Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Intelligent Transportation System Technologies and Applications
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Transportation System Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 11146

Special Issue Editors

Dr. Mahjoub Dridi

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, 90010 Belfort, France
Interests: autonomous intersections; transportation systems; traffic control; urban mobility; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yazan Mualla

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University of Technology of Belfort-Montbéliard (UTBM), 90010 Belfort, France
Interests: explainable artificial intelligence (XAI); human computer interaction (HCI); multiagent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdeljalil Abbas-Turki

E-Mail Website
Guest Editor
Laboratoire Connaissances et Intelligence Artificielle Distribuées;Université de Technologie de Belfort-Montbéliard, Belfort, France
Interests: connected autonomous vehicles; cooperative driving; artificial intelligence; control theory; urban mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic congestion is among the largest sources of pollution and noise, not to mention an enormous waste of time and energy. Vehicle traffic rationalization and optimization have become mandatory to at least minimize the impact of pollutant emissions and unsustainable fuel consumption in cities and urban areas. Intelligent transportation systems (ITSs) constitute a fertile research area to manage urban traffic in smart cities, and also to improve transportation efficiency, environmental care and safety. As science harnesses the technological progress in the ITS domain, paradigm shifts are anticipated.

This Special Issue aims to study the various advanced technologies and applications of intelligent transport systems and highlight their contributions in terms of reducing traffic congestion in cities, improving the safety of vulnerable road users, reducing pollution, increasing the attractiveness of cities and thus supporting the economy of cities. Topics of interest include (but are not limited to) the following:

  • Traffic signal management;
  • Autonomous intersection management;
  • Explainable AI and intelligent transportation;
  • Navigation in smart cities;
  • Cloud services for smart mobility;
  • Control and management of electric and hybrid vehicles;
  • Multi-agent systems;
  • Combinatorial optimization;
  • Meta-heuristics;
  • Reinforcement learning;
  • Deep learning;
  • Petri nets modelling and control;
  • Connected vehicles;
  • Cooperative driving;
  • Computer vision and smart transportation systems.

Dr. Mahjoub Dridi
Dr. Yazan Mualla
Prof. Dr. Abdeljalil Abbas-Turki
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

  • cooperative driving
  • traffic control
  • urban mobility
  • explainability
  • smart mobility

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

26 pages, 3235 KiB  
Article
Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
by Teerapun Meepokgit and Sumek Wisayataksin
Appl. Sci. 2024, 14(17), 7908; https://doi.org/10.3390/app14177908 - 5 Sep 2024
Viewed by 809
Abstract
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning [...] Read more.
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

18 pages, 66715 KiB  
Article
Vehicle Ego-Trajectory Segmentation Using Guidance Cues
by Andrei Mihalea and Adina Magda Florea
Appl. Sci. 2024, 14(17), 7776; https://doi.org/10.3390/app14177776 - 3 Sep 2024
Viewed by 523
Abstract
Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with [...] Read more.
Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems, as it enhances the accuracy of perceiving and forecasting the vehicle’s movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle’s path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network’s decisions through image and guidance input concatenation, feature concatenation, or cross-attention between pixel features and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs and prove that our proposed improvements bring major boosts in the segmentation metrics, increasing IoU by more than 12% and 5% compared with our two baseline models. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

28 pages, 1451 KiB  
Article
Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
by Horațiu Florian, Camelia Avram, Dan Radu and Adina Aștilean
Appl. Sci. 2024, 14(15), 6743; https://doi.org/10.3390/app14156743 - 2 Aug 2024
Viewed by 739
Abstract
Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users’ demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, [...] Read more.
Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users’ demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

24 pages, 2453 KiB  
Article
Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style
by Yahya Kadhim Jawad and Mircea Nitulescu
Appl. Sci. 2024, 14(9), 3905; https://doi.org/10.3390/app14093905 - 3 May 2024
Cited by 1 | Viewed by 1069
Abstract
This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions [...] Read more.
This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

26 pages, 1517 KiB  
Article
Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
by Gamil Ahmed, Tarek Sheltami, Mustafa Ghaleb, Mosab Hamdan, Ashraf Mahmoud and Ansar Yasar
Appl. Sci. 2024, 14(6), 2418; https://doi.org/10.3390/app14062418 - 13 Mar 2024
Cited by 2 | Viewed by 1395
Abstract
The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. [...] Read more.
The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD’s path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO’s superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

21 pages, 3544 KiB  
Article
Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control
by Claudio Meneguzzer
Appl. Sci. 2024, 14(5), 1891; https://doi.org/10.3390/app14051891 - 25 Feb 2024
Viewed by 1061
Abstract
Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger [...] Read more.
Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

12 pages, 1919 KiB  
Article
Controlling Traffic Congestion in a Residential Area via GLOSA Development
by Yahya Kadhim Jawad and Mircea Nitulescu
Appl. Sci. 2024, 14(4), 1474; https://doi.org/10.3390/app14041474 - 11 Feb 2024
Cited by 1 | Viewed by 1165
Abstract
The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society’s constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what [...] Read more.
The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society’s constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities’ traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

14 pages, 3453 KiB  
Article
Systemic Design Strategies for Shaping the Future of Automated Shuttle Buses
by Ming Yan, Peng Lu, Venanzio Arquilla, Fausto Brevi, Lucia Rampino and Giandomenico Caruso
Appl. Sci. 2023, 13(21), 11767; https://doi.org/10.3390/app132111767 - 27 Oct 2023
Viewed by 1110
Abstract
Automated shuttle buses entail adopting new technologies and modifying users’ practices, cultural and symbolic meanings, policies, and markets. This results in a paradigmatic transition for a typical sociotechnical system: the transport system. However, the focus of the extant literature often lacks an overall [...] Read more.
Automated shuttle buses entail adopting new technologies and modifying users’ practices, cultural and symbolic meanings, policies, and markets. This results in a paradigmatic transition for a typical sociotechnical system: the transport system. However, the focus of the extant literature often lacks an overall vision, addressing a single technology, supply chain, or societal dimension. Although systemic design can manage multiple-level and long-term transitions, the literature does not discuss how systemic design tools can support implementation. This paper takes the four strategies proposed by Pereno and Barbero in 2020 as the theoretical framework to fill this literature gap, discussing the specific systemic design methods applicable to the design of automated shuttle bus systems. A six-week workshop to facilitate the exploration of future autonomous public transportation is taken as a case study. The systemic design approach was applied to enrich the Human–Machine Interaction (HMI) and functional architecture of automated shuttle buses. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1264 KiB  
Review
Review of Traffic Assignment and Future Challenges
by Manal Elimadi, Abdeljalil Abbas-Turki, Abder Koukam, Mahjoub Dridi and Yazan Mualla
Appl. Sci. 2024, 14(2), 683; https://doi.org/10.3390/app14020683 - 13 Jan 2024
Cited by 1 | Viewed by 2028
Abstract
The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress [...] Read more.
The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-9

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A comprehensive review on traffic assignment and future challenges
Authors: Mahjoub Dridi
Affiliation: Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, 90010 Belfort, France

Title: Vehicle ego-trajectory segmentation using guidance cues for autonomous driving
Authors: Andrei Mihalea; Adina Magda Florea
Affiliation: Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
Abstract: Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems as it enhances the accuracy of perceiving and forecasting the vehicle's movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle's path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network's decisions through image and guidance input concatenation, feature concatenation or cross-attention between pixel features, and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles.

Back to TopTop