Data Management of Large-Scale Transportation Networks

1996 ◽  
pp. 359-372
Author(s):  
N. Christofides ◽  
H. O. Badra ◽  
Y. M. Sharaiha ◽  
G. Salkin
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Transportation Networks

scholarly journals PowerSystems.jl — A power system data management package for large scale modeling

SoftwareX ◽  
2021 ◽  
Vol 15 ◽  
pp. 100747
Author(s):  
José Daniel Lara ◽  
Clayton Barrows ◽  
Daniel Thom ◽  
Dheepak Krishnamurthy ◽  
Duncan Callaway
Keyword(s):  
Power System ◽  
Data Management ◽  
Large Scale ◽  
Scale Modeling ◽  
System Data ◽  
Large Scale Modeling

Distributed-to-Centralized Data Management: A New Sense of Large-Scale ICT Management of Smart City IoT Networks

2020 ◽  
Vol 3 (3) ◽  
pp. 76-82
Author(s):  
Amir Sinaeepourfard ◽  
John Krogstie ◽  
Souvik Sengupta
Keyword(s):  
Data Management ◽  
Smart City ◽  
Large Scale

Multi-modal transportation recommendation with unified route representation learning

2020 ◽  
Vol 14 (3) ◽  
pp. 342-350
Author(s):  
Hao Liu ◽  
Jindong Han ◽  
Yanjie Fu ◽  
Jingbo Zhou ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Representation Learning ◽  
Transportation Systems ◽  
Graph Representation ◽  
Dynamic Graph ◽  
Arbitrary Length ◽  
Task Learning ◽  
Semantic Coherence ◽  
Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

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