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Machine Learning for Traffic Modeling and Prediction

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 47247

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Prof. Dr. Paulo Quaresma

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Algoritmi Research Center, Informatics Department, University of Évora, 7002–554 Évora, Portugal
Interests: artificial intelligence; natural language processing
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Prof. Dr. Vítor Nogueira

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Informatics Department, University of Évora, 7002-554 Évora, Portugal
Interests: AI; Semantic Web; Ontologies; NLP

Dr. José Saias

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1. Department of Informatics, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
2. Vista Lab, ALGORITMI Centre, University of Évora, 7000-671 Évora, Portugal
Interests: artificial intelligence (question-answering, opinion mining, and persuasion detection in digital content); distributed systems; data security
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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
Dr. José Saias
Guest Editors

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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

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Published Papers (9 papers)

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Research

36 pages, 11052 KiB

Open AccessArticle

Real-Time Self-Adaptive Traffic Management System for Optimal Vehicular Navigation in Modern Cities

by Youssef Benmessaoud, Loubna Cherrat and Mostafa Ezziyyani

Computers 2023, 12(4), 80; https://doi.org/10.3390/computers12040080 - 14 Apr 2023

Cited by 3 | Viewed by 3824

Abstract

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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19 pages, 4060 KiB

Open AccessArticle

Optimization of a Fuzzy System Used to Characterize the Factors That Affect Drivers on Urban Roads

by Lilian Astrid Bejarano, Carlos Enrique Montenegro and Helbert Eduardo Espitia

Computers 2023, 12(4), 70; https://doi.org/10.3390/computers12040070 - 30 Mar 2023

Viewed by 1349

Abstract

This document seeks to model the behavior of drivers on urban roads considering different environmental factors using a Mamdani-type fuzzy system. For this, a leader-following traffic model and a fuzzy logic system are used to characterize the behavior of drivers. Real data are obtained using a camera in the roads under consideration, and these data and an optimization process are employed to fit the fuzzy model. For the optimization process, the fuzzy logic system used to model the driver’s behavior is incorporated into a dynamic vehicle tracking model where the fuzzy system allows considering different environmental factors in the traffic model simulation. After carrying out the optimization process, it is possible to assign linguistic labels to the fuzzy sets associated with the output. In this way, the interpretability of the proposed fuzzy system is achieved by assigning labels (concepts) to the fuzzy sets. The results show that the proposed model fits the real data, and the fuzzy sets are adjusted according to the measured data for the different considered cases. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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16 pages, 868 KiB

Open AccessArticle

Macroscopic Spatial Analysis of the Impact of Socioeconomic, Land Use and Mobility Factors on the Frequency of Traffic Accidents in Bogotá

by Alejandro Sandoval-Pineda, Cesar Pedraza and Aquiles E. Darghan

Computers 2022, 11(12), 180; https://doi.org/10.3390/computers11120180 - 8 Dec 2022

Cited by 5 | Viewed by 2039

Abstract

The urban structure of a city, defined by its inhabitants, daily movements, and land use, has become an environmental factor of interest that is related to traffic accidents. Traditionally, macro modeling is usually implemented using spatial econometric methods; however, techniques such as support vector regression have proven to be efficient in identifying the relationships between factors at the zonal level and the frequency associated with these events when considering the autocorrelation between spatial units. As a result of this, the main objective of this study was to evaluate the impact of socioeconomical, land use, and mobility variables on the frequency of traffic accidents through the analysis of area data using spatial and vector support regression models. The spatial weighting matrix term was incorporated into the support vector regression models to compare the results against those that ignore it. The urban land of Bogotá, disaggregated into the territorial units of mobility analysis, was delimited as a study area. Two response variables were used: the traffic accidents index on the road perimeter and the traffic accidents index with deaths on the road perimeter, to analyze the total number of traffic accidents and the deaths caused by them. The results indicated that the rate of trips per person by taxi and motorcycle had the greatest impact on the increase in total accidents and deaths caused by them. Support vector regression models that incorporate the spatial structure allowed the modeling of the spatial dependency between spatial units with a better fit than the spatial regression models. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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11 pages, 1949 KiB

Open AccessArticle

Vehicle Auto-Classification Using Machine Learning Algorithms Based on Seismic Fingerprinting

by Ahmad Bahaa Ahmad, Hakim Saibi, Abdelkader Nasreddine Belkacem and Takeshi Tsuji

Computers 2022, 11(10), 148; https://doi.org/10.3390/computers11100148 - 30 Sep 2022

Cited by 8 | Viewed by 4251

Abstract

Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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16 pages, 2292 KiB

Open AccessArticle

Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems

by Giuseppe Guido, Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Alessandro Vitale and Vittorio Astarita

Computers 2022, 11(10), 145; https://doi.org/10.3390/computers11100145 - 23 Sep 2022

Cited by 19 | Viewed by 1913

Abstract

Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called “feature selection algorithms” are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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18 pages, 3027 KiB

Open AccessArticle

Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data

by Camilo Gutierrez-Osorio, Fabio A. González and Cesar Augusto Pedraza

Computers 2022, 11(9), 126; https://doi.org/10.3390/computers11090126 - 23 Aug 2022

Cited by 24 | Viewed by 3764

Abstract

Traffic accidents are a major concern worldwide, since they have a significant impact on people’s safety, health, and well-being, and thus, they constitute an important field of research on the use of state-of-the-art techniques and algorithms to analyze and predict them. The study of traffic accidents has been conducted using the information published by traffic entities and road police forces, but thanks to the ubiquity and availability of social media platforms, it is possible to have detailed and real-time information about road accidents in a given region, which allows for detailed studies that include unrecorded road accident events. The focus of this paper is to propose a model to predict traffic accidents using information gathered from social media and open data, applying an ensemble Deep Learning Model, composed of Gated Recurrent Units and Convolutional Neural Networks. The results obtained are compared with baseline algorithms and results published by other researchers. The results show promising outcomes, indicating that in the context of the problem, the proposed ensemble Deep Learning model outperforms the baseline algorithms and other Deep Learning models reported by literature. The information provided by the model can be valuable for traffic control agencies to plan road accident prevention activities. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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11 pages, 1706 KiB

Open AccessArticle

Scalable Traffic Signal Controls Using Fog-Cloud Based Multiagent Reinforcement Learning

by Paul (Young Joun) Ha, Sikai Chen, Runjia Du and Samuel Labi

Computers 2022, 11(3), 38; https://doi.org/10.3390/computers11030038 - 8 Mar 2022

Cited by 6 | Viewed by 2888

Abstract

Optimizing traffic signal control (TSC) at intersections continues to pose a challenging problem, particularly for large-scale traffic networks. It has been shown in past research that it is feasible to optimize the operations of individual TSC systems or a small collection of such systems. However, it has been computationally difficult to scale these solution approaches to large networks partly due to the curse of dimensionality that is encountered as the number of intersections increases. Fortunately, recent studies have recognized the potential of exploiting advancements in deep and reinforcement learning to address this problem, and some preliminary successes have been achieved in this regard. However, facilitating such intelligent solution approaches may require large amounts of infrastructure investments such as roadside units (RSUs) and drones, to ensure that connectivity is available across all intersections in the large network. This represents an investment that may be burdensome for the road agency. As such, this study builds on recent work to present a scalable TSC model that may reduce the number of enabling infrastructure that is required. This is achieved using graph attention networks (GATs) to serve as the neural network for deep reinforcement learning. GAT helps to maintain the graph topology of the traffic network while disregarding any irrelevant information. A case study is carried out to demonstrate the effectiveness of the proposed model, and the results show much promise. The overall research outcome suggests that by decomposing large networks using fog nodes, the proposed fog-based graphic RL (FG-RL) model can be easily applied to scale into larger traffic networks. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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15 pages, 802 KiB

Open AccessArticle

Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

by Daniel Santos, José Saias, Paulo Quaresma and Vítor Beires Nogueira

Computers 2021, 10(12), 157; https://doi.org/10.3390/computers10120157 - 24 Nov 2021

Cited by 47 | Viewed by 12700

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 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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17 pages, 2832 KiB

Open AccessArticle

Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques

by Sergio Robles-Serrano, German Sanchez-Torres and John Branch-Bedoya

Computers 2021, 10(11), 148; https://doi.org/10.3390/computers10110148 - 9 Nov 2021

Cited by 28 | Viewed by 12297

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 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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