Enabling time-dependent uncertain eco-weights for road networks

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Enabling time-dependent uncertain eco-weights for road networks. / Hu, Jilin; Yang, Bin; Jensen, Christian S.; Ma, Yu.

In: Geoinformatica, Vol. 21, No. 1, 01.2017, p. 57-88.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Hu, J, Yang, B, Jensen, CS & Ma, Y 2017, 'Enabling time-dependent uncertain eco-weights for road networks', Geoinformatica, vol. 21, no. 1, pp. 57-88. https://doi.org/10.1007/s10707-016-0272-z

APA

Hu, J., Yang, B., Jensen, C. S., & Ma, Y. (2017). Enabling time-dependent uncertain eco-weights for road networks. Geoinformatica, 21(1), 57-88. https://doi.org/10.1007/s10707-016-0272-z

CBE

MLA

Vancouver

Hu J, Yang B, Jensen CS, Ma Y. Enabling time-dependent uncertain eco-weights for road networks. Geoinformatica. 2017 Jan;21(1):57-88. https://doi.org/10.1007/s10707-016-0272-z

Author

Hu, Jilin ; Yang, Bin ; Jensen, Christian S. ; Ma, Yu. / Enabling time-dependent uncertain eco-weights for road networks. In: Geoinformatica. 2017 ; Vol. 21, No. 1. pp. 57-88.

Bibtex

@article{71ade764900a4708954fc001281aa309,
title = "Enabling time-dependent uncertain eco-weights for road networks",
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.",
keywords = "Eco-routing, Road network, Time-dependent uncertain edge weight",
author = "Jilin Hu and Bin Yang and Jensen, {Christian S.} and Yu Ma",
year = "2017",
month = jan,
doi = "10.1007/s10707-016-0272-z",
language = "English",
volume = "21",
pages = "57--88",
journal = "Geoinformatica",
issn = "1384-6175",
publisher = "Springer New York LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Enabling time-dependent uncertain eco-weights for road networks

AU - Hu, Jilin

AU - Yang, Bin

AU - Jensen, Christian S.

AU - Ma, Yu

PY - 2017/1

Y1 - 2017/1

N2 - 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.

AB - 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.

KW - Eco-routing

KW - Road network

KW - Time-dependent uncertain edge weight

UR - http://www.scopus.com/inward/record.url?scp=84988932743&partnerID=8YFLogxK

U2 - 10.1007/s10707-016-0272-z

DO - 10.1007/s10707-016-0272-z

M3 - Journal article

AN - SCOPUS:84988932743

VL - 21

SP - 57

EP - 88

JO - Geoinformatica

JF - Geoinformatica

SN - 1384-6175

IS - 1

ER -