Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025International Research Journal of Multidisciplinary ScopeOpen Access

Mining Approach for Traffic Congestion Detection

View Full Paper
Ask AI
Bookmark
Share

Authors

SRSekar Kidambi RajuVVVenkataramanVRV Rengarajan

Discussion

Loading...

Member takes

Implication

The unsupervised learning strategy detects traffic congestion in urban areas, indicating improvements in efficiency and safety.

Key Points

  • The proposed model effectively analyzes and anticipates traffic congestion in metropolitan areas, improving efficiency.
  • Using GPS data, traffic flow forecasting in urban road networks shows improved accuracy with new methods.
  • Clustering methods such as DBSCAN and OPTICS successfully identify accident-prone regions, enhancing safety measures.
  • Emerging hot spot analysis indicates diverse patterns, providing valuable insights into traffic management strategies.

Cite This Study

Raju et al. (2025) studied this question.

synapsesocial.com/papers/68c1c63e54b1d3bfb60f2340https://doi.org/10.47857/irjms.2025.v06i03.04170
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems2024 · 2 citations
  2. 2Traffic Congestion Prediction Algorithms in Urban Environments: A Survey2026
  3. 3APPROACH OF AGGLOMERATIVE CLUSTERING ALGORITHM FOR DEEP LEARNING-BASED SPATIAL AND TEMPORAL ROAD TRAFFIC DATA ANALYSIS2025
  4. 4A MACHINE LEARNING APPROACH FOR PREDICTIVE ANALYSIS OF TRAFFIC FLOW2024 · 9 citations
  5. 5Proposal of a Machine Learning Approach for Traffic Flow Prediction2024 · 50 citations