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Algorithms
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Neural Network for Traffic Forecasting
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Neural Network for Traffic Forecasting

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 18060

Special Issue Editors

Dr. Weiwei Jiang

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: 5G; internet of things; intelligent transportation system; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haiyong Luo

E-Mail Website
Guest Editor
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Interests: location-based services; pervasive computing; mobile computing; Internet of Things

Special Issue Information

Dear Colleagues,

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks (GNNs) have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems, e.g., road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms.

This has been an active field of research dealing with the application of neural networks for traffic forecasting problems, which are at the heart of this Special Issue. 

We invite you to submit high-quality papers to the Special Issue on “Neural Network for Traffic Forecasting”, with subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interests:

  • Novel neural networks, e.g., graph convolutional and graph attention networks, spatiotemporal graph neural networks, traffic transformers, temporal convolutional networks, and recurrent neural networks;
  • Neural networks for traffic forecasting problems, e.g., road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms;
  • Open data and source resources for traffic forecasting problems.

Dr. Weiwei Jiang
Dr. Haiyong Luo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • traffic forecasting
  • convolutional neural network
  • recurrent neural network
  • graph neural network

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Published Papers (6 papers)

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Editorial

Jump to: Research

2 pages, 154 KiB  
Editorial
Special Issue “Neural Network for Traffic Forecasting”
by Weiwei Jiang
Algorithms 2023, 16(9), 421; https://doi.org/10.3390/a16090421 - 2 Sep 2023
Viewed by 1010
Abstract
Traffic forecasting is an important research topic in intelligent transportation systems and smart cities [...] Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)

Research

Jump to: Editorial

12 pages, 2004 KiB  
Article
Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
by Yiming Zhao and Jing Dong-O’Brien
Algorithms 2023, 16(6), 298; https://doi.org/10.3390/a16060298 - 15 Jun 2023
Cited by 1 | Viewed by 1650
Abstract
Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic [...] Read more.
Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic breakdown. Therefore, ramp meters have been used to regulate the traffic flow from the ramps to maintain stable traffic flow on the mainline. However, existing traffic breakdown prediction models do not consider on-ramp traffic flow. In this paper, an algorithm based on artificial neural networks (ANN) is developed to predict the probability of a traffic breakdown occurrence on freeway segments with merging traffic, considering temporal and spatial correlations of the traffic conditions from the location of interest, the ramp, and the upstream and downstream segments. The feature selection analysis reveals that the traffic condition of the ramps has a significant impact on the occurrence of traffic breakdown on the mainline. Therefore, the traffic flow characteristics of the on-ramp, along with other significant features, are used to build the ANN model. The proposed ANN algorithm can predict the occurrence of traffic breakdowns on freeway segments with merging traffic with an accuracy of 96%. Furthermore, the model has been deployed at a different location, which yields a predictive accuracy of 97%. In traffic operations, the high probability of the occurrence of a traffic breakdown can be used as a trigger for the ramp meters. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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21 pages, 7218 KiB  
Article
Predicting Road Traffic Accidents—Artificial Neural Network Approach
by Dragan Gatarić, Nenad Ruškić, Branko Aleksić, Tihomir Đurić, Lato Pezo, Biljana Lončar and Milada Pezo
Algorithms 2023, 16(5), 257; https://doi.org/10.3390/a16050257 - 17 May 2023
Cited by 5 | Viewed by 3119
Abstract
Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and [...] Read more.
Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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20 pages, 5620 KiB  
Article
Physics-Informed Neural Networks (PINNs)-Based Traffic State Estimation: An Application to Traffic Network
by Muhammad Usama, Rui Ma, Jason Hart and Mikaela Wojcik
Algorithms 2022, 15(12), 447; https://doi.org/10.3390/a15120447 - 27 Nov 2022
Cited by 11 | Viewed by 4610
Abstract
Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. The physics information-based neural network (PINN) framework emerges to [...] Read more.
Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. The physics information-based neural network (PINN) framework emerges to mitigate the limitations of the traditional TSE methods, while the state-of-art of such a framework has focused on single road segments but can hardly deal with traffic networks. This paper introduces a PINN framework that can effectively make use of a small amount of observational speed data to obtain high-quality TSEs for a traffic network. Both model-driven and data-driven components are incorporated into PINNs to combine the advantages of both approaches and to overcome their disadvantages. Simulation data of simple traffic networks are used for studying the highway network TSE. This paper demonstrates how to solve the popular LWR physical traffic flow model with a PINN for a traffic network. Experimental results confirm that the proposed approach is promising for estimating network traffic accurately. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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22 pages, 2227 KiB  
Article
Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem
by Stefano Fiorini, Michele Ciavotta and Andrea Maurino
Algorithms 2022, 15(10), 376; https://doi.org/10.3390/a15100376 - 14 Oct 2022
Cited by 7 | Viewed by 2200
Abstract
In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number [...] Read more.
In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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22 pages, 2300 KiB  
Article
Autonomous Intersection Management by Using Reinforcement Learning
by P. Karthikeyan, Wei-Lun Chen and Pao-Ann Hsiung
Algorithms 2022, 15(9), 326; https://doi.org/10.3390/a15090326 - 13 Sep 2022
Cited by 5 | Viewed by 2815
Abstract
Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement [...] Read more.
Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake-safe control module is utilized to make sure that each vehicle travels safely, and we train the system to acquire an effective model by using reinforcement learning. We have simulated our proposed method by using a simulation of urban mobility tools. Experimental results show that our approach outperforms the traditional method. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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