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Future Transportation
Special Issues
Machine Learning for Sustainable Planning and Modelling in Future Smart...
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Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System

A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4611

Special Issue Editors

Dr. Raj Mani Shukla

E-Mail Website
Guest Editor
Computing and Information Science, Anglia Ruskin University, Cambridge CB11PT, UK
Interests: machine learning; internet of thing; smart transportation; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Lakshmi Babu-Saheer

E-Mail Website
Guest Editor
Computing and Information Science, Anglia Ruskin University, Cambridge CB11PT, UK
Interests: artificial intelligence and machine learning; Internet of Things (IoT)-based systems; AI in biomedical and environmental sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transportation sector plays a pivotal role in the economic development of society. The planning and management of this sector are significant to the government as well as different companies. Urbanization and economic development have led to traffic congestion, increased travel time, fuel consumption, as well as the emission of greenhouse gases. Although the growing use of electric vehicles (EVs) has the potential to mitigate some of these problems, wide-scale charging infrastructure is yet to be developed to enable their use. To meet many of these challenges, smart as well as sustainable transportation infrastructure is required to enable the best use of the transportation sector and enable economic growth. The objective of this special issue is to employ machine learning (ML)- and artificial intelligence (AI)-based knowledge to better plan and model future smart transportation systems. The data generated from the wide-scale sensor network in the smart city can be efficiently used for developing several applications for smart transportation. We invite manuscripts including review papers that critically examine how ML and technological innovations can the sustainable smart transportation planning. These topics include but are not limited to: 

  1. Prediction, control, and management of pollutants; 
  2. Electric vehicle routing;
  3. Charging infrastructure development for modern electric vehicles; 
  4. Management of traffic flow and route optimization; 
  5. Management of the traffic congestion and reducing travel time; 
  6. Vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), and vehicle-to-home (V2H) technologies; 
  7. Integrating renewable energy in the transportation sector; 
  8. Vehicle energy management. 

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Sustainability.

Dr. Raj Mani Shukla
Dr. Lakshmi Babu-Saheer
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. Future Transportation is an international peer-reviewed open access quarterly 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 1000 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

  • smart transportation
  • sustainability
  • artificial intelligence

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Published Papers (3 papers)

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Research

22 pages, 2794 KiB  
Article
Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective
by Faizanul Haque, Farhan Ahmad Kidwai, Ishwor Thapa, Sufyan Ghani and Lincoln M. Mtapure
Future Transp. 2025, 5(1), 11; https://doi.org/10.3390/futuretransp5010011 - 1 Feb 2025
Viewed by 230
Abstract
Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and [...] Read more.
Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions. Full article
(This article belongs to the Special Issue Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System)
22 pages, 5891 KiB  
Article
Optimizing Cold Chain Logistics with Artificial Intelligence of Things (AIoT): A Model for Reducing Operational and Transportation Costs
by Hamed Nozari, Maryam Rahmaty, Parvaneh Zeraati Foukolaei, Hossien Movahed and Mahmonir Bayanati
Future Transp. 2025, 5(1), 1; https://doi.org/10.3390/futuretransp5010001 - 1 Jan 2025
Viewed by 882
Abstract
This paper discusses the modeling and solution of a cold chain logistics (CCL) problem using artificial intelligence of things (AIoT). The presented model aims to reduce the costs of the entire CCL network by maintaining the minimum quality of cold products distributed to [...] Read more.
This paper discusses the modeling and solution of a cold chain logistics (CCL) problem using artificial intelligence of things (AIoT). The presented model aims to reduce the costs of the entire CCL network by maintaining the minimum quality of cold products distributed to customers. This study considers equipping distribution centers and trucks with IoT tools and examines the advantages of using these tools to reduce logistics costs. Also, four algorithms based on artificial intelligence (AI), including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Emperor Penguin Optimizer (EPO), have been used in solving the mathematical model. The analysis results show that equipping trucks and distribution centers with the Internet of Things has increased the total costs by 15% compared to before. This approach resulted in a 26% reduction in operating costs and a 60% reduction in transportation costs. As a result of using the Internet of Things, total costs have been reduced by 2.78%. Furthermore, the performance of AI algorithms showed that the high speed of these algorithms is guaranteed against the high accuracy of the obtained results. So, EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time. Further analyses show the effectiveness of EPO in the indicators of average objective function, average RPD error, and solution time. The results of this paper help managers understand the need to create IoT infrastructure in the distribution of cold products to customers. Because implementing IoT devices can offset a large portion of transportation and energy costs, this paper provides management solutions and insights at the end. As a result, there is a need to deploy IoT tools in other parts of the mathematical model and its application. Full article
(This article belongs to the Special Issue Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System)
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16 pages, 2415 KiB  
Article
Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment
by Ardeshir Mirbakhsh, Joyoung Lee and Dejan Besenski
Future Transp. 2023, 3(2), 552-567; https://doi.org/10.3390/futuretransp3020032 - 1 May 2023
Cited by 5 | Viewed by 2291
Abstract
Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement [...] Read more.
Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures. Full article
(This article belongs to the Special Issue Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System)
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