Todo o conteúdo produzido para a unidade curricular MS (Modelação e Simulação), para o curso em Engenharia Informática e Computação na FEUP
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Updated
May 16, 2023 - Python
Todo o conteúdo produzido para a unidade curricular MS (Modelação e Simulação), para o curso em Engenharia Informática e Computação na FEUP
A deep learning model based on reinforcement learning to construct pathlet dictionaries.
Road network graph refinement with GNNs.
This repository contains a (hack) procedure to assign geo-referenced detectors on a SUMO network, and generate the detector input file.
Python tool for generating undirected weighted graphs representing road networks.
base on Albert-László Barabási and Réka Albert BA model research for road network
Analysis of Road Network Suitability
Research work which explores the application of link prediction methods on road networks.
This repository includes all files (both scripts and results) necessary to reproduce the figures in the paper 'Using global variance-based sensitivity analysis to prioritise bridge retrofits in a regional road network subject to seismic hazard' by G. Bhattacharjee and J. W. Baker, forthcoming in Structure and Infrastructure Engineering (2021). M…
A Random Walk tool with some network analysis functions in ArcGIS.
Given network and commodity information this tool computes a feasible multi-commodity flow over time.
SARN: Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning - EDBT 2023
This is an implementation of a traffic speed predicition model.
Traffic analysis using Automatic Number Plate Recognition (ANPR) cameras.
Graph Reasoned Multi-Scale Road Segmentation in Remote Sensing Imagery
[CVPR 2023] Repository for the UrbanLaneGraph Dataset & Benchmark and the LaneGNN approach
Convert Shapefile to the Network and find number of shortest paths
A python script to demonstrate the usage of the "alpha model" for procedural generation of large-scale road networks
Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer (ICDE'23)
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