Abstract
Drivers routing decisions can be influenced to minimize environmental impacts by using, for instance, dynamic and intelligent road pricing schemes. However, some previous research studies have shown that often different pollutants can dictate different traffic assignment strategies which makes necessary to assign weights to these pollutants so they become comparable. In this chapter, a tool for traffic assignment taking into account eco-routing purposes is presented. The main goal of this work is to identify the best traffic volume distribution that allows a minimization of environmental costs for a given corridor with predetermined different alternative routes. To achieve this, an integrated numerical computing platform was developed by integrating microscopic traffic and emission models. The optimization tool employs non-linear techniques to perform different traffic assignment methods: User Equilibrium (UE), System Optimum (SO) and System Equitable (SE). For each method, different strategies can be assessed considering: (i) individual pollutants and traffic performance criteria; and (ii) all pollutants simultaneously. For the latter case, three different optimization approaches can be assessed based on: (i) economic costs of pollutants once released into the air; (ii) human health impacts according to the Eco-Indicator 99; and (iii) real time atmospheric pollutant concentration levels. The model was applied to a simple network, simulating three levels of traffic demand and three different strategies for traffic assignment. The system is developed in Microsoft Excel and offers a user friendly access to optimization algorithms by including a dynamic user interface.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
European Environmental Agency—EEA: The contribution of transport to air quality—TERM 2012. EEA European Environment Agency, Copenhagen (2012)
European Commission: White paper Roadmap to a Single European Transport Area—Towards a competitive and resource efficient transport system. COM (2011) 144 final, Brussels (2011)
Yin, Y., Lawphongpanich, S.: Internalizing emission externality on road networks. Transp. Res. Part D Transp. Environ. 11, 292–301 (2006)
Sharma, S., Mishra, S.: Intelligent transportation systems-enabled optimal emission pricing models for reducing carbon footprints in a bimodal network. J. Intell. Transp. Syst. 17, 54–64 (2013). doi:10.1080/15472450.2012.708618
Bandeira, J.M., Carvalho, D.O., Khattak, A.J., et al.: A comparative empirical analysis of eco-friendly routes during peak and off-peak hours. 91st Annual Meeting of Transportation Research Board (2012)
Bandeira, J.M., Almeida, T.G., Khattak, A.J., et al.: Generating emissions information for route selection: experimental monitoring and routes characterization. J. Intell. Transp. Syst. 17, 3–17 (2013). doi:10.1080/15472450.2012.706197
Barth, M., Boriboonsomsin, K., Vu, A: Environmentally-friendly navigation. Conference on Intelligent Transportation Systems, Seattle, WA, USA, pp. 684–689 (2007)
Ahn, K.G., Rakha, H.: The effects of route choice decisions on vehicle energy consumption and emissions. Transp. Res. Part D-Transp. Environ. 13, 151–167 (2008). doi:10.1016/j.trd.2008.01.005
Yao, E., Song, Y.: Study on eco-route planning algorithm and environmental impact assessment. J. Intell. Transp. Syst. 17, 42–53 (2013). doi:10.1080/15472450.2013.747822
Gazis, A., Fontes, T., Bandeira, J., et al.: Integrated computational methods for traffic emissions route assessment. Fifth ACM SIGSPATIAL International Workshop on Computational Transportation Science (2012)
Guo, L., Huang, S., Sadek, A.W.: An evaluation of environmental benefits of time-dependent green routing in the greater buffalo-niagara region. J. Intell. Transp. Syst. 17, 18–30 (2013). doi:10.1080/15472450.2012.704336
Ahn, K., Rakha, H., Moran, K.: System-wide impacts of eco-routing strategies on large-scale networks. 91st Annual meeting of the transportation research board (2011)
DOT US: Applications for the environment: real-time information synthesis (AERIS)—benefit-cost analysis (2012)
Bureau of Public Roads: Traffic assignment manual. Washington DC (1964)
Spiess, H.: Conical volume-delay functions. Transp. Sci. 24 (1990)
Goedkoop, M., Spriensma, R.: The eco-indicator 99—a damage oriented method for life cycle assessment. Methodology Report (2000)
Rilett, L., Benedek, C.: Traffic assignment under environmental and equity objectives. Transp. Res. Rec. 1443, 92–99 (1994)
Lasdon, L.S., Waren, A.D., Jain, A., Ratner, M.: Design and testing of a generalized reduced gradient code for nonlinear programming. ACM Trans. Math. Softw. 4, 34–50 (1978). doi:10.1145/355769.355773
Frontline Systems Inc.: Basic solver—nonlinear optimization. http://www.solver.com/content/basic-solver-nonlinear-optimization (2013)
PTV.: Vissim 5.30-05 user manual. Planung Transport Verkehr AG, Karlsruhe, German (2011)
Fontes, T., Fernandes, P., Rodrigues, H., et al.: Are eco-lanes a sustainable option to reducing emissions in a medium-sized European city? 92nd Transportation Research Board Annual Meeting, Washington DC, January 2013
US Environmental Protection Agency—EPA: Methodology for developing modal emission rates for epa’s multi-scale motor vehicle and equipment emission system. Prepared by North Carolina State University for US Environmental Protection Agency, Ann Arbor (2002)
Coelho, M.C., Frey, H.C., Rouphail, N.M., et al.: Assessing methods for comparing emissions from gasoline and diesel light-duty vehicles based on microscale measurements. Transp. Res. Part D-Transp. Environ. 14, 91–99 (2009). doi:10.1016/j.trd.2008.11.005
Zhai, H., Frey, H.C., Rouphail, N.M.: A vehicle-specific power approach to speed- and facility-specific emissions estimates for diesel transit buses. Environ. Sci. Technol. 42, 7985–7991 (2008). doi:10.1021/es800208d
European Environment Agency—EEA: EMEP EEA Air Pollutant Emission Inventory Guidebook 2010–2011 (2009)
ACAP: Automobile Industry Statistics 2010 Edn. (2012)
Dejong, K., Spears, W.M.: An analysis of the interacting roles of population size and crossover in genetic function optimization. Proceedings of Parallel Problem Solving from Nature. Berlin, pp. 38–47 (1991)
Sadek, A.W., Smith, B.L., Demetsk, M.J.: Dynamic traffic assignment genetic algorithms approach. Transp. Res. Rec.1588 Pap. 1588, 95–103 (1997)
Acknowledgments
Bandeira and P. Fernandes acknowledge the support of the Portuguese Science and Technology Foundation—FCT for the Doctoral grants SFRH/BD/66104/2009 and SFRH/BD/87402/2012. This work was partially funded by FEDER Funds through the Operational Program ‘‘Factores de Competitividade COMPETE’’ and by National Funds through FCT within the project PTDC/SEN-TRA/115117/2009. The authors also acknowledge the Strategic Project PEst-C/EME/UI0481/2013 and Toyota Caetano Portugal, which allowed the use of vehicles. We are thankful to the US Department of Transportation, Research and Innovative Technology Administration for providing funding through the TranLIVE consortium of university transportation centers." The collaboration between Drs. Coelho and Khattak was under the auspices of the Luso-American Transportations Impacts Study Group (LATIS-G). The contents of this article reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Bandeira, J.M., Pereira, S.R., Fontes, T., Fernandes, P., Khattak, A.J., Coelho, M.C. (2014). An Eco-Traffic Management Tool. In: de Sousa, J., Rossi, R. (eds) Computer-based Modelling and Optimization in Transportation. Advances in Intelligent Systems and Computing, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-04630-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-04630-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04629-7
Online ISBN: 978-3-319-04630-3
eBook Packages: EngineeringEngineering (R0)