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Electronics
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Recent Advances of Intelligent Transportation Systems in Smart Cities
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Recent Advances of Intelligent Transportation Systems in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 4172

Special Issue Editors

Dr. Tamás Bécsi

E-Mail Website
Guest Editor
Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Muegyetem rkp. 3, 1111 Budapest, Hungary
Interests: reinforcement learning; intelligent transportation systems; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Szilárd Aradi

E-Mail Website
Guest Editor
Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary
Interests: reinforcement learning; intelligent transportation systems; autonomous vehicles

Special Issue Information

Dear Colleagues,

As the world becomes increasingly urbanized, the need for efficient and sustainable transportation systems in cities has become increasingly pressing. The rapid population growth in urban areas has led to a rise in traffic congestion, air pollution, and carbon emissions, which not only negatively impact the environment but also hinder economic development and the quality of life of citizens. To address these challenges, intelligent transportation systems (ITS) have been proposed as a solution; integrating advanced technologies, such as artificial intelligence, machine learning, and the Internet of Things (IoT), can enhance transportation efficiency, safety, and sustainability. ITS is one of the key elements of smart cities, as it can help to optimize the use of transportation infrastructure, to reduce traffic congestion, and to improve mobility for citizens.

Despite the potential of ITS in smart cities, there are still many technical and non-technical challenges to overcome. For example, the integration of different ITS technologies and systems, the deployment of ITS infrastructure, the development of new business models, and the assurance of data privacy and security are all major challenges that need to be addressed. Moreover, the social and economic impacts of ITS, as well as their effects on urban planning and design, also need to be further studied.

This Special Issue aims to bring together the latest research and developments in ITS and their applications in smart cities. By publishing high-quality original research articles, review articles, and case studies, this Special Issue will provide valuable insights into the current state of the art and future directions of ITS in smart cities.

Scope: This Special Issue will cover a wide range of topics related to ITS in smart cities, including but not limited to the following:

  • Connected and autonomous vehicles;
  • Advanced traffic management and control systems;
  • Intelligent public transportation systems;
  • Smart parking and mobility-as-a-service;
  • Urban logistics and last-mile delivery;
  • Human-centered design and evaluation of ITS;
  • Real-world case studies and implementation challenges.

Dr. Tamás Bécsi
Dr. Szilárd Aradi
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. Electronics 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
  • smart traffic management
  • advanced driver assistance systems
  • urban planning
  • Internet of Things (IoT)
  • data analytics
  • energy efficiency
  • transportation management

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

Published Papers (2 papers)

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Research

16 pages, 4395 KiB  
Article
Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study
by Hongbo Li, Xiao Chang, Pingping Lu and Yilong Ren
Electronics 2023, 12(11), 2344; https://doi.org/10.3390/electronics12112344 - 23 May 2023
Viewed by 1695
Abstract
Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running [...] Read more.
Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants. Full article
(This article belongs to the Special Issue Recent Advances of Intelligent Transportation Systems in Smart Cities)
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14 pages, 418 KiB  
Article
Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent
by Laszlo Szoke, Szilárd Aradi and Tamás Bécsi
Electronics 2023, 12(6), 1442; https://doi.org/10.3390/electronics12061442 - 17 Mar 2023
Cited by 1 | Viewed by 1853
Abstract
In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed [...] Read more.
In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network capacity (throughput). Recent studies embrace machine learning approaches that have the power to aid and optimize the increasing demands. However, most reinforcement learning algorithms fail to be adaptive regarding goal functions. To this end, we provide a novel successor feature-based solution to control a single intersection to optimize the traffic flow, reduce the environmental impact, and promote sustainability. Our method allows for flexibility and adaptability to changing circumstances and goals. It supports changes in preferences during inference, so the behavior of the trained agent (traffic signal controller) can be changed rapidly during the inference time. By introducing the successor features to the domain, we define the basics of successor features, the base reward functions, and the goal preferences of the traffic signal control system. As our main direction, we tackle environmental impact reduction and support prioritized vehicles’ commutes. We include an evaluation of how our method achieves a more effective operation considering the environmental impact and how adaptive it is compared to a general Deep-Q-Network solution. Aside from this, standard rule-based and adaptive signal-controlling technologies are compared to our method to show its advances. Furthermore, we perform an ablation analysis on the adaptivity of the agent and demonstrate a consistent level of performance under similar circumstances. Full article
(This article belongs to the Special Issue Recent Advances of Intelligent Transportation Systems in Smart Cities)
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