An intelligent traffic congestion detection approach based on fuzzy inference system

Amini, Mehran; Hatwagner, Miklos F.; Mikulai, Gergely Cs.; Kóczy, László T.

doi:10.1109/saci51354.2021.9465637

Cited by 15 publications

(12 citation statements)

References 34 publications

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“…In [13], Amini et al (2021) proposed a robust and accurate traffic congestion detection system utilizing a Hierarchical Fuzzy Rule-Based System (HFRBS) optimized by Genetic Algorithms (GA), effectively reducing input data size without losing information but with increased method complexity.…”

Section: Chen Et Al (2021) Inmentioning

confidence: 99%

See 1 more Smart Citation

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

S Jasim,

Zaghden,

Salim Bouhlel

2024

IJCDS

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Traffic congestion remains a pressing challenge in urban areas, causing significant economic and environmental repercussions. To address this issue, accurate detection and prediction of traffic congestion are imperative for effective traffic management and planning. This research study investigates the efficacy of Support Vector Machines (SVM) and various other machine learning algorithms in augmenting traffic congestion detection and prediction for Vehicular Ad hoc Networks (VANETs). Leveraging historical congestion patterns, we train and evaluate the performance of the algorithms. Our results demonstrate the potential of SVM, coupled with advanced feature engineering techniques, to outperform other methods in accurately identifying and forecasting traffic congestion. The SVM classifier achieved an impressive classification accuracy of 0.99, showcasing its effectiveness in handling diverse traffic scenarios. Additionally, the K-Nearest Neighbors (KNN) and Ensemble Learning classifiers also yielded commendable accuracies of 0.99. Notably, the Decision Tree (DT) classifier attained a perfect accuracy score of 1.00, indicating its robustness in handling congestion patterns. The proposed approach not only achieves high detection accuracy but also exhibits remarkable robustness and scalability, enabling its application across various traffic scenarios. These findings contribute significantly to the development of intelligent traffic management systems, providing valuable insights into optimizing transportation networks. Ultimately, implementing our approach holds the potential to alleviate congestion, enhance travel efficiency, and foster urban sustainability.

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Section: Chen Et Al (2021) Inmentioning

confidence: 99%

“…Stevens and Yeh (2016) in [14] developed a methodology based on reinforcement learning for optimizing traffic flow using Q-learning and Markov decision processes to assign speed limits. While it performed well in small road networks, it had limitations with datasets containing numerous attributes and features.…”

Section: Chen Et Al (2021) Inmentioning

confidence: 99%

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

S Jasim,

Zaghden,

Salim Bouhlel

2024

IJCDS

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“…In this paper, the proposed model is applied to classify traffic conditions of the motorway between Padua and Venice in Italy using flow, density, and speed fundamental variables on the Mamdani-based fuzzy logic application. In the literature, this approach was applied with only density and flow traffic flow characteristics [8,9] and the level of congestion was related to the variable density. Differently from those studies, we believe that including speed values can improve the effectiveness of the method because it can better characterize unstable conditions when fundamental variables are affected by rapid changes that violate the relationships that hold in stationary conditions.…”

Section: Case Studymentioning

confidence: 99%

Application of a Mamdani-Based Fuzzy Traffic State Identifier to a Real Case Study

Erdinç,

Colombaroni,

Fusco

2023

2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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This paper presents a fuzzy-logic application based on the Mamdani inference method to get the range of road traffic conditions. It was tested with real data extracted from the Padua-Venice motorway in Italy, which contains a dense network of monitoring that provide continuous measurements of flow, occupancy, and speed. The empirical results show that the proposed study functions well in qualitative classification. The experiment can provide another perspective on motorway traffic control.

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“…The most commonly used approach is based on fixed road sensors which are used to compute traffic parameters for the segments of the motorway that they are covering. Those data are passed to the rule-based algorithm which determines the motorway segments with congestion [14,15]. The cellular-probe-based motorway congestion detection approach relies on on-call wireless location technologies with signal transition data.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Dynamic Zone Positions for Mixed Traffic Flow Variable Speed Limit Control with Congestion Detection

Vrbanić,

Gregurić,

Miletić

et al. 2023

Machines

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Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10% to 100% and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario.

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An intelligent traffic congestion detection approach based on fuzzy inference system

Cited by 15 publications

References 34 publications

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

Application of a Mamdani-Based Fuzzy Traffic State Identifier to a Real Case Study

Reinforcement Learning-Based Dynamic Zone Positions for Mixed Traffic Flow Variable Speed Limit Control with Congestion Detection

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