scholarly journals Pemetaan Bangunan Tiga Dimensi Untuk Pemodelan Jalur Evakuasi Darurat

2017 ◽  
Author(s):  
debby nurliza ulhaq ◽  
Budhy Soeksmantono ◽  
Ketut Wikantika
Keyword(s):  
Shortest Paths ◽  
Three Dimensional ◽  
Emergency Evacuation ◽  
Dijkstra’S Algorithm ◽  
Related Data ◽  
Building Model ◽  
Dijkstra's Algorithm ◽  
3D Building Model ◽  
Road Data ◽  
Evacuation Route

AbstrakMitigasi bencana merupakan salah satu hal penting yang harus dipertimbangkan terutama dalam konstruksi bangunan karena hal tersebut cukup rumit terlebih apabila dikaitkan dengan fakta tidak adanya informasi yang dapat digunakan untuk orang-orang menyelamatkan diri mereka sendiri. Maka dari itu, makalah ilmiah ini memperkenalkan mengenai network analysist untuk rute evakuasi darurat yang bertujuan untuk mencari rute terbaik menuju tempat aman seperti titik berkumpul tergantung pada situasi terkini. Pembuatan keputusan berdasarkan rute yang tepat akan dipilih berdasarkan kategori usia korban dan kondisi saat bencana terjadi, sehingga dapat mengurangi dampak buruk yang akan muncul. Algoritma Dijkstra menunjukan suatu algoritma perncarian rute terpendek antara gedung dan titik berkumpul dengan menghubungkan keduanya melalui data jalan. Model rute evakuasi ini dibentuk dengan menggunakan kombinasi antara model bangunan tiga dimensi yang dibangun dari data LiDAR, orthophoto, dan data lainnya yang berkaitan dengan pemodelan. Bangunan tiga dimensi dapat digunakan dalam manajemen bencana dan respon darurat karena dapat menyediakan informasi penting seperti lokasi bangunan. Evaluasi dari model yang diajukan meningkatkan kemampuan penyelamatan diri sendiri yang mengarah pada berkurangnya dampak buruk yang akan terjadi.Kata kunci: Evakuasi Darurat, Algoritma Dijkstra, LiDAR, pemodelan bangunan 3D AbstractMitigation is an important thing to be considered especially in building construction because it is quite complicated due to the fact that much of the information is unavailable for people to rescue themselves. Hence, this paper introduces about network analysis for evacuation emergency route which aims at finding the best route to the secured place such as the closest assembly point depends on the situation. Thus, decision making regarding the proper route to be chosen depends of the victim age category and current condition to minimize impact that can be generated. Dijkstra’s Algorithm is presented an algorithm for finding the shortest paths between building and assembly point by linking them through road data. This emergency evacuation route model is constructed by combining with three dimensional building model which constructed by using LiDAR data, orthophoto, and the other related data. Three dimensional geo data can be used in disaster management and emergency response because they may provide valuable information such as location of the building. The evaluation of the proposed model for a case study building improve self-sustaining which lead to chances of less adverse effects can appear.Keywords: Emergency Evacuation, Dijkstra’s Algorithm, LiDAR, 3D building model

scholarly journals Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 269 ◽  
Author(s):  
Rhyd Lewis
Keyword(s):  
Waiting Times ◽  
Shortest Paths ◽  
Dijkstra’S Algorithm ◽  
Weighted Graphs ◽  
Worst Case ◽  
Sparse Graphs ◽  
Graph Expansion ◽  
Advantages And Disadvantages ◽  
Dijkstra's Algorithm ◽  
Dense Graphs

In this paper we review many of the well-known algorithms for solving the shortest path problem in edge-weighted graphs. We then focus on a variant of this problem in which additional penalties are incurred at the vertices. These penalties can be used to model things like waiting times at road junctions and delays due to transfers in public transport. The usual way of handling such penalties is through graph expansion. As an alternative, we propose two variants of Dijkstra’s algorithm that operate on the original, unexpanded graph. Analyses are then presented to gauge the relative advantages and disadvantages of these methods. Asymptotically, compared to using Dijkstra’s algorithm on expanded graphs, our first variant is faster for very sparse graphs but slower with dense graphs. In contrast, the second variant features identical worst-case run times.

Use of topographic LiDAR point clouds for building reconstruction

2021 ◽  
Author(s):  
Yipeng Yuan
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Roof ◽  
Building Model ◽  
Building Models ◽  
Urban Models ◽  
Elevation Data ◽  
3D Building Model ◽  
Building Reconstruction

Demand for three-dimensional (3D) urban models keeps growing in various civil and military applications. Topographic LiDAR systems are capable of acquiring elevation data directly over terrain features. However, the task of creating a large-scale virtual environment still remains a time-consuming and manual work. In this thesis a method for 3D building reconstruction, consisting of building roof detection, roof outline extraction and regularization, and 3D building model generation, directly from LiDAR point clouds is developed. In the proposed approach, a new algorithm called Gaussian Markov Random Field (GMRF) and Markov Chain Monte Carlo (MCMC) is used to segment point clouds for building roof detection. The modified convex hull (MCH) algorithm is used for the extraction of roof outlines followed by the regularization of the extracted outlines using the modified hierarchical regularization algorithm. Finally, 3D building models are generated in an ArcGIS environment. The results obtained demonstrate the effectiveness and satisfactory accuracy of the developed method.

scholarly journals Finding Your Way: Shortest Paths on Networks

2020 ◽  
Author(s):  
Teresa Rexin ◽  
Mason A. Porter
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real World ◽  
Shortest Paths ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Total Cost ◽  
The Real ◽  
Dijkstra's Algorithm ◽  
The Cost

Traveling to different destinations is a big part of our lives. How do we know the best way to navigate from one place to another? Perhaps we could test all of the different ways of traveling between two places, but another method is using mathematics and computation to find a shortest path. We discuss how to find a shortest path and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance or travel time. We also discuss how shortest paths can be used in the real world to save time and increase traveling efficiency.

A Three-Dimensional Dijkstra's algorithm for multi-objective ship voyage optimization

2019 ◽  
Vol 186 ◽  
pp. 106131 ◽  
Author(s):  
Helong Wang ◽  
Wengang Mao ◽  
Leif Eriksson
Keyword(s):  
Three Dimensional ◽  
Dijkstra’S Algorithm ◽  
Multi Objective ◽  
Dijkstra's Algorithm

Modification of Dijkstra's algorithm for safest and shortest path during emergency evacuation

2015 ◽  
Vol 9 ◽  
pp. 1531-1541 ◽  
Author(s):  
Khyrina Airin Fariza Abu Samah ◽  
Burairah Hussin ◽  
Abd Samad Hasan Basari
Keyword(s):  
Shortest Path ◽  
Emergency Evacuation ◽  
Dijkstra’S Algorithm ◽  
Dijkstra's Algorithm

scholarly journals Finding Shortest Path for Road Network Using Dijkstra’s Algorithm

2019 ◽  
Vol 1 (2) ◽  
pp. 41-45
Author(s):  
Md. Almash Alam ◽  
Md. Omar Faruq
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Shortest Paths ◽  
Road Networks ◽  
Transportation Systems ◽  
Shortest Path Problem ◽  
Dijkstra’S Algorithm ◽  
Traffic Condition ◽  
Shortest Path Problems ◽  
Dijkstra's Algorithm

Roads play a Major role to the people live in various states, cities, town and villages, from each and every day they travel to work, to schools, to business meetings, and to transport their goods. Even in this modern era whole world used roads, remain one of the most useful mediums used most frequently for transportation and travel. The manipulation of shortest paths between various locations appears to be a major problem in the road networks. The large range of applications and product was introduced to solve or overcome the difficulties by developing different shortest path algorithms. Even now the problem still exists to find the shortest path for road networks. Shortest Path problems are inevitable in road network applications such as city emergency handling and drive guiding system. Basic concepts of network analysis in connection with traffic issues are explored. The traffic condition among a city changes from time to time and there are usually huge amounts of requests occur, it needs to find the solution quickly. The above problems can be rectified through shortest paths by using the Dijkstra’s Algorithm. The main objective is the low cost of the implementation. The shortest path problem is to find a path between two vertices (nodes) on a given graph, such that the sum of the weights on its constituent edges is minimized. This problem has been intensively investigated over years, due to its extensive applications in graph theory, artificial intelligence, computer network and the design of transportation systems. The classic Dijkstra’s algorithm was designed to solve the single source shortest path problem for a static graph. It works starting from the source node and calculating the shortest path on the whole network. Noting that an upper bound of the distance between two nodes can be evaluated in advance on the given transportation network.

scholarly journals Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds

2021 ◽  
Vol 13 (6) ◽  
pp. 1107
Author(s):  
Linfu Xie ◽  
Han Hu ◽  
Qing Zhu ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Linear Optimization ◽  
Three Dimensional ◽  
Recovery Process ◽  
Point Clouds ◽  
Rule Based ◽  
Building Model ◽  
Building Models ◽  
Potential Applications ◽  
3D Building Model ◽  
Oblique Images

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.

scholarly journals Use of topographic LiDAR point clouds for building reconstruction

2021 ◽  
Author(s):  
Yipeng Yuan
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Roof ◽  
Building Model ◽  
Building Models ◽  
Urban Models ◽  
Elevation Data ◽  
3D Building Model ◽  
Building Reconstruction

Demand for three-dimensional (3D) urban models keeps growing in various civil and military applications. Topographic LiDAR systems are capable of acquiring elevation data directly over terrain features. However, the task of creating a large-scale virtual environment still remains a time-consuming and manual work. In this thesis a method for 3D building reconstruction, consisting of building roof detection, roof outline extraction and regularization, and 3D building model generation, directly from LiDAR point clouds is developed. In the proposed approach, a new algorithm called Gaussian Markov Random Field (GMRF) and Markov Chain Monte Carlo (MCMC) is used to segment point clouds for building roof detection. The modified convex hull (MCH) algorithm is used for the extraction of roof outlines followed by the regularization of the extracted outlines using the modified hierarchical regularization algorithm. Finally, 3D building models are generated in an ArcGIS environment. The results obtained demonstrate the effectiveness and satisfactory accuracy of the developed method.

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