Traffic flow prediction models – A review of deep learning techniques

Kashyap, Anirudh Ameya; Raviraj, Shravan; Devarakonda, Ananya; K, Shamanth R Nayak; Santhosh, K. V.; Bhat, Soumya J

doi:10.1080/23311916.2021.2010510

Cited by 76 publications

(31 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results demonstrate that bagging got better scores when compared to other meta-learners in terms of accuracy and false positives, whereas other metalearners yielded scores equivalent to non-meta algorithms, with no discernible enhancements. Several surveys have been conducted to summarize ML and DL approaches for NTA [76,91,92,93].…”

Section: A Network Traffic Analysismentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

Network forensics focuses on the identification and investigation of internal and external network attacks, the reverse engineering of network protocols, and the uninstrumented investigation of networked devices. It lies at the intersection of digital forensics, incident response and network security. Network attacks exploit software and hardware vulnerabilities and communication protocols. The scope of a network forensic investigation can range from Internet-wide down to a single device's network traffic. Network analysis tools (NATs) aid security professionals and law enforcement in the capturing, identification and analysis of network traffic. However, in most instances, the sheer volume of data to be analyzed is enormous and, despite some built-in NAT automation, the investigation of network traffic is often an arduous process. Furthermore, significant expert time remains wasted in the investigation of a high frequency of false positive alerting from automated systems. To address this globally impacting problem, artificial intelligence based approaches are becoming increasingly employed to automatically detect attacks and increase network traffic classification accuracy. This paper provides a comprehensive survey of the state-of-the-art in network forensics and the application of expert systems, machine learning, deep learning, and ensemble/hybrid approaches to a range of application areas in the field. These include network traffic analysis, intrusion detection systems, Internetof-Things devices, cloud forensics, DNS tunneling, smart grid forensics, and vehicle forensics. In addition, the current challenges and future research directions for each of the aforementioned application areas is discussed.

show abstract

Section: A Network Traffic Analysismentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…It has non-linear mapping and non-parametric characteristics and has great application potential in traffic flow prediction [ 41 ]. Many researchers have applied the ANN or Back Propagation (BP) neural network to predict traffic flow rate or congestion levels [ 27 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Recently, a dynamic feedback neural network called Elman was used in traffic flow prediction and showed improved results [ 19 , 20 , 21 , 22 , 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Li²,

Song³

et al. 2022

Entropy

View full text Add to dashboard Cite

Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.

show abstract

“…The authors examined various forecasting models and their findings showed significant sensitivity of ARIMA models in dealing with missing values. Kashyap et al (2022) reviewed various traffic forecasting models related to deep learning techniques [7]. Their article reviewed some of the latest works in deep learning for the traffic flow prediction.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Forecasting the road accident rate and the impact of the covid 19 on its frequency in the polish provinces

Gorzelańczyk¹,

Jurkovič²,

Kalina³

et al. 2022

Communications

View full text Add to dashboard Cite

The COVID-19 pandemic has significantly affected the development of road transport, not only in Poland, but also worldwide. Limited mobility, especially at the beginning of the pandemic, had a large impact on the number of road accidents. The aim of the present study is to predict the number of road accidents in Poland and to assess the impact of the COVID-19 pandemic on variability of the number of road accidents. To attain the objective, annual data on the road accidents for every province in Poland were collected and analysed. Based on historical crash data, obtained from the police, the number of road accidents was forecasted for both pandemic and non-pandemic scenarios. Selected time series models and exponential models were used to forecast the number of accidents.

show abstract

Traffic flow prediction models – A review of deep learning techniques

Cited by 76 publications

References 61 publications

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Forecasting the road accident rate and the impact of the covid 19 on its frequency in the polish provinces

Contact Info

Product

Resources

About