scholarly journals Graph Theory Approach to the Vulnerability of Transportation Networks

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 270
Author(s):  
Sambor Guze
Keyword(s):  
Graph Theory ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Dominating Set ◽  
Transport Sector ◽  
Critical Element ◽  
Theory Approach ◽  
Dominating Sets ◽  
Transport Networks

Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal dominating set and minimal (maximal) weighted dominating sets are proposed. This way, the exemplary academic transportation network was analyzed in two cases: stationary and dynamic. Some conclusions are presented. The main one is the fact that the methods given in this article are universal and applicable to both small and large-scale networks. Moreover, the approach can support the dynamic analysis of bottlenecks in transport networks.

scholarly journals Connectivity of Semiring Valued Graphs

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1227
Author(s):  
Shyam Sundar Santra ◽  
Prabhakaran Victor ◽  
Mahadevan Chandramouleeswaran ◽  
Rami Ahmad El-Nabulsi ◽  
Khaled Mohamed Khedher ◽  
...  
Keyword(s):  
Graph Theory ◽  
Transportation Network ◽  
Dominating Set ◽  
Network Routing ◽  
Graph Connectivity ◽  
Edge Dominating Set

Graph connectivity theory is important in network implementations, transportation, network routing and network tolerance, among other things. Separation edges and vertices refer to single points of failure in a network, and so they are often sought-after. Chandramouleeswaran et al. introduced the principle of semiring valued graphs, also known as S-valued symmetry graphs, in 2015. Since then, works on S-valued symmetry graphs such as vertex dominating set, edge dominating set, regularity, etc. have been done. However, the connectivity of S-valued graphs has not been studied. Motivated by this, in this paper, the concept of connectivity in S-valued graphs has been studied. We have introduced the term vertex S-connectivity and edge S-connectivity and arrived some results for connectivity of a complete S-valued symmetry graph, S-path and S-star. Unlike the graph theory, we have observed that the inequality for connectivity κ(G)≤κ′(G)≤δ(G) holds in the case of S-valued graphs only when there is a symmetry of the graph as seen in Examples 3–5.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals A graph theory approach of the study of macroeconomics networks

2017 ◽  
Author(s):  
Γεώργιος-Αντώνιος Σαραντίτης
Keyword(s):  
Graph Theory ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Dominating Set ◽  
Theory Approach ◽  
Minimum Dominating Set

Πολλά σύγχρονα οικονομικά συστήματα χαρακτηρίζονται από αυξημένο βαθμό πολυπλοκότητας. Οι οντότητες αυτών των συστημάτων αναπτύσσουν διακριτές, αναδυόμενες και μη γραμμικές συμπεριφορές που δεν μπορούν να περιγραφούν πλήρως με οικονομετρικές τεχνικές. Τα τελευταία χρόνια, λόγω της γρήγορης αύξησης της υπολογιστικής ισχύος και της εξέλιξης των αλγορίθμων, η επιστήμη της Ανάλυσης Δικτύων ενσωματώθηκε στην ανάλυση τέτοιων πολύπλοκων οικονομικών συστημάτων, συμπληρώνοντας τη χρήση της οικονομετρίας.Μια κοινώς χρησιμοποιούμενη τεχνική στο πλαίσιο της Ανάλυσης Δικτύων είναι το Minimum Spanning Tree (MST). Το MST παράγει ένα υπο-δίκτυο του αρχικού δικτύου στο οποίο είναι συνδεδεμένοι όλοι οι κόμβοι έτσι ώστε να μην υπάρχουν βρόχοι. Ωστόσο, το MST φέρει κάποια εγγενή μειονεκτήματα που προέρχονται άμεσα από τη διαδικασία αλγοριθμικού προσδιορισμού του και μπορεί να το καταστήσουν ακατάλληλο για τη μελέτη οικονομικών δικτύων. Αυτή η διατριβή αποσκοπεί στο να αναδείξει τα μειονεκτήματα του MST όταν χρησιμοποιείται στα οικονομικά δίκτυα και να επισημάνει τα πλεονεκτήματα μιας νέας τεχνικής βελτιστοποίησης, που ονομάζεται Threshold-Minimum Dominating Set (T-MDS) ως μια καταλληλότερη λύση. Επιπλέον, εισάγεται το Threshold Weighted - Minimum Dominating Set (TW-MDS), το οποίο διατηρεί όλα τα πλεονεκτήματα του T-MDS και, ανάλογα με το δεδομένο σύνολο, μπορεί να είναι πιο κατάλληλο για διαχρονικές αναλύσεις που εκτελούνται στην πάροδο του χρόνου.Η ανωτερότητα των T-MDS και TW-MDS σε σχέση με το κλασικό MST αρχικά επισημαίνεται σε αυτή τη διατριβή με κατάλληλα θεωρητικά παραδείγματα. Στη συνέχεια συνεχίζουμε παρέχοντας ένα ευρύ φάσμα μακροοικονομικών εφαρμογών: τον συγχρονισμό των οικονομικών κύκλων, την εξέλιξη της ανισότητας εισοδήματος και τη μέτρηση του πληθωρισμού πυρήνα. Με αυτόν τον τρόπο τονίζουμε την καταλληλότητα των προτεινόμενων μεθοδολογιών στη μακροοικονομική ανάλυση. Έτσι, αυτή η διατριβή έχει διπλή συμβολή στην ανάλυση των σύνθετων οικονομικών δικτύων: από τη θεωρητική πλευρά επεκτείνει τη σχετική βιβλιογραφία παρέχοντας ένα πιο κατάλληλο εργαλείο από αυτό που χρησιμοποιείται προς το παρόν, ενώ από την εμπειρική πλευρά παρέχει νέα αποτελέσματα από τις διαφορετικές οικονομικές Εφαρμογές του T-MDS.

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

Mathematical Modelling for Transportation With Application to Airline Transportation Network

2021 ◽  
pp. 28-51
Author(s):  
Sadiqah Almarzooq ◽  
Njwd Albishi
Keyword(s):  
Graph Theory ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Transportation Networks ◽  
Transportation Network ◽  
Weighted Graph ◽  
Search Method ◽  
Nearest Neighbour ◽  
Water Pipelines ◽  
Regional Airports

Graph theory is a basic tool to solve real-world problems such as communication between people, water pipelines, and transportation networks. A transportation network can be modeled as connected weighted graph. This chapter starts by introducing some fundamental concepts of graph theory to be applied to three main problems: the minimum spanning tree, the shortest path, and the travel salesperson. The authors discuss some appropriated algorithms such as depth first algorithm, Prim's algorithm, Kruskal's algorithm, Dijkstra's algorithm, the nearest neighbour algorithm, the minimum spanning tree depth first search method (MST-DFS) algorithm, and the Christofides' algorithm to solve these problems and apply them the airlines network between international and regional airports in Saudi Arabia.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

Definition and Evaluation Modeling of Transportation Network Layout Adaptability

2011 ◽  
Vol 71-78 ◽  
pp. 3938-3941 ◽  
Author(s):  
Jie Gao ◽  
Mei Xiang Wu ◽  
Chen Qiang Yin
Keyword(s):  
Heuristic Algorithm ◽  
Coefficient Of Variation ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Optimization Methods ◽  
Network Capacity ◽  
Network Layout ◽  
Proposed Model ◽  
Coordination Degree

According to the reliability theories and the characteristics of transportation networks, the layout adaptability is defined as the coupling and coordination degree of transportation network capacity and demand firstly. Then a layout adaptability model is built adopting the optimization methods, degree of layout adaptability index and coefficient of variation are used to evaluate the adaptability of scale and distribution respectively. Meanwhile, the heuristic algorithm suitable for large scale is designed to solve the proposed model. At last, a numerical example and its results are provided to demonstrate the validity of the proposed model and algorithm.

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

Sign in / Sign up

Export Citation Format

Share Document