Time Aware Hybrid Hidden Markov Models for Traffic Congestion Prediction

Zaki, John; Ali-Eldin, Amr; Hussein, Sherif E.; Saraya, Sabry F.; Areed, Fayez F.

doi:10.15676/ijeei.2019.11.1.1

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…A Markov chain is a model that identifies probabilities of sequences of state variables, and commonly applied for modelling time-series data. HMM have been used in several studies to recognize traffic pattern in congestion prediction (Zaki et al, 2019(Zaki et al, , 2020Zhao, 2015). Zaki et al (2019) discussed the use of HMM and the Adaptive NeuroFuzzy Inference System (ANFIS) in congestion prediction.…”

Section: B Hidden Markov Model (Hmm)mentioning

confidence: 99%

“…HMM have been used in several studies to recognize traffic pattern in congestion prediction (Zaki et al, 2019(Zaki et al, , 2020Zhao, 2015). Zaki et al (2019) discussed the use of HMM and the Adaptive NeuroFuzzy Inference System (ANFIS) in congestion prediction. Four processing steps known as initialization, recursion, termination, and backtracking are implemented to achieve optimal state transition.…”

Section: B Hidden Markov Model (Hmm)mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

Ahanin¹,

Mustapha²,

Zolkepli³

et al. 2023

IJARBSS

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In today's world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management.

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Section: B Hidden Markov Model (Hmm)mentioning

confidence: 99%

Section: B Hidden Markov Model (Hmm)mentioning

confidence: 99%

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

Ahanin¹,

Mustapha²,

Zolkepli³

et al. 2023

IJARBSS

View full text Add to dashboard Cite

show abstract

“…The reason for applying the sliding window matching is that there is always a slight variation in traffic conditions that may depend on the variation of the last few minutes and also a dependence between the traffic conditions that persist in the current day and those of previous days [33] and [35]. Therefore, the previous days of the same time are checked to find similar traffic conditions.…”

Section: ) Sliding Window Algorithmmentioning

confidence: 99%

Smart System for Emergency Traffic Recommendations : Urban Ambulance Mobility

Charef¹,

Jarir²,

Quafafou³

2022

IJACSA

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With the increasing evolution of advanced technologies and techniques such as the Internet of Things, Artificial Intelligence and Big Data, the traffic management systems industry has acquired new methodologies for creating advanced and intelligent services and applications for traffic management and safety. The current contribution focuses on the implementation of a path recommendation service for paramedics in emergency situations, which is one of the most critical and complex issues in traffic management for the survival of individuals involved in emergency incidents. This work mainly focused on the response time to life-threatening incidents, which is an indicator for emergency ambulance services and for recommending a fastest ambulance route. To this end, we propose a hybrid approach consisting on a local approach using machine learning techniques to predict the congestion of different sections of a map from an origin to a destination, and a global approach to suggest the fastest path to ambulance drivers in real time as they move in OpenStreetMap.

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“…Over the years, the infrastructure for collecting traffic data has improved. Researchers studying transportation may now use DNN predictions for this field, thanks to this advancement and the expansion of computational power [12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning

Abdullah

Periyasamy²,

Kamaludeen

et al. 2023

Sustainability

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Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction.

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Time Aware Hybrid Hidden Markov Models for Traffic Congestion Prediction

Cited by 4 publications

References 26 publications

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

Smart System for Emergency Traffic Recommendations : Urban Ambulance Mobility

Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning

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