Skip to main content

Advertisement

SpringerLink
Log in

Enabling time-dependent uncertain eco-weights for road networks

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Reduction of greenhouse gas (GHG) emissions from transportation is an essential part of the efforts to prevent global warming and climate change. Eco-routing, which enables drivers to use the most environmentally friendly routes, is able to substantially reduce GHG emissions from vehicular transportation. The foundation of eco-routing is a weighted-graph representation of a road network in which road segments, or edges, are associated with eco-weights that capture the GHG emissions caused by traversing the edges. Due to the dynamics of traffic, the eco-weights are best modeled as being time dependent and uncertain. We formalize the problem of assigning a time-dependent, uncertain eco-weight to each edge in a road network based on historical GPS records. In particular, a sequence of histograms is employed to describe the uncertain eco-weight of an edge at different time intervals. Compression techniques, including histogram merging and buckets reduction, are proposed to maintain compact histograms while retaining their accuracy. In addition, to better model real traffic conditions, virtual edges and extended virtual edges are proposed in order to represent adjacent edges with highly dependent travel costs. Based on the techniques above, different histogram aggregation methods are proposed to accurately estimate time-dependent GHG emissions for routes. Based on a 200-million GPS record data set collected from 150 vehicles in Denmark over two years, a comprehensive empirical study is conducted in order to gain insight into the effectiveness and efficiency of the proposed approach.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Notes

  1. http://www.openstreetmap.org/

References

  1. Ahn K, Rakha H, Trani A, Van Aerde M (2002) Estimating Vehicle Fuel Consumption and Emissions based on Instantaneous Speed and Acceleration Levels. J Transp Eng 128(2):182–190

    Article  Google Scholar 

  2. Andersen O, Jensen CS, Torp K, Yang B (2013) Ecotour: Reducing the environmental footprint of vehicles using eco-routes. In: Proc MDM, 338–340

  3. Chen A, Ji Z (2005) Path finding under uncertainty. J Adv Transp 39(1):19–37

    Article  Google Scholar 

  4. Ding B, Yu JX, Qin L (2008) Finding time-dependent shortest paths over large graphs. In: Proc EDBT, 205–216

  5. Disser Y, Müller-Hannemann M., Schnee M (2008) Multi-criteria shortest paths in time-dependent train networks. In: Proc WEA, 347–361

  6. Guo C, Ma Y, Yang B, Jensen CS, Kaul M (2012) Ecomark: evaluating models of vehicular environmental impact. In: Proc ACM SIGSPATIAL, 269–278

  7. Guo C, Yang B, Andersen O, Jensen CS, Torp K (2015) Ecomark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. GeoInformatica 19(3):567–599

    Article  Google Scholar 

  8. Guo C, Yang B, Andersen O, Jensen CS, Torp K (2015) Ecosky: Reducing vehicular environmental impact through eco-routing. In: Proc ICDE, 1412–1415

  9. Hua M, Pei J (2010) Probabilistic path queries in road networks: traffic uncertainty aware path selection. In: Proc EDBT, 347–358

  10. Cormode G, Garofalakis MN (2009) Histograms and Wavelets on Probabilistic Data. In: Proc ICDE, 293–304

  11. Ioannidis Y (2003) The history of histograms (abridged). In: Proc VLDB, 19–30

  12. Kanoulas E, Du Y, Xia T, Zhang D (2012) Finding fastest paths on a road network with speed patterns. In: Proc ICDE, 10–21

  13. Demiryurek U, Pan B, Banaei-Kashani F, Shahabi C (2009) Towards modeling the traffic data on road networks. In: Proc IWCTS, 13–18

  14. Ali RY, Gunturi V, Shekhar S (2015) Spatial big data for eco-routing services: computational challenges and accomplishment. In: SIGSPATIAL Special, 6(2): 19–25

  15. Lim S, Sommer C, Nikolova E, Rus D (2012) Practical route planning under delay uncertainty: Stochastic shortest path queries. In: Proc Robotics: Science and Systems, 249–264

  16. Ma Y, Yang B, Jensen CS (2014) Enabling time-dependent uncertain eco-weights for road networks. In: Proc. GeoRich@SIGMOD, 1–6

  17. Miller-Hooks E, Mahmassani HS (1998) Optimal routing of hazardous materials in stochastic, time-varying transportation networks. Transportation Research Record: Journal of the Transportation Research Board 1645(1):143–151

    Article  Google Scholar 

  18. Nikolova E, Brand M, Karger DR (2006) Optimal route planning under uncertainty. In: Proc ICAPS, 131–141

  19. Pereira FC, Costa H, Pereira NM (2009) An off-line map-matching algorithm for incomplete map databases. Eur Transp Res Rev 1(3):107–124

    Article  Google Scholar 

  20. Wijeratne AB, Turnquist MA, Mirchandani PB (1993) Multiobjective routing of hazardous materials in stochastic networks. Eur J Oper Res 65(1):33–43

    Article  Google Scholar 

  21. Yang B, Guo C, Jensen CS (2013) Travel cost inference from sparse, spatio-temporally correlated time series using markov models. PVLDB 6(9):769–780

    Google Scholar 

  22. Dai J, Yang B, Guo C, Jensen CS, Hu J (2016) Path Cost Distribution Estimation Using Trajectory Data. PVLDB 10(3)

  23. Dai J, Yang B, Guo C, Ding Z (2015) Personalized route recommendation using big trajectory data. In: Proc ICDE, 543–554

  24. Kaul M, Yang B, Jensen CS (2013) Building Accurate 3D Spatial Networks to Enable Next Generation Intelligent Transportation Systems. In: Proc MDM, 137–146

  25. Yang B, Guo C, Jensen CS, Kaul M, Shang S (2014) Stochastic skyline route planning under time-varying uncertainty. In: Proc ICDE, 136–147

  26. Yang B, Guo C, Ma Y, Jensen CS (2015) Toward personalized, context-aware routing. VLDB J 24(2):297–318

    Article  Google Scholar 

  27. Yang B, Kaul M, Jensen CS (2014) Using incomplete information for complete weight annotation of road networks. IEEE Trans Knowl Data Eng 26(5):1267–1279

    Article  Google Scholar 

  28. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: Proc SIGSPATIAL, 99–108

  29. Ahn K, Rakha H, Trani A, Van Aerde M (2002) Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J Transp Eng 128(2):182–190

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Reduction project that is funded by the European Commission as FP7-ICT-2011-7 STREP project number 288254, by the DiCyPS project, and by a grant from the Obel Family Foundation.

Author information

Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Jilin Hu, Bin Yang & Christian S. Jensen

  2. Department of Computer Science, Aarhus University, Aarhus, Denmark

    Yu Ma

Authors
  1. Jilin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian S. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, J., Yang, B., Jensen, C.S. et al. Enabling time-dependent uncertain eco-weights for road networks. Geoinformatica 21, 57–88 (2017). https://doi.org/10.1007/s10707-016-0272-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-016-0272-z

Keywords

Search

Navigation