Potensi Jangkauan Wisata Harian Berdasarakan Analisis Network Sistem Informasi Geografis: Studi Kasus Kabupaten Hulu Sungai Utara

Hulu Sungai Utara Regency has a tourism mainstay of the province, swamp buffalo, which has attracted many national and international tourists. As one of the great tourist magnets, swamp buffalo tourism is not yet supported by other attractions, so tourists tend to only visit one object and leave immediately. Hulu Sungai Utara Regency has other potential tourism objects, but with a wide landscape and not optimal tourism conditions, it is necessary to plan tourism routes with daily reach. This research uses quantitative methodology with network analysis and application assistance of ArcGIS, finding the optimum route as the basic daily reach of travel. As a result, analysis of the optimum route selection results in the main tourist area as a tour package from a collection of all tourist themes (natural, religious, historical, cultural and shopping) that can be reached by a tour package for 2 days, while tourism with a natural theme can be taken 1 day by 3 package options, as well as religious tourism that can be reached within 1 day

DEVELOPMENT AND ANALYSIS OF 2D FLIGHT PLANNING SEARCH ENGINE CONSIDERING FUSION OF SWIM DATA

Flight planning is one of the essential factors of the airline operation. The selection of routes will determine the economic value of the flight. However, some conditions may prevent the flight to use the most optimum route due to airspace restriction or weather condition. The research aims to develop a search engine program that uses dynamic flight parameters that considers fusion of System Wide Information Management (SWIM) data including weather data and NOTAM to produce the most optimum route in 2D flight planning. The Dijkstra’s pathfinding is implemented in Python programming language to produce the flight plan. The navigation data used is enroute airway in Indonesian FIR regions. The scenario used is a flight from Jakarta to Makassar with duration of 2 hours flight with considering the effect of restricted airspace and weather blockage during in-flight. The study also uses the optimum route produced by the algorithm to be compared with the possible alternate routes to define how optimum the route is. Adding a restricted airspace parameter will result in a new optimum flight plan that able avoids the airspace and the most minimum distance. The effect of external wind parameter could influence the optimum route which may vary depends on the speed of the wind.

Efficient path planning of drone swarms over clustered human crowds in social events

PurposeIn this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.Design/methodology/approachThese drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.FindingsThe nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.Originality/valueThis study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

Examining the Road Networks and Locations of Firefighting Teams by Using GIS Techniques

For effective response to forest fires, the period of time necessary for the firefighting team to reach the fire site should not exceed the critical response time, where the fire is more likely to be taken under control. For this reason, the optimum route that allows the team to reach the fire site by a fire truck within the shortest time possible should be determined. Computer-aided methods such as the road network analysis are widely used in the solution of such transportation problems that require the shortest path analysis. In this study, the locations of the existing road networks and firefighting team were examined using GIS techniques in order to determine the optimum route that will provide the promptest access to the fire site. The study was carried out in the Adana Forest Enterprise Directorate, where first degree fire-sensitive forests are located. There are three firefighting teams located in the boundaries of the study area. The sites in the study area where previously occurred forest fires (15), which burned 1 ha or more forest areas, were evaluated as potential fire sites. The analysis results showed that 64,12% of the forest areas in the study area was reached by the firefighting teams within 20 minutes, which is the critical response time for first degree fire sensitive forests. It was found that the teams could reach 12 potential fire sites within the critical response time. This result revealed the necessity to establish new firefighting teams in the study area. In addition, it is thought that improving the road network density in the study area by building new roads or increasing the truck travel speed by improving the conditions of existing roads will help to solve the problem.

Offline Route planner using Web Automation

Automation refers to decreasing repetitive human work, tedious tasks, and minimizing the errors. With the correct automation tools, it's possible to automate browser tasks, web testing, and online data extraction, to fill forms, scrape data, transfer data between applications, and generate reports. The research project focuses on automating the task of placing an order of particular set of items from an online website. The main aim of the project is planning the most efficient route to visit all your stoppages and reach your destination. It automates the process of finding the most optimum route and saves a PDF of commute details to your disk.

MANIFESTATION OF OPTIMAL ROUTE ALIGNMENT SELECTION OF RURAL ROAD USING GIS AND LEAST COST PATH (LCP) MODEL WITH ENGINEERING AND ENVIRONMENTAL SUITABILITY PERSPECTIVE: A CASE STUDY IN NEPAL

For the economic growth and sustainable development of any country, the road networks play a pivotal role. Hence, the selection of best route alignment for the road networks becomes even more significant. The Geographical Information System (GIS) integration with the Least Cost Path (LCP) model is used to determine the optimum route to address sustainable road development. In this study, Dupcheswor Rural Municipality, Nuwakot, Nepal and part of Langtang National Park was taken as a study area; and engineering and environmental parameters were selected to create a cost layer. Using the Least Cost Path (LCP) model, fifteen routes were generated in the GIS. All the generated fifteen routes were compared based on cost, and the optimum route was selected based on the least cost. The optimum route in this study was derived from the hybrid theme of engineering and environmental perspectives. This study suggests further research can be done to improve preliminary to detailed road alignment planning and design coordination by considering other factors.

The robust counterpart open capacitated vehicle routing problem with time windows on waste transport problems

The optimum route for garbage transport vehicles is restricted by vehicle capacity and time windows that the garbage transport vehicle starts at the origin and does not return to the origin. The problem of transporting waste routes is a robust optimization problem where the amount of waste in an area and travel time is uncertain. In the real world, traffic jams and vehicle engine damage can cause delays. This paper proposes the robust counterpart open capacitated vehicle routing problem (denoted by RCOCVRP) with soft time windows model. The aim of RCOCVRP with soft time windows model is to find schedule and optimum route of transporting waste. This model calculation uses LINGO software and GAMS software. Finally for the evaluation of the RCOCVRP model with soft time windows on the proposed waste transportation problem is conducted so that it hasa feasible solution.

Development of a New Ship Adaptive Weather Routing Model Based on Seakeeping Analysis and Optimization

This paper provides a new adaptive weather routing model, based on the Dijkstra shortest path algorithm, aiming to select the optimal route that maximizes the ship performances in a seaway. The model is based on a set of ship motion-limiting criteria and on the weather forecast maps, providing the sea state conditions the ship is expected to encounter along the scheduled route. The new adaptive weather routing model is applied to optimize the scheduled route in the Northern Atlantic Ocean of the S175 containership, assumed as a reference vessel, based on the weather forecast data provided by the Global WAve Model (GWAM). In the analysis, both wave and combined wind/swell wave conditions are embodied to investigate the incidence on the optimum route assessment. Furthermore, the effect of the vessel speed on the optimum route detection is also investigated. Current results clearly show that it is possible to achieve appreciable improvements, up to 50% of the ship seakeeping performances, without excessively increasing the route length and the voyage duration.

Route selection for a three-dimensional elevator using deep reinforcement learning

While the car of the conventional elevator system moves only vertically in one dimension (up and down), the car of the three-dimensional elevator system travels in three perpendicular dimensions. The elevator moves through a vertical shaft to a certain floor and then the elevator serves multiple passengers distributed among different rooms at that floor. The controller decides which route should be taken to serve the passengers. This article proposes the use of deep reinforcement learning to select a route for the three-dimensional elevator. Deep reinforcement learning method learns from experiencing a large number of scenarios generated using Monte Carlo simulation offline. Once trained, deep reinforcement learning can select the route online. Numerical experimentations are used to show the superiority of deep reinforcement learning in finding an optimum or near optimum-route instantaneously. Although deep reinforcement learning is closer to finding the optimum route than other methods, finding an optimum route is not always guaranteed. Deep reinforcement learning has some limitations that include the long training time and the difficulties in training the neural networks. Practical application:Multidimensional elevators have been of expanding interest to the elevator industry as well as to traffic analysis engineers. This article demonstrates that deep reinforcement learning surpasses other methods in finding an optimum or near-optimum route for the three-dimensional elevator, and it also overcomes the challenges of the non-intelligent methods. This article can help enterprises that develop multidimensional elevators in overcoming the challenges of the controller in addition to boosting the feasibility of multidimensional elevators.