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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 25505

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Special Issue Editors

Dr. Filipe Moura

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Guest Editor

Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: transportation systems; sustainable urban mobility; active modes; travel behaviour; transportation environmental impacts; econometric analysis in transportation; discrete choice modeling in transportation; technological diffusion; spatial analysis of transport activity
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Dr. Manuel Marques

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Assistant Guest Editor

Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: computer vision; image processing; assistive robotics

Special Issue Information

Dear Colleagues,

Transportation systems are complex, diverse, and dynamic in nature and operation. Researchers and practitioners have recently been faced with difficulties in obtaining comprehensive and current data needed to tackle rapidly emerging challenges, such as congestion of infrastructures, safety problems, environmental impacts, energy dependency, and social equity. With the rapid digitization and implementation of sensors in transport systems (e.g., personal devices, vehicles, infrastructures—including streets and sidewalks at the urban scale), there is a substantial wealth of data related to transport complex phenomena. Due to their advanced computation and data collection processes, machine learning is a fast and powerful tool that breaks down such complex problems into more straightforward and manageable mathematical operations. Researchers have developed machine learning methods to approach more traditional and novel transportation research problems with varying levels of success.

Machine learning encompasses many methodologies (e.g., supervised learning, unsupervised learning, reinforcement learning, and self-supervised learning, among others) and models (e.g., deep learning, support vector machines, decision trees, and evolutionary algorithms, among others) to explore new data sources and applications. Besides their improved performance compared to more conventional methods, machine learning could evolve to support planning and policy making in the transport field and, therefore, achieve more interpretable models and results (i.e., explainable artificial intelligence).

This Special Issue aims to collect and report new and innovative applications of machine learning methods to solve challenges presented by transportation systems. The scope of the research is diverse; topics of interest include, but are not limited to, the application of machine learning in various transportation fields and the following topics:

- Safety of transport infrastructures, particularly road users and vulnerable road users (pedestrians, cyclists, and scooter users);

- Monitoring, operation control, and management of mobility services, including shared-mobility services, public transportation management, Mobility-as-a-Service (MaaS), etc.;

- Intelligent transportation systems;

- Smart city logistics and micro-logistics;

- Management of public space management at the urban scale, including the intermittent and dynamic usage of road carriageways;

- Case studies in which machine learning was effectively used to make transportation systems more effective;

- Comparison of different approaches of machine learning methods with conventional approaches;

We welcome both original research and review articles. All submissions will be peer-reviewed according to the high standards of the journal.

Dr. Filipe Moura
Dr. Manuel Marques
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

Urban mobility
Infrastructure management
City logistics
Road safety
Big data
Signal processing
Artificial intelligence

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Published Papers (8 papers)

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Research

23 pages, 4912 KiB

Open AccessArticle

Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate

by Grzegorz Mazurek and Paulina Bąk-Patyna

Appl. Sci. 2023, 13(7), 4116; https://doi.org/10.3390/app13074116 - 23 Mar 2023

Cited by 1 | Viewed by 1429

Abstract

The primary purpose of the analysis presented here is to assess the feasibility of effectively predicting the aggregate luminance coefficient. Current road lighting standards and recommendations are based on assessing the level and distribution of luminance on the road surface. The brightness of a road surface depends on the amount of light falling on it, as well as the reflective properties of the road surface, which in turn depend on its physical condition, type and mineralogical composition. The complexity of the factors on which the value of the luminance coefficient depends it makes that data mining techniques the most appropriate tools for evaluation luminance coefficient phenomenon. This article uses five types of techniques: C&RT, boosted trees, random forest, neural network, and support vector machines. After a preliminary analysis, it was determined that the most effective technique was the boosted tree method. The results of the analysis indicated that the actual value of the luminance coefficient has multiple modal values within a single aggregate stockpile, depending on the mineralogical composition and grain size, and cannot be determined by a single central measure. The present model allowed us to determine the value of the luminance coefficient Qd with a mean error of 4.3 mcd-m⁻²·lx⁻¹. In addition, it was found that the best aggregate for pavement brightening that allows high visibility during the day Qd and at night RL is a limestone aggregate. In the group of those that have the ability to potentially brighten the pavement were quartzite and granite aggregates. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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22 pages, 7291 KiB

Open AccessArticle

Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections

by Isaac Oyeyemi Olayode, Lagouge Kwanda Tartibu and Frimpong Justice Alex

Appl. Sci. 2023, 13(2), 744; https://doi.org/10.3390/app13020744 - 5 Jan 2023

Cited by 9 | Viewed by 2338

Abstract

In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road intersections. However, transportation researchers have not focused on prediction of vehicular traffic flow at road intersections using hybrid algorithms such as adaptive neuro-fuzzy inference systems optimized by genetic algorithms. In this research, we propose two models, namely the adaptive neuro-fuzzy inference system (ANFIS) and the adaptive neuro-fuzzy inference system optimized by genetic algorithm (ANFIS-GA), to model and predict vehicles at signalized road intersections using the South African public road transportation system. The traffic data used for this research were obtained via up-to-date traffic data equipment. Eight hundred fifty traffic datasets were used for the ANFIS and ANFIS-GA modelling. The traffic data comprised traffic volume (output), speed of vehicles, and time (inputs). We used 70% of the traffic data for training and 30% for testing. The ANFIS and ANFIS-GA results showed training performance of (R²) 0.9709 and 0.8979 and testing performance of (R²) 0.9790 and 0.9980. The results show that ANFIS-GA is more appropriate for modelling and prediction of traffic flow of vehicles at signalized road intersections. This research adds further to our knowledge of the application of hybrid genetic algorithms in traffic-flow prediction of vehicles at signalized road intersections. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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24 pages, 4058 KiB

Open AccessArticle

Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques

by Ibrahim Aldhari, Meshal Almoshaogeh, Arshad Jamal, Fawaz Alharbi, Majed Alinizzi and Husnain Haider

Appl. Sci. 2023, 13(1), 233; https://doi.org/10.3390/app13010233 - 24 Dec 2022

Cited by 13 | Viewed by 4020

Abstract

Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been identified as a primary focus as part of vision 20230 goals. Driver distraction is the primary cause of increased severity traffic accidents in KSA. In this study, three different machine learning-based severity prediction models were developed and implemented for accident data from the Qassim Province, KSA. Traffic accident data for January 2017 to December 2019 assessment period were obtained from the Ministry of Transport and Logistics Services. Three classifiers, two of which are ensemble machine learning methods, namely random forest, XGBoost, and logistic regression, were used for crash injury severity classification. A resampling technique was used to deal with the problem of bias due to data imbalance issue. SHapley Additive exPlanations (SHAP) analysis interpreted and ranked the factors contributing to crash injury. Two forms of modeling were adopted: multi and binary classification. Among the three models, XGBoost achieved the highest classification accuracy (71%), precision (70%), recall (71%), F1-scores (70%), and area curve (AUC) (0.87) of receiver operating characteristic (ROC) curve when used for multi-category classifications. While adopting the target as a binary classification, XGBoost again outperformed the other classifiers with an accuracy of 94% and an AUC of 0.98. The SHAP results from both global and local interpretations illustrated that the accidents classified under property damage only were primarily categorized by their consequences and the number of vehicles involved. The type of road and lighting conditions were among the other influential factors affecting injury s severity outcome. The death class was classified with respect to temporal parameters, including month and day of the week, as well as road type. Assessing the factors associated with the severe injuries caused by road traffic accidents will assist policymakers in developing safety mitigation strategies in the Qassim Region and other regions of Saudi Arabia. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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18 pages, 4917 KiB

Open AccessArticle

A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm

by Qing Li, Xintai He, Kun Chen and Qicheng Ouyang

Appl. Sci. 2022, 12(21), 10979; https://doi.org/10.3390/app122110979 - 29 Oct 2022

Cited by 3 | Viewed by 1286

Abstract

Labeled data in insufficient amounts and missing categories are two observable features for high maneuvering target trajectory data. However, the existing research achievements are insufficient for solving these two problems simultaneously during data classification. This study proposed a two-stage semi-supervised trajectory data classification algorithm. By pre-training the autoencoder and combining it with the Siamese network, a two-stage joint training was formed, which enabled the model to deal with missing categories by clustering and maintaining the classification ability under the missing label categories. The experimental simulation results showed that the performance of this algorithm was better than the classical semi-supervised algorithm label propagation and transferred learning when the amount of various labeled data was as low as 1–5. The two-stage training model also had a good effect on the problem of missing categories. When 75% of the types were missing, the purity could still reach 82%, which was about eight percentage points higher than the directly trained network. When two problems appeared simultaneously, compared with the directly trained network, the performance improved by about three percentage points on average, and the purity was consistently higher than the clustering results. In summary, this algorithm was more tolerant of the problems of labeled datasets, so it was more practical. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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16 pages, 4543 KiB

Open AccessArticle

Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning

by Nan Wu and Jingjuan Sun

Appl. Sci. 2022, 12(20), 10252; https://doi.org/10.3390/app122010252 - 12 Oct 2022

Cited by 16 | Viewed by 2209

Abstract

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner’s training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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19 pages, 4536 KiB

Open AccessArticle

Comparison of Different Approaches of Machine Learning Methods with Conventional Approaches on Container Throughput Forecasting

by Shuojiang Xu, Shidong Zou, Junpeng Huang, Weixiang Yang and Fangli Zeng

Appl. Sci. 2022, 12(19), 9730; https://doi.org/10.3390/app12199730 - 27 Sep 2022

Cited by 3 | Viewed by 2071

Abstract

Container transportation is an important mode of international trade logistics in the world today, and its changes will seriously affect the development of the international market. For example, the COVID-19 pandemic has added a huge drag to global container logistics. Therefore, the accurate forecasting of container throughput can make a significant contribution to stakeholders who want to develop more accurate operational strategies and reduce costs. However, the current research on port container throughput forecasting mainly focuses on proposing more innovative forecasting methods on a single time series, but lacks the comparison of the performance of different basic models in the same time series and different time series. This study uses nine methods to forecast the historical throughput of the world’s top 20 container ports and compares the results within and between methods. The main findings of this study are as follows. First, GRU is a method that can produce more accurate results (0.54–2.27 MAPE and 7.62–112.48 RMSE) with higher probability (85% for MAPE and 75% for RMSE) when constructing container throughput forecasting models. Secondly, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. Thirdly, the average accuracy of machine learning forecasting methods is higher than that of traditional methods, but the accuracy of individual machine learning forecasting methods may not be higher than that of the best conventional traditional methods. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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29 pages, 5650 KiB

Open AccessArticle

Machine Learning Applications in Surface Transportation Systems: A Literature Review

by Hojat Behrooz and Yeganeh M. Hayeri

Appl. Sci. 2022, 12(18), 9156; https://doi.org/10.3390/app12189156 - 13 Sep 2022

Cited by 18 | Viewed by 6115

Abstract

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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13 pages, 747 KiB

Open AccessArticle

In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning

by Ghaith Al-refai, Hisham Elmoaqet and Mutaz Ryalat

Appl. Sci. 2022, 12(18), 8928; https://doi.org/10.3390/app12188928 - 6 Sep 2022

Cited by 13 | Viewed by 3001

Abstract

Many network protocols such as Controller Area Network (CAN) and Ethernet are used in the automotive industry to allow vehicle modules to communicate efficiently. These networks carry rich data from the different vehicle systems, such as the engine, transmission, brake, etc. This in-vehicle data can be used with machine learning algorithms to predict valuable information about the vehicle and roads. In this work, a low-cost machine learning system that uses in-vehicle data is proposed to solve three categorization problems; road surface conditions, road traffic conditions and driving style. Random forests, decision trees and support vector machine algorithms were evaluated to predict road conditions and driving style from labeled CAN data. These algorithms were used to classify road surface condition as smooth, even or full of holes. They were also used to classify road traffic conditions as low, normal or high, and the driving style was classified as normal or aggressive. Detection results were presented and analyzed. The random forests algorithm showed the highest detection accuracy results with an overall accuracy score between 92% and 95%. Full article

(This article belongs to the Special Issue Machine Learning Applications in Transportation Engineering)

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