The MapReduce-based approach to improve vehicle controls on big traffic events

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

doi:10.1109/logistiqua.2017.7962864

Cited by 10 publications

(13 citation statements)

References 13 publications

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“…The proposed approach compares with the existing approach based on the time complexity. It is observed that the proposed approach requires less time compare to the approach of (Cárdenas-Benítez et al, 2016;Gupta et al, 2013;Ganesh and Appavu, 2015;Adoni et al, 2017). The four strategies are useful for efficient traffic management.…”

Section: Resultsmentioning

confidence: 91%

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Emerging Technologies for Driving Road Safety and Traffic Management for Urban Area

Golhar¹,

Kshirsagar²

2021

Journal of Computer Science

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The on-road traffic increases due to the exponential growth of the number of vehicles on the road. That leads to undesirable road conditions and makes urban population life uncomfortable. It includes traffic congestion, traffic jams and long queues at traffic signals and toll booths. Many people violate road traffic rules and regulations rules every day. Manual human effort and supervision are not enough to resolve these issues. Still, many road traffic managements follow the traditional approach, which results increase in road accidents that makes humans seriously injured and even loss of life. To address these issues, we have proposed four strategies as (1) congestion status on square, (2) efficient toll tax collection, (3) fine collection on road for the people who violate road traffic rules, (4) shortest pathfinder for drivers. This research work mainly focuses on the various strategies and their implementation with the help of the Map-Reduce framework. We have combined video surveillance, big data analytics algorithm and proposed an innovative way to manage and handle the road traffic.

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Section: Resultsmentioning

confidence: 91%

“…The limitation of this approach includes (1) not suitable for large scale deployment, (2) misclassification of traffic jam scenarios due to in-accurate vehicle localization. Adoni et al (2017) proposed a traffic event detection using Map Reduce framework. The input traffic event logs are generated using the Apache flume.…”

Section: Literature Surveymentioning

confidence: 99%

Emerging Technologies for Driving Road Safety and Traffic Management for Urban Area

Golhar¹,

Kshirsagar²

2021

Journal of Computer Science

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“…One solution to overcome this challenge is map-reduce which is part of HADOOP Framework. the computations are distributed in the framework into more than one computer to ease the process of analyzing big data [149]. The idea was first developed by [150].…”

Section: Computation Complexity Of Ai Algorithmsmentioning

confidence: 99%

“…Reference [151] shows a challenge in the fast computation for greedy algorithms as they are a sequential and applies the algorithms to a map-reduce parallel computation to increase its efficiency. Similarly [149], the authors present a map-reduce approach to solve and manage the complexity of dense traffic data. The authors proved it as a successful method to adapt when detecting anomalies of traffic events for the big file of data.…”

Section: Computation Complexity Of Ai Algorithmsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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“…This new technology has attracted the attention of business and academic communities (e.g. vehicle controls on big traffic events [3]) because of its ability to meet the 5V (Volume, Velocity, Variety, Veracity and Value) challenges related to shortest path queries in large graphs. Most of the the efficient approaches [4][5][6][7][8][9] dedicated to these routing problems are based on the concept of parallel and distributed computing provided by Hadoop MapReduce [10].…”

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confidence: 99%

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

et al. 2018

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With the increasing size of road networks (4.4 billions of vertices and 6 billions of uploaded GPS points, according to OpenStreetMap data stats 2018 [1]), there has been vast improvement in hardware architecture for intelligent transportation system. The traditional GPS systems embedded in vehicles are only designed to find the shortest paths in small or medium road networks. With the increase in the size of road-networks, implementing efficient GPS programs has become challenging. This is mainly due to impractical computational time taken to compute the optimal path. The basic algorithms used for the Single Source Shortest Path Problem (SSSPP) are not suited for intensive computation in large-scale networks because of long latency time. This is one of the crucial problem of route-guidance systems for highway vehicles including the Vehicle Routing Problem (VPR), Traveling Salesman Problem (TSP) and Pickup and Delivery Problem (PDP). Currently, there are lot of approach of SSSPP such as label setting, dynamic programming, heuristic and/or bidirectional heuristic. However, they are inefficient

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The MapReduce-based approach to improve vehicle controls on big traffic events

Cited by 10 publications

References 13 publications

Emerging Technologies for Driving Road Safety and Traffic Management for Urban Area

Emerging Technologies for Driving Road Safety and Traffic Management for Urban Area

Applications of Artificial Intelligence in Transport: An Overview

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

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