1. Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones
- Author
-
Afandizadeh, Shahriar, Abdolahi, Saeid, and Mirzahossein, Hamid
- Subjects
Data warehousing/data mining ,Algorithm ,Market trend/market analysis ,Natural language interfaces -- Forecasts and trends -- Analysis ,Computational linguistics -- Forecasts and trends -- Analysis ,Language processing -- Forecasts and trends -- Analysis ,Data mining -- Forecasts and trends -- Analysis ,Social networks -- Analysis -- Forecasts and trends ,Algorithms -- Analysis -- Forecasts and trends - Abstract
Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain., Author(s): Shahriar Afandizadeh (corresponding author) [1]; Saeid Abdolahi [1]; Hamid Mirzahossein [2] 1. Introduction Recently, rapid population growth and an increasing number of vehicles, traffic congestion, and overcrowding traffic have [...]
- Published
- 2024
- Full Text
- View/download PDF