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Intelligent Transportation Systems towards Sustainable Transportation

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 12219

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Special Issue Editors

Dr. Keshuang Tang

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Guest Editor

Department of Comprehensive Transportation Information and Control Engineering, Tongji University, Shanghai, China
Interests: data-driven optimization of traffic management and control; human–computer interaction in intelligent transportation systems; artificial intelligence and transportation

Dr. Ashish Bhaskar

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Guest Editor

Civil Engineering, Queensland University of Technology, Brisbane, Australia
Interests: traffic data science; predictive analytics; transport modeling; simulation; traffic control

Dr. Hong Zhu

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Guest Editor

Key Laboratory of Road and Traffic Engineering, Ministry of Education & College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: automated driving control and pedestrian safety, optimization and evaluation for connected traffic

Special Issue Information

Dear Colleagues,

The transportation system is a huge and complex system that is closely involved in everybody’s daily life. Intelligent Transport Systems (ITSs) play an important role in directing the future methods of transportation. The research field of ITSs is broad and ever-changing, the connotations of which are continuously being updated through the integration of emerging information science, computer technologies, and advanced transportation theories. More recently, big data, artificial intelligence (AI), automated vehicles, and connected transportation are promoting the ITS to encompass new research topics. Utilizing emerging technologies and developing transport modes, the ITS should provide a feasible and ideal path for the entire transportation system to become more efficient, safer, and more sustainable. Consequently, transportation researchers are focusing on offering inspiration and solutions for practitioners who are working on the ITS worldwide.

This Special Issue covers different topics that address the most recent advancements in the ITS and the analysis of their effects in application. This Special Issue will shed light on innovative traffic control approaches in the ITS through which traffic performance (safety, efficiency, or sustainability) at critical transportation facilities could be improved. Thus, the topics of interest for this Special Issue include, but are not limited to:

Traffic monitoring, evaluation, and controlling in intersections/junctions, urban motorways, and highways through the use of the ITS;
AI and Big data applications in the ITS;
Connected technologies and autonomous driving;
ITS technologies for analyzing traffic flows;
Low-carbon emissions and energy saving in the ITS.

Dr. Keshuang Tang
Dr. Ashish Bhaskar
Dr. Hong Zhu
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. Sustainability 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

intelligent transportation systems
connected vehicles
big data
artificial intelligence
autonomous driving
traffic signal control

Published Papers (5 papers)

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Research

13 pages, 2904 KiB

Open AccessArticle

Left-Turn Lane Capacity Estimation based on the Vehicle Yielding Maneuver Model to Pedestrians at Signalized Intersections

by Yifei Wang, Xin Zhang and Hideki Nakamura

Sustainability 2024, 16(6), 2313; https://doi.org/10.3390/su16062313 - 11 Mar 2024

Viewed by 462

Abstract

Crossing pedestrians may significantly affect the capacity of the left-turn (LT) lane at signalized intersections while sharing the same signal phase in the left-hand traffic system, the quantitative estimation method is still not intensively discussed when considering the vehicle yielding maneuver. Despite the Road Traffic Act in Japan mandating vehicles to yield to pedestrians, instances of vehicles crossing in front of pedestrians are frequent. This study aims to refine the evaluation of LT lane capacity by introducing a novel vehicle yielding maneuver model, considering factors such as pedestrian numbers, crosswalk length, and signal timing. The model, developed using data from various Japanese crosswalks, is subjected to Monte Carlo simulation for validation. Comparative analysis with existing methods in Japanese and U.S. manuals, along with observed data, highlights the effectiveness of our model. This innovative approach has the potential to mitigate vehicle–pedestrian conflicts and reduce air pollution. By incorporating techniques such as signal optimization and two-stage crossing, our model contributes to sustainability while maintaining efficient traffic flow. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems towards Sustainable Transportation)

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17 pages, 3584 KiB

Open AccessArticle

Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS

by Jaiteg Singh, Noopur Tyagi, Saravjeet Singh, Ahmad Ali AlZubi, Firas Ibrahim AlZubi, Sukhjit Singh Sehra and Farman Ali

Sustainability 2023, 15(22), 15833; https://doi.org/10.3390/su152215833 - 10 Nov 2023

Viewed by 1097

Abstract

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems towards Sustainable Transportation)

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23 pages, 8749 KiB

Open AccessArticle

Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network

by Wenjiao Zai and Dan Yang

Sustainability 2023, 15(18), 13668; https://doi.org/10.3390/su151813668 - 13 Sep 2023

Cited by 1 | Viewed by 1156

Abstract

Reinforcement learning is one of the most widely used methods for traffic signal control, but the method experiences issues with state information explosion, inadequate adaptability to special scenarios, and low security. Therefore, this paper proposes a traffic signal control method based on the efficient channel attention mechanism (ECA-NET), long short-term memory (LSTM), and double Dueling deep Q-network (D3QN), which is EL_D3QN. Firstly, the ECA-NET and LSTM module are included in order to lessen the state space’s design complexity, improve the model’s robustness, and adapt to various emergent scenarios. As a result, the cumulative reward is improved by 27.9%, and the average queue length, average waiting time, and

{C O}_{2}

emissions are decreased by 15.8%, 22.6%, and 4.1%, respectively. Next, the dynamic phase interval

t_{g a p}

is employed to enable the model to handle more traffic conditions. Its cumulative reward is increased by 34.2%, and the average queue length, average waiting time, and

{C O}_{2}

emissions are reduced by 19.8%, 30.1%, and 5.6%. Finally, experiments are carried out using various vehicle circumstances and unique scenarios. In a complex environment, EL_D3QN reduces the average queue length, average waiting time, and

{C O}_{2}

emissions by at least 13.2%, 20.2%, and 3.2% compared to the four existing methods. EL_D3QN also exhibits good generalization and control performance when exposed to the traffic scenarios of unequal stability and equal stability. Furthermore, even when dealing with unique events like a traffic surge, EL_D3QN maintains significant robustness. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems towards Sustainable Transportation)

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17 pages, 3604 KiB

Open AccessArticle

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

by Mahmoud Ragab, Hesham A. Abdushkour, Louai Maghrabi, Dheyaaldin Alsalman, Ayman G. Fayoumi and Abdullah AL-Malaise AL-Ghamdi

Sustainability 2023, 15(16), 12601; https://doi.org/10.3390/su151612601 - 20 Aug 2023

Cited by 3 | Viewed by 1022

Abstract

Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems towards Sustainable Transportation)

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15 pages, 879 KiB

Open AccessArticle

Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations

by Auwal Alhassan Musa, Salim Idris Malami, Fayez Alanazi, Wassef Ounaies, Mohammed Alshammari and Sadi Ibrahim Haruna

Sustainability 2023, 15(13), 9859; https://doi.org/10.3390/su15139859 - 21 Jun 2023

Cited by 6 | Viewed by 7542

Abstract

The emergence of smart cities has addressed many critical challenges associated with conventional urbanization worldwide. However, sustainable traffic management in smart cities has received less attention from researchers due to its complex and heterogeneous nature, which directly affects smart cities’ transportation systems. The study aimed at addressing traffic-related issues in smart cities by focusing on establishing a sustainable framework based on the Internet of Things (IoT) and Intelligent Transportation System (ITS) applications. To sustain the management of traffic in smart cities, which is composed of a hybridized stream of human-driven vehicles (HDV) and connected automated vehicles (CAV), a dual approach was employed by considering traffic as either modeling- and analysis-based, or/and the decision-making issues of previous research works. Moreover, the two techniques utilized real-time traffic data, and collected vehicle and road users’ information using AI sensors and ITS-based devices. These data can be processed and transmitted using machine learning algorithms and cloud computing for traffic management, traffic decision-making policies, and documentation for future use. The proposed framework suggests that deploying such systems in smart cities’ transportation could play a significant role in predicting traffic outcomes, traffic forecasting, traffic decongestion, minimizing road users’ lost hours, suggesting alternative routes, and simplifying urban transportation activities for urban dwellers. Also, the proposed integrated framework adopted can address issues related to pollution in smart cities by promoting public transportation and advocating low-carbon emission zones. By implementing these solutions, smart cities can achieve sustainable traffic management and reduce their carbon footprint, making them livable and environmentally friendly. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems towards Sustainable Transportation)

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