The MapReduce-based approach to improve the shortest path computation in large-scale road networks: the case of A* algorithm

Adoni, Wilfried Yves Hamilton; Nahhal, Tarik; Aghezzaf, Brahim; Elbyed, Abdeltif

doi:10.1186/s40537-018-0125-8

Cited by 17 publications

(17 citation statements)

References 26 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their proposed technique is very inspiring and achieves significant gain in term of runtime complexity. They used the similarly technique to propose parallel and distributed version of A* pathfinding algorithm called MRA* (MapReduce-A*) [7,8] which consists to combine the A* algorithm with MapReduce paradigm to compute the shortest path on large-scale road network.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

Adoni

Nahhal

Aoun

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we present a scalable and real-time intelligent transportation system based on a big data framework. The proposed system allows for the use of existing data from road sensors to better understand traffic flow, traveler behavior, and increase road network performance. Our transportation system is designed to process large-scale stream data to analyze traffic events such as incidents, crashes and congestion. The experiments performed on the public transportation modes of the city of Casablanca in Morocco reveal that the proposed system achieves a significant gain of time, gathers large-scale data from many road sensors and is not expensive in terms of hardware resource consumption.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Our research was motivated by [6,7,8,9,10,11,12] and made feasible by access to data from Casablanca's buses, tramways, and GIS data of Morocco. The data originates from a variety of sources, including GPS (Global Positioning System) sensors, traffic control devices, and car embedded systems.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

Adoni

Nahhal

Aoun

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the example below, the graph Gr = (V r , Ed ) in Figure 4 (b) is a sub-graph of Gr = (V r, Ed) because V r ⊂ V r and Ed ⊂ Ed. We obtain Gr by removing from Gr the vertex 4 and these adjacent edges (1,4), (3,4) et (4,5). Definition 3 (Partition) Let Gr be a nonempty set and k is a natural integer greater than or equal to 2.…”

Section: Definitions and Notationsmentioning

confidence: 99%

“…Large-scale network such as social networks (e.g., Facebook and Twitter) [11,1], road networks [6,10,2,3,1], brain networks [34], etc... with their heterogeneity allow to analyze a chaotic dynamics or represent a complex phenomenon. They represent numerous exciting challenges related to high performance computing problems, where data scalability, program complexity and robustness hardware configurations play an important roles.…”

Section: Introductionmentioning

confidence: 99%

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

Adoni

Nahhal

Krichen

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Big graphs are part of the movement of "Not Only SQL" databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP

show abstract

“…Heuristic probabilistic search has better global optimization ability. However, the disadvantage of ant colony algorithm is that the computation is large [10]. Mauricio et al [11] adopted a particle swarm optimization method to solve the nonuniform vehicle routing problem with terminal.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning

Wang²

2020

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency.

show abstract

The MapReduce-based approach to improve the shortest path computation in large-scale road networks: the case of A* algorithm

Cited by 17 publications

References 26 publications

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning

Contact Info

Product

Resources

About