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.

scholarly journals Finding Your Way: Shortest Paths on Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Teresa Rexin ◽  
Mason A. Porter
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real World ◽  
Major Part ◽  
Shortest Paths ◽  
Travel Distance ◽  
Daily Lives ◽  
Total Cost ◽  
The Real ◽  
The Cost

Traveling to different destinations is a major part of our lives. We visit a variety of locations both during our daily lives and when we are on vacation. How can we find the best way to navigate from one place to another? Perhaps we can test all of the different ways of traveling between two places, but another method is to use mathematics and computation to find a shortest path between them. In this article, we discuss how to construct shortest paths and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance, the travel time, or some other quantity. We also discuss how to use shortest paths in the real world to save time and increase traveling efficiency.

scholarly journals Shortest Path with Dynamic Weight Implementation using Dijkstra’s Algorithm

2016 ◽  
Vol 7 (3) ◽  
pp. 161 ◽  
Author(s):  
Elizabeth Nurmiyati Tamatjita ◽  
Aditya Wikan Mahastama
Keyword(s):  
Shortest Path ◽  
Real World ◽  
Minimum Cost ◽  
Dijkstra’S Algorithm ◽  
Traffic Condition ◽  
The Real ◽  
Dijkstra's Algorithm ◽  
Dynamic Weight ◽  
Shortest Path Algorithms

Shortest path algorithms have been long applied to solve daily problems by selecting the most feasible route with minimum cost or time. However, some of the problems are not simple. This study applied the case using Dijkstra's algorithm on a graph representing street routes with two possible digraphs: one-way and twoway. Each cost was able to be changed anytime, representing the change in traffic condition. Results show that the usage of one way digraph in mapping the route does make the goal possible to reach, while the usage of twoway digraph may cause confusion although it is probably the possible choice in the real world. Both experiments showed that there are no additional computation stresses in re-calculating the shortest path while going halfway to reach the goal.

scholarly journals Dijkstra Algorithm for Determining the Shortest Path in the Case of Seven Hotels in Manado City Towards Manado’s Sam Ratulangi Airport.

d'CARTESIAN ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 158
Author(s):  
Yohana Permata Hutapea ◽  
Chriestie E.J.C. Montolalu ◽  
Hanny A.H. Komalig
Keyword(s):  
Graph Theory ◽  
Travel Time ◽  
Shortest Path ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Google Maps ◽  
Transportation Mode ◽  
Dijkstra's Algorithm ◽  
The Difference

Manado city has many notable tourist sites, resulting in the increase of the number of tourists visiting every year. Tourists require hotels with adequate facilities for their stay, such as 4-star hotels. After visiting Manado, tourists go back to where they come from. One of the transportation mode being used is airplanes. They then need a path to go through and not the usual one; they need the shortest path to get to Sam Ratulangi airport. Based on previous research, the shortest path is modeled by Graph Theory. Hotels will be represented as vertices, and the path from each hotels and to the airport will be represented as edges. The shortest path are searched by using Dijkstra’s Algorithm then will see the difference to shortest path from google maps. Based on the analysis results, Dijkstra’s Algorithm selects the shortest path with the smallest weight. The difference between Dijkstra’s Algorithm and google maps can be concluded that, in determining the shortest path used for the trip from the 4-star hotel to the airport, Dijkstra’s Algorithm is emphasized towards short travel distance, whereas google maps is emphasized more in short travel time.

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.

Time-dependent Dijkstra’s algorithm under bipolar neutrosophic fuzzy environment

2021 ◽  
pp. 1-10
Author(s):  
Esra Çakır ◽  
Ziya Ulukan ◽  
Tankut Acarman
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real Life ◽  
Time Dependent ◽  
Dijkstra’S Algorithm ◽  
Fuzzy Environment ◽  
Dijkstra's Algorithm ◽  
Fuzzy Graphs ◽  
Negative Statements ◽  
Shortest Path Algorithms

Determining the shortest path and calculating the shortest travel time of a complex networks are important for transportation problems. Numerous approaches has been developed to search shortest path on graphs, and one of the well-known is the Dijkstra’s label correcting algorithm. Dijkstra’s approach is capable of determining shortest path of directed or undirected graph with non-negative weighted arcs. To handle with uncertainty in real-life, the Dijkstra’s algorithm should be adapted to fuzzy environment. The weight of arc -which is the vague travel time between two nodes- can be expressed in bipolar neutrosophic fuzzy sets containing positive and negative statements. In addition, the weights of arcs in bipolar neutrosophic fuzzy graphs can be affected by time. This study proposes the extended Dijkstra’s algorithm to search the shortest path and calculate the shortest travel time on a single source time-dependent network of bipolar neutrosophic fuzzy weighted arcs. The proposed approach is illustrated, and the results demonstrate the validity of the extended algorithm. This article is intended to guide future shortest path algorithms on time-dependent fuzzy graphs.

The Cost-Effectiveness of Pre-school Peanut Oral Immunotherapy in the Real World Setting

2021 ◽  
Author(s):  
Marcus Shaker ◽  
Edmond S. Chan ◽  
Jennifer LP. Protudjer ◽  
Lianne Soller ◽  
Elissa M. Abrams ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Real World ◽  
Oral Immunotherapy ◽  
The Real ◽  
Real World Setting ◽  
The Cost

scholarly journals The Effect of Route-choice Strategy on Transit Travel Time Estimates

2019 ◽  
Author(s):  
Nate Wessel ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Imperfect Information ◽  
Optimal Path ◽  
Shortest Paths ◽  
Route Choice ◽  
Travel Times ◽  
Selection Strategies ◽  
Time Estimates ◽  
Average Travel Time

Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

scholarly journals A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS

2016 ◽  
Vol XLI-B2 ◽  
pp. 299-304 ◽  
Author(s):  
A. A. Heidari ◽  
M. R. Delavar
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Shortest Paths ◽  
The Body ◽  
Shortest Path Problems ◽  
Conventional Procedure ◽  
Path Lengths ◽  
The Cost ◽  
Uncertain Networks ◽  
Exploration Exploitation

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

scholarly journals Journey to Crime Using Dijkstra’s Algorithm

2017 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
J. O. Olusina ◽  
J. B. Olaleye
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Shortest Path ◽  
Geographic Information ◽  
Service Area ◽  
Dijkstra’S Algorithm ◽  
Crime Mapping ◽  
Cost Matrix ◽  
Dijkstra's Algorithm ◽  
Journey To Crime

This paper describes some benefits of crime mapping in a Geographic Information Systems (G.I.S.) environment. The underlining principle of Journey to Crime was discussed. Crime Spots and Police Stations in the study area were mapped, Shortest-Path, Closest Facility, Service Area and OD (Origin – Destination) Cost Matrix were determined based on Dijkstra's Algorithm. Results show that the distribution of police stations does not correspond with the spread of crime spots.

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