Paper Info
Title
Automatic Inference of Road and Pedestrian Networks From Spatial-Temporal Trajectories
Abstract
Mining GPS traces is proposed, as an alternative to surveying and satellite image processing, for constructing road and pedestrian networks, trading out the constructed network’s perfectness for substantially reduced cost and time. To construct a road or pedestrian network, the proposed algorithm requires only GPS traces, which can be collected with low or no cost using crowd-sourced repositories such as open street map. Network construction algorithms in the literature, mostly do not provide enough details about different steps of constructing a network. Besides, their evaluations are mostly visual, qualitative, and limited to one dataset. This paper not only describes all the steps involved in constructing a network but also quantitatively evaluates the algorithm (via three metrics: precision, completeness, and topology correctness) using numerous datasets from different sources and discusses its time complexity. The proposed algorithm’s distinguishing strengths can be summarized as: 1) fixing the constructed network’s topological errors while many existing algorithms overlook the constructed network’s topological integrity; 2) imposing no restrictions on the shape of GPS traces while many existing algorithms include such implicit or explicit constraints, e.g. GPS traces cannot include U turns; 3) being fully automatic and involving no subjective judgments or human interventions for filtering GPS points unlike many existing algorithms; and 4) achieving high accuracies in practice. The proposed algorithm includes six hyperparameters, all related to GPS traces’ geometrical traits and optimized during evaluation.
Year
DOI
Venue
2019
10.1109/TITS.2019.2916588
IEEE Transactions on Intelligent Transportation Systems
Keywords
Field
DocType
Global Positioning System,Roads,Image segmentation,Turning,Detectors,Data mining
Computer vision,Pedestrian,Artificial intelligence,Automatic inference,Engineering
Journal
Volume
Issue
ISSN
20
12
1524-9050
Citations 
PageRank 
References 
4
0.54
0
Authors
1
Name
Order
Citations
PageRank
Mahdi Hashemi1539.43