsin K (2008) Real-World carbon dioxide impacts of traffic congestion.
Trans Res Record: J Trans Res Board 2058(1):163–171. https://doi.org/10.3141/2058-20
7. Bíl M, Andrásik R, Janoska Z (2013) Identification of hazardous road locations of traffic
accidents by means of kernel density estimation and cluster significance evaluation. Accid
Anal Prev 55:265–273. https://doi.org/10.1016/j.aap.2013.03.003
8. Blazquez C, Celis M (2011) A spatial and temporal analysis of child pedestrian crashes in
Santiago, Chile. Accident Anal Prevent 50:304–311. https://doi.org/10.1016/j.aap.2012.05.001
9. Burns LD (2013) A vision of our transport future. Nature 497:181–182. https://doi.org/10.
1038/497181a
10. Chen K, Yu L (2007) Microscopic traffic-emission simulation and case study for evaluation of
traffic control strategies. J Transp Syst Eng Inf Technol 7(1):93–99. https://doi.org/10.1016/
s1570-6672(07)60011-7
11. Chen L, Cao Y, Ji R (2010) Automatic incident detection algorithm based on support vector
machine. IEEE Sixth International conference on natural computation, 864–866. https://doi.
org/10.1109/icnc.2010.5583920
12. Chen Y, Gomez A, Frame G (2017) Achieving energy savings by intelligent transportation
systems investments in the context of smart cities. Transp Res Part D: Trans Environ 54:381–
396. https://doi.org/10.1016/j.trd.2017.06.008
51
In view of the needs for further studies on the subject, we recommend to apply the
methodology throughout the Rio de Janeiro city, not only in an express road as carried
out in this chapter, as well as to apply it to other areas in Brazil and in developing
countries that suffer from the loss of quality of life of the urban population due to
the high CO 2 emissions.
Acknowledgements This work was partially supported by the National Council for Scientific and
Technological Development (CNPq), under grant #307835/2017-0. This work was supported by
Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro, under grants
#233926. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior—Brasil (CAPES)—Finance Code 001. We would like to thank CET-Rio for
providing the database for this research.
References
1. 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: Trans Environ 21:39–46. https://
doi.org/10.1016/j.trd.2013.02.003
2. Adler N, Hakkert AS, Kornbluth J, Raviv T, Sher M (2013) Location-allocation models for
traffic police patrol vehicles on an interurban network. Ann Oper Res 221(1):9–31. https://doi.
org/10.1007/s10479-012-1275-2
3. Anderson JM, Kalra N, Stanley KD, Sorensen P, Samaras C, Oluwatola OA (2016) Autonomous
vehicle technology: a guide for policymakers. RAND Corporation, RR-443-2-RC, Santa
Monica, Calif. Available at: http://www.rand.org/pubs/research_reports/RR443-2.html
4. Bai X, Zhou Z, Chin K-S, Huang B (2017) Evaluating lane reservation problems by carbon
emission approach. Trans Res Part D: Trans Environ 53:178–192. https://doi.org/10.1016/j.trd.
2017.04.002
5. Baltar M, Abreu V, Ribeiro G, Bahiense L (2020) Multi-objective model for the problem of
locating tows for incident servicing on expressways. TOP. https://doi.org/10.1007/s11750-02000567-w
6. Barth M, Boriboonsomsin K (2008) Real-World carbon dioxide impacts of traffic congestion.
Trans Res Record: J Trans Res Board 2058(1):163–171. https://doi.org/10.3141/2058-20
7. Bíl M, Andrásik R, Janoska Z (2013) Identification of hazardous road locations of traffic
accidents by means of kernel density estimation and cluster significance evaluation. Accid
Anal Prev 55:265–273. https://doi.org/10.1016/j.aap.2013.03.003
8. Blazquez C, Celis M (2011) A spatial and temporal analysis of child pedestrian crashes in
Santiago, Chile. Accident Anal Prevent 50:304–311. https://doi.org/10.1016/j.aap.2012.05.001
9. Burns LD (2013) A vision of our transport future. Nature 497:181–182. https://doi.org/10.
1038/497181a
10. Chen K, Yu L (2007) Microscopic traffic-emission simulation and case study for evaluation of
traffic control strategies. J Transp Syst Eng Inf Technol 7(1):93–99. https://doi.org/10.1016/
s1570-6672(07)60011-7
11. Chen L, Cao Y, Ji R (2010) Automatic incident detection algorithm based on support vector
machine. IEEE Sixth International conference on natural computation, 864–866. https://doi.
org/10.1109/icnc.2010.5583920
12. Chen Y, Gomez A, Frame G (2017) Achieving energy savings by intelligent transportation
systems investments in the context of smart cities. Transp Res Part D: Trans Environ 54:381–
396. https://doi.org/10.1016/j.trd.2017.06.008