Abstract
In this paper we address the challenge of inferring the road network of a city from crowd-sourced GPS traces. While the problem has been addressed before, our solution has the following unique characteristics: (i) we formulate the road network inference problem as a network alignment optimization problem where both the nodes and edges of the network have to be inferred, (ii) we propose both an offline (Kharita) and an online (Kharita∗) algorithm which are intuitive and capture the key aspects of the optimization formulation but are scalable and accurate. The Kharita∗ in particular is, to the best of our knowledge, the first known online algorithm for map inference, (iii) we test our approach on two real data sets and both our code and data sets have been made available for research reproducibility.
| Original language | English |
|---|---|
| Pages | 135-143 |
| Number of pages | 9 |
| Publication status | Published - 1 Jan 2018 |
| Event | 2018 SIAM International Conference on Data Mining, SDM 2018 - San Diego, United States Duration: 3 May 2018 → 5 May 2018 |
Other
| Other | 2018 SIAM International Conference on Data Mining, SDM 2018 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 3/5/18 → 5/5/18 |
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ASJC Scopus subject areas
- Computer Science Applications
- Software
Cite this
Robust road map inference through network alignment of trajectories. / Stanojevic, Rade; Abbar, Sofiane; Thirumuruganathan, Saravanan; Chawla, Sanjay; Filali, Fethi; Aleimat, Ahid.
2018. 135-143 Paper presented at 2018 SIAM International Conference on Data Mining, SDM 2018, San Diego, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Robust road map inference through network alignment of trajectories
AU - Stanojevic, Rade
AU - Abbar, Sofiane
AU - Thirumuruganathan, Saravanan
AU - Chawla, Sanjay
AU - Filali, Fethi
AU - Aleimat, Ahid
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper we address the challenge of inferring the road network of a city from crowd-sourced GPS traces. While the problem has been addressed before, our solution has the following unique characteristics: (i) we formulate the road network inference problem as a network alignment optimization problem where both the nodes and edges of the network have to be inferred, (ii) we propose both an offline (Kharita) and an online (Kharita∗) algorithm which are intuitive and capture the key aspects of the optimization formulation but are scalable and accurate. The Kharita∗ in particular is, to the best of our knowledge, the first known online algorithm for map inference, (iii) we test our approach on two real data sets and both our code and data sets have been made available for research reproducibility.
AB - In this paper we address the challenge of inferring the road network of a city from crowd-sourced GPS traces. While the problem has been addressed before, our solution has the following unique characteristics: (i) we formulate the road network inference problem as a network alignment optimization problem where both the nodes and edges of the network have to be inferred, (ii) we propose both an offline (Kharita) and an online (Kharita∗) algorithm which are intuitive and capture the key aspects of the optimization formulation but are scalable and accurate. The Kharita∗ in particular is, to the best of our knowledge, the first known online algorithm for map inference, (iii) we test our approach on two real data sets and both our code and data sets have been made available for research reproducibility.
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M3 - Paper
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