Skip to main content

Advertisement

SpringerLink
Log in

Finding Efficient and Environmentally Friendly Paths for Risk-Averse Freight Carriers

  • Published:
Networks and Spatial Economics Aims and scope Submit manuscript

Abstract

This paper aims to incorporate two important measures into a freight shortest path problem, namely reliability and sustainability. Reliability measure deals with the uncertainty of link travel time while sustainability measure tends to reduce the fuel consumption and emission along the path. Greenhouse gas (GHG) emission rates are generated from Motor Vehicle Emission Simulator (MOVES) model and approximated as a function of the average link travel speed. To model uncertainty, the link travel speed is treated as a discrete random variable with a given distribution. Freight carriers are assumed to be risk-averse; for example, given two paths with the same average cost, carriers prefer the one with less variability. The risk-averse behavior is captured by the second order stochastic dominance (SSD) relationship. Specifically, SSD constraints are introduced in our model to narrow down the feasible paths which dominate a chosen benchmark path. The reliable and sustainable routing model is formulated as an integer program that can be easily tailored to a variety of modeling preferences. The study experiments with eight variants of the base model, each corresponding to a different trade-off strategy between three objectives, namely, efficiency, reliability and sustainability. The numerical experiments illustrate the benefits of the models discussed in the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. www.ens-newswire.com/ens/may2010/2010-05-21-02.html, last visited on July 5, 2012.

  2. see, e.g., http://www.mpgforspeed.com/.

  3. We note that road travel speeds may vary with the time of day, especially in urban areas. This practical feature is not considered in this paper in order to simplify the analysis of efficiency, reliability and environmental consideration.

  4. Determining the weight vector is a challenge in its own right in practice. One possibility is for the analyst to interview the decision makers and then estimate the weights properly from the survey data. The reader is referred to Keeney (1993) for detailed discussions along this line.

References

  • Ahn K, Rakha H (2008) The effects of route choice decisions on vehicle energy consumption and emissions. Transp Res D 13(3):151–167

    Article  Google Scholar 

  • Ando N, Taniguchi E (2006) Travel time reliability in vehicle routing and scheduling with time windows. Netw Spat Econ 6(3–4):293–311

    Article  Google Scholar 

  • Arrow K (1965) Aspects of the theory of risk-bearing. Helsinki: Yrj Hahnsson Foundation

  • Barth M, An F, Norbeck J, Ross M (1996) Modal emissions modeling: a physical approach. Transp Res Rec J Transp Res Board 1520:81–88

    Article  Google Scholar 

  • Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res B 8:1232–1250

    Article  Google Scholar 

  • Chen A, Zhou Z, Ryu S (2011) Modeling physical and environmental side constraints in traffic equilibrium problem. Int J Sustain Transp 5(3):172–197

    Article  Google Scholar 

  • Chen BY, Lam WH, Sumalee A, Li Q, Shao H, Fang Z (2012) Finding reliable shortest paths in road networks under uncertainty. Networks and spatial economics, pp 1–26

  • Davies J, Facanha C (2007) Greenhouse gas emissions from freight trucks. International Emissions Inventory Conference. http://www.epa.gov/ttnchie1/conference/ei16/session5/davies_pres.pdf, Accessed 5 July 2012

  • Dentcheva D, Ruszczynski A (2003) Optimization with stochastic dominance constraints. SIAM J Optim 14(2):548–566

    Article  Google Scholar 

  • Dial RB (1979) A model and algorithm for multicriteria route-mode choice. Transp Res B 13(4):311–316

    Article  Google Scholar 

  • EPA U (2002) User guide to mobile 6.1 and 6.2., Technical report, Office of Transportation and Air Quality

  • EPA U (2012) User guide for moves2010b. Technical report, United States Environmental Protection Agency

  • Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Transp Res E 48(1):100–114

    Article  Google Scholar 

  • Fan Y, Nie Y (2006) Optimal routing for maximizing the travel time reliability. Netw Spat Econ 6(3–4):333–344

    Article  Google Scholar 

  • FHWA (2005) Traffic congestion and reliability: trends and advanced strategies for congestion mitigatio. Technical report, Federal Highway Administration

  • Figliozzi M (2010) Emissions minimization vehicle routing problem, paper# 10-4117. In: ‘89th annual meeting of transportation research board 2010, Washington DC

  • Friedman M, Savage L (1948) The utility analysis of choices involving risk. J Polit Econ 56:279–304

    Article  Google Scholar 

  • Hadar J, Russell WR (1971) Stochastic dominance and diversification. J Econ Theory 3:288–305

    Article  Google Scholar 

  • Hanoch G, Levy H (1969) The efficiency analysis of choices involving risk. Rev Econ Stud 36:335–346

    Article  Google Scholar 

  • Keeney RL (1993) Decisions with multiple objectives: preferences and value trade-offs. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kickhöfer B, Nagel K (2013) Towards high-resolution first-best air pollution tolls. Networks and Spatial Economics, pp 1–24

  • Koupal J, Hart C, Brzezinski D, Giannelli R, Bailey C (2002) Draft emission analysis plan for moves ghg. Technical report, Report EPA420-P-02-008. US Environmental Protection Agency

  • Levy H (1992) Stochastic dominance and expected utility: survey and analysis. Manag Sci 38:555–592

    Article  Google Scholar 

  • Li Z-C, Wang Y-D, Lam WH, Sumalee A, Choi K (2013) Design of sustainable cordon toll pricing schemes in a monocentric city. Networks and Spatial Economics, pp 1–26

  • Lin D-Y, Xie C (2011) The pareto-optimal solution set of the equilibrium network design problem with multiple commensurate objectives. Netw Spat Econ 11(4):727–751

    Article  Google Scholar 

  • Maden W, Eglese R, Black D (2009) Vehicle routing and scheduling with time-varying data: a case study. J Oper Res Soc 61(3):515–522

    Article  Google Scholar 

  • Muller A, Stoyan D (2002) Comparison methods for stochastic models and risks. Wiley, Chichester

    Google Scholar 

  • Nagurney A (2000) Congested urban transportation networks and emission paradoxes. Transp Res D 5(2):145–151

    Article  Google Scholar 

  • Nie Y, Wu X, Dillenburg JF, Nelson PC (2012) Reliable route guidance: a case study from chicago. Transp Res A 46(2):403–419

    Google Scholar 

  • Nie Y, Wu X, Homem-de-Mello T (2011) Optimal path problems with second-order stochastic dominance constraints. Networks and Spatial Economics. doi:10.1007/s11067-011-9167-6, in press

  • Penic M, Upchurch J (1992) Transyt-7f: enhancement for fuel consumption, pollution emissions, and user costs. Transportation Research Record, 1360

  • Rakha H, Ahn K, Trani A (2004) Development of vt-micro model for estimating hot stabilized light duty vehicle and truck emissions. Transp Res D 9(1):49–74

    Article  Google Scholar 

  • Rilett L, Benedek C (1994) Traffic assignment under environmental and equity objectives. Transp Res Rec 1443:92–92

    Google Scholar 

  • Rothschild M, Stiglitz JE (1970) Increasing risk I a definition. J Econ Theory 2:225–243

    Article  Google Scholar 

  • Sugawara S, Niemeier D (2002) How much can vehicle emissions be reduced?: exploratory analysis of an upper boundary using an emissions-optimized trip assignment. Transp Res Rec J Transp Res Board 1815:29–37

    Article  Google Scholar 

  • Tzeng G, Chen C (1993) Multiobjective decision making for traffic assignment. IEEE Trans Eng Manag 2:180–187

    Article  Google Scholar 

  • Whitmore GA (1970) Third degree stochastic dominance. Am Econ Rev 60:457–459

    Google Scholar 

  • Winkelman S, Bishins A, Kooshian C (2009) Cost-effective ghg reductions through smart growth and improved transportation choice. Technical report

  • Wu X, Nie Y (2011) Modeling heterogeneous risk-taking behavior in route choice: a stochastic dominance approach. Transp Res A 45(9):896–915

    Google Scholar 

  • Yang Y, Yin Y, Lu H (2013) Designing emission charging schemes for transportation conformity. J Adv Transp

  • Yin Y, Lawphongpanich S (2006) Internalizing emission externality on road networks. Transp Res D 11(4):292–301. doi:10.1002/atr.1226

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by National Science Foundation under the Award number CMMI-0928577 and National Center for Freight and Infrastructure Research and Eduction.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA

    Qianfei Li & Yu (Marco) Nie

  2. Department of Civil and Materials Engineering, University of Illinois, Chicago, IL, USA

    Suriya Vallamsundar & Jane Lin

  3. School of Business, Universidad Adolfo Ibanez, Santiago, Chile

    Tito Homem-de-Mello

Authors
  1. Qianfei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu (Marco) Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suriya Vallamsundar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jane Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tito Homem-de-Mello
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu (Marco) Nie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, Q., Nie, Y.(., Vallamsundar, S. et al. Finding Efficient and Environmentally Friendly Paths for Risk-Averse Freight Carriers. Netw Spat Econ 16, 255–275 (2016). https://doi.org/10.1007/s11067-013-9220-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11067-013-9220-8

Keywords

Search

Navigation