A route planner using the A* algorithm (uses Open Street Map data)

Find the shortest route using A* algorithm and graphs (Route Planner application)

A travel planner that calculates the optimal travel route by plane or bus based on Dijkstra's famous graph algorithm

"A smart C++ trip planner that finds the shortest travel routes using Dijkstra’s algorithm."

A Route Planner based on A* search algorithm implemented in C++ to find the best route in a given OpenStreetMap

Path planner for UAV that search the best flight route through the clouds from NOAA weather forecast. The pathfinding algorithm tested are A* and Dijkstra

Rescue 1122 Route Planner is a Artificial Intelligence project which implements the A-star algorithm using Python and the simulation is done using Pygame.

Travel Route Planner with Simulated Annealing Algorithm

PoliGPS is an algorithm able to solve a Multi-Objective Minimum Path Problem. The mathematical model relies completely on "A multi-objective time-dependent route planner: a real world application to Milano city" (2014) by Bruglieri et al. The codes are completely programmed by me and are available under the GNU General Public License v3.

Genetic Algorithm based Dynamic Scheduler and Route Planner for Ambulance Management System, developed in Python.

DSA Project | IOE, Pulchowk Campus | Department of Electronics and Computer Engineering | A travel planner that calculates the optimal travel route by plane or bus based on Dijkstra's algorithm.

Implementation of the A* Search Algorithm. The application will implement a Google-maps-style route planner.

Implement a flight route planner that uses graphs, dijkstra's algorithm to find the shortest route between two airports. Clean coding practices in Python, algorithms, CI/CD workflow.

City Route Planner is a Java-based console application that models a city road network using graph algorithms to compute shortest paths and detect cycles. It enables dynamic management of intersections and roads while optimizing routes using Dijkstra’s algorithm and DFS-based cycle detection.

A C++/Qt flight planner for MSFS, X-Plane, and Prepar3D. Craft optimal routes with A algorithms, visualize on MapTiler maps, and export to .pln, .fms, or .lnmpln. Features a sleek GUI with real-time weather, mock charts, and pilot tools.

Localized motion planning on-board an Autonomous Vehicle (AV) using SLAM (Simultaneous Localization and Mapping) has been implemented. This individual motion planning of the route by AVs does not account for other AVs' routes, urgency, other such parameters and can lead to deadlock situations. A centralized computation unit that provides the registered AVs with a optimal motion plan is proposed by this project. A central planner will find a optimal motion plan for all the AVs in its control area. This central planner will consider the fuel constraint, priorities, speed constraint, etc while planning. This project proposes the use of Particle Swarm Optimization algorithm for meta-heuristic centralized decoupled motion planning. A classic decoupled centralized motion planning algorithm will be implemented for baseline comparison. Advanced algorithm like Grey Wolf Optimization will be studied and explored in the project.

Team implementation of a C++ algorithm concerning a variant of a graph traversal problem given a maximum number of traversable edges.

A multi destination most optimal route planner using modified A* algorithm.

Map route and planner calculates the shortest paths among the nodes/location of Pakistan using Dijkstra Algorithm and Graphs data structure.

Trip Planner SwiftUI MVVM, show the cheapest flight route on a​ map, with Dijkstra's algorithm for finding the shortest paths between nodes in a graph

Campus Compass 🧭🏫: Smart campus route planner using Dijkstra, A, & Floyd–Warshall algorithms. 🚦🌤️♿ Adjusts for traffic, weather, and accessibility to find the optimal path between campus locations.

A route planner app utilizing A* Search Algorithm and C++

A algorithm based route planner with dynamic data of traffic prediction for route planning

Open Street Maps Route Planner using A* Algorithm implemented in C++

A Open Street Route Planner which works on A-Star algorithm and finds path from source to destination on map.

Travel Smart Planner is a Java-based console application that helps users find the shortest route, plan trips within a budget, and optimize multi-city travel using Dijkstra's, Greedy, and Divide & Conquer algorithms.

🌍✈️ **Dijkstra World Flight** – A JavaFX-based flight planner using Dijkstra's Algorithm to find the shortest path between cities. Choose routes via ComboBox or map, and optimize by distance, cost, or time. Visualize paths on a world map with real-time updates! 🚀

Driving in metropolitan areas means hours of traffic jams, which results in a reduction of productivity for both the city and people’s lives. Therefore, urban air mobility/air taxis are proposed as a solution to this overwhelming problem. As a consequence, this study is focused on determining the number of feasible trips that could fly over an urban area (in this study, the City of Chicago) considering passenger costs for the air taxi trip using short-, medium-, and long-term operating cost models suggested by Uber. To estimate the number of trips, the study starts with historical data recording the number of commutes in the city together with the origin and destination coordinates for these commute trips. The distribution of these commutes over one day estimates the number of trips at a given hour of the day. The Open Trip Planner API calculates the duration of these commutes, which allows calculation of the effective cost of the trip when using an air taxi for part of the trip. The computational model for this study compares the effective cost of a trip using the air taxi, for part of all the trip, to the effective costs of using only ground-based public transit or a passenger-owned automobile for the same trip. Trips where effective cost is lowest when using air taxi provide a count of the number of UAM trips conducted over the city. Following this initial estimate of the urban airspace density, the model can subsequently assess the impact of additional considerations about restricted areas of the city (in Chicago, the class B airspace for Midway Airport, is one example). Thereby, a first approach of feasible routes is implemented using an optimization path algorithm.

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Implement Google map route planner algorithm A*(Best estimated total path cost first)