Special Issue "Machine Learning for Traffic Modeling and Prediction"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 14 February 2022.

Special Issue Editors

Dr. Paulo Quaresma
E-Mail Website
Guest Editor
Informatics Department, University of Évora, 7000-671 Évora, Portugal
Interests: natural language processing; machine learning; AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vítor Nogueira
E-Mail Website
Guest Editor
Informatics Department, University of Évora, 7002-554 Évora, Portugal
Interests: AI; Semantic Web; Ontologies; NLP
Prof. Dr. José Saias
E-Mail Website
Guest Editor
Informatics Departament, University of Évora, 7002-554 Évora, Portugal
Interests: AI; sentiment analysis; natural language processing; multimodal interaction

Special Issue Information

Dear Colleagues,

Traffic, and in particular traffic accidents, is a matter of high importance. This is due not only to the economic factor but also related to its social, environmental, and even health impact. There is a vast amount of research that applies Artificial Intelligence (AI) approaches, or more specifically, Machine Learning (ML), to deal with traffic-related problems. Recently, AI/ML has been used in modelling and predicting traffic-related subjects such as accidents, speed, flow, and status. There is also work in ML for traffic control, management, and even in traffic-related pollution.

Dr. Paulo Quaresma
Prof. Dr. Vítor Nogueira
Prof. Dr. José Saias
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 papers will be 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. Computers 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 1400 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 modeling
  • traffic prediction
  • traffic accident risk prediction
  • datasets on road accidents
  • artificial intelligence for road safety
  • smart traffic lights
  • traffic-related data acquisition in real-time
  • intelligent mobility and traffic management for smart cities

Published Papers (2 papers)

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Research

Article
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction
Computers 2021, 10(12), 157; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10120157 - 24 Nov 2021
Viewed by 712
Abstract
Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors [...] Read more.
Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots. Full article
(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)
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Article
Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques
Computers 2021, 10(11), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110148 - 09 Nov 2021
Viewed by 689
Abstract
According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. Due to this [...] Read more.
According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes desirable for computer vision researchers. Nowadays, Deep Learning (DL)-based approaches have shown high performance in computer vision tasks that involve a complex features relationship. Therefore, this work develops an automated DL-based method capable of detecting traffic accidents on video. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure. Full article
(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)
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