Traffic-Aware Navigation in Road Networks
- URL: http://arxiv.org/abs/2602.02158v1
- Date: Mon, 02 Feb 2026 14:35:49 GMT
- Title: Traffic-Aware Navigation in Road Networks
- Authors: Sarah Nassar,
- Abstract summary: This project compares three graph search approaches for the task of traffic-aware navigation in Kingston's road network.<n>Dijkstra's and A* resulted in the most traffic-aware optimal solutions with minimal preprocessing required.<n>Floyd-Warshall-Ingerman was the fastest in real time but provided distance based paths with no traffic awareness.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This project compares three graph search approaches for the task of traffic-aware navigation in Kingston's road network. These approaches include a single-run multi-query preprocessing algorithm (Floyd-Warshall-Ingerman), continuous single-query real-time search (Dijkstra's and A*), and an algorithm combining both approaches to balance between their trade-offs by first finding the top K shortest paths then iterating over them in real time (Yen's). Dijkstra's and A* resulted in the most traffic-aware optimal solutions with minimal preprocessing required. Floyd-Warshall-Ingerman was the fastest in real time but provided distance based paths with no traffic awareness. Yen's algorithm required significant preprocessing but balanced between the other two approaches in terms of runtime speed and optimality. Each approach presents advantages and disadvantages that need to be weighed depending on the circumstances of specific deployment contexts to select the best custom solution. *This project was completed as part of ELEC 844 (Search and Planning Algorithms for Robotics) in the Fall 2025 term.
Related papers
- A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot
Systems [6.853165736531941]
A multi-robot application aims to assign a unique goal to each robot while ensuring collision-free paths.
We propose an efficient conflict-guided method to compute the next best assignment.
We extensively evaluate our algorithm for up to a hundred robots on several benchmark workspaces.
arXiv Detail & Related papers (2024-02-19T19:04:19Z) - Connection-Based Scheduling for Real-Time Intersection Control [6.796017024594715]
We introduce a scheduling algorithm for real-time adaptive traffic signal control to reduce traffic congestion.
This algorithm adopts a lane-based model that estimates the arrival time of all vehicles approaching an intersection through different lanes, and then computes a schedule that minimizes the cumulative delay incurred by all approaching vehicles.
arXiv Detail & Related papers (2022-10-16T04:37:03Z) - A Metaheuristic Algorithm for Large Maximum Weight Independent Set
Problems [58.348679046591265]
Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum.
Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges.
We develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search framework.
arXiv Detail & Related papers (2022-03-28T21:34:16Z) - Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex
Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network.
We design two optimal algorithms that attain these lower bounds.
We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
arXiv Detail & Related papers (2021-06-08T15:54:44Z) - Waypoint Planning Networks [66.72790309889432]
We propose a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm.
We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value networks (VIN)
It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results.
arXiv Detail & Related papers (2021-05-01T18:02:01Z) - Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles [1.370633147306388]
Path finding is a well-studied problem in AI, which is often framed as graph search.
We present two algorithms, grounded in the same idea, that can obtain provably optimal solutions to the considered problem.
arXiv Detail & Related papers (2021-04-14T07:59:53Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - Performance Improvement of Path Planning algorithms with Deep Learning
Encoder Model [1.1995939891389429]
Convolutional Neural Network (CNN) was used to overcome this situation.
This paper analyzes in-depth the performance to eliminate the useless paths using this CNN model.
arXiv Detail & Related papers (2020-08-05T17:34:31Z) - Run2Survive: A Decision-theoretic Approach to Algorithm Selection based
on Survival Analysis [75.64261155172856]
survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime.
We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive.
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
arXiv Detail & Related papers (2020-07-06T15:20:17Z) - Learning to Accelerate Heuristic Searching for Large-Scale Maximum
Weighted b-Matching Problems in Online Advertising [51.97494906131859]
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource.
We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
arXiv Detail & Related papers (2020-05-09T02:48:23Z) - Enhance the performance of navigation: A two-stage machine learning
approach [13.674463804942837]
Real time traffic navigation is an important capability in smart transportation technologies.
In this paper, we adopt the ideas of ensemble learning and develop a two-stage machine learning model to give accurate navigation results.
arXiv Detail & Related papers (2020-04-02T08:55:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.