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Sustainability
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Advanced Technologies for Energy Saving in Sustainable Transportation Engineering
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Advanced Technologies for Energy Saving in Sustainable Transportation Engineering

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4397

Special Issue Editors

Dr. Wei Liu

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: urban underground engineering; shield tunneling engineering; geotechnical engineering; numerical calculations
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Cheng

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: intelligent transportation systems; logistics and supply chain management; operations research; production scheduling; optimization algorithms

Special Issue Information

Dear Colleagues,

Transportation is not only essential for societal connectivity and economic growth but this sector is also a significant contributor to energy consumption and environmental impact. To address these challenges, it is crucial for researchers and practitioners to actively explore innovative technologies and strategies that can effectively reduce energy usage in transportation systems. This Special Issue offers experts in the field a unique opportunity to share their most recent advancements, novel methodologies, and compelling case studies focused on energy-saving applications in transportation engineering. By fostering a collaborative environment for knowledge exchange and dissemination, this Special Issue aims to accelerate the development and implementation of sustainable practices in transportation and seeks to highlight the importance of integrating energy-saving approaches into transportation planning, design, and operation, ultimately leading to more efficient and environmentally friendly transportation systems.

In this Special Issue, original research articles and reviews are welcome and research areas may include, but are not limited to, the following:

  • Underground transportation infrastructure;
  • Deep excavation;
  • Energy saving in intelligent transportation systems;
  • Sustainable logistics and freight transportation;
  • Transportation system planning, optimization, and humanitarian logistics;
  • Artificial intelligence and machine learning applications in transportation;
  • Environmental methods in construction and maintenance;
  • Energy efficiency improvement in traffic infrastructure.

We look forward to receiving your contributions.

Dr. Wei Liu
Dr. Ming Cheng
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

  • energy saving
  • intelligent transportation systems
  • optimization
  • deep excavation
  • underground spaces
  • smart infrastructure

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

Published Papers (4 papers)

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Research

25 pages, 1240 KiB  
Article
An Intelligent Heuristic Algorithm for a Multi-Objective Optimization Model of Urban Rail Transit Operation Plans
by Weisong Han, Zhihan Shi, Xiaodong Lv and Guangming Zhang
Sustainability 2025, 17(10), 4617; https://doi.org/10.3390/su17104617 - 18 May 2025
Viewed by 105
Abstract
Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and [...] Read more.
Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations. Full article
(This article belongs to the Special Issue Advanced Technologies for Energy Saving in Sustainable Transportation Engineering)
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18 pages, 1759 KiB  
Article
DHDRDS: A Deep Reinforcement Learning-Based Ride-Hailing Dispatch System for Integrated Passenger–Parcel Transport
by Huanwen Ge, Xiangwang Hu and Ming Cheng
Sustainability 2025, 17(9), 4012; https://doi.org/10.3390/su17094012 - 29 Apr 2025
Viewed by 269
Abstract
Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting [...] Read more.
Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article
(This article belongs to the Special Issue Advanced Technologies for Energy Saving in Sustainable Transportation Engineering)
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22 pages, 1199 KiB  
Article
Charging Scheduling of Electric Vehicles Considering Uncertain Arrival Times and Time-of-Use Price
by Zhaojie Wang, Feifeng Zheng and Ming Liu
Sustainability 2025, 17(3), 1100; https://doi.org/10.3390/su17031100 - 29 Jan 2025
Viewed by 1125
Abstract
To advance sustainable transportation solutions, this work investigates an electric vehicle charging scheduling problem under the uncertainty of vehicle arrival times. Given a set of appointed electric vehicles, the objective of the considered problem is to explore charging strategies that minimize the total [...] Read more.
To advance sustainable transportation solutions, this work investigates an electric vehicle charging scheduling problem under the uncertainty of vehicle arrival times. Given a set of appointed electric vehicles, the objective of the considered problem is to explore charging strategies that minimize the total charging cost for the charging station. To address this problem, this work first establishes a mixed-integer programming model. Then, an enhanced sample average approximation approach alongside two versions of distribution-free approaches are applied to solve the studied problem. Additionally, this study introduces a BP neural network-enhanced distribution-free approach to efficiently resolve the problem. Finally, numerical experiments are conducted to demonstrate the effectiveness of the proposed approaches. Full article
(This article belongs to the Special Issue Advanced Technologies for Energy Saving in Sustainable Transportation Engineering)
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33 pages, 12755 KiB  
Article
Optimizing Sustainability Offshore Hybrid Tidal-Wind Energy Storage Systems for an Off-Grid Coastal City in South Africa
by Ladislas Mutunda Kangaji, Atanda Raji and Efe Orumwense
Sustainability 2024, 16(21), 9139; https://doi.org/10.3390/su16219139 - 22 Oct 2024
Cited by 2 | Viewed by 2285
Abstract
South Africa’s extensive marine energy resources present a unique opportunity for advancing sustainable energy solutions. This study focuses on developing a sustainable hybrid power generation system that combines offshore wind and tidal current energy to provide a stable, renewable energy supply for off-grid [...] Read more.
South Africa’s extensive marine energy resources present a unique opportunity for advancing sustainable energy solutions. This study focuses on developing a sustainable hybrid power generation system that combines offshore wind and tidal current energy to provide a stable, renewable energy supply for off-grid coastal communities. By addressing the challenges of intermittency and unpredictability in renewable energy sources, the proposed system integrates wind and tidal energy with energy storage and diesel backup to ensure reliability while reducing greenhouse gas emissions and minimizing the environmental footprint. The system is optimized for sustainability, with a configuration of one wind turbine, five tidal turbines, and a diesel generator demonstrated to be the most effective in increasing the renewable energy fraction and lowering the net present cost. Simulations conducted using HOMER Pro version 3.20 software underscore the potential of this hybrid system to support South Africa’s transition to a more sustainable energy future, aligning with national and global sustainability goals. The results emphasize the environmental benefits of combining these renewable energy sources, offering a blueprint for achieving energy security and sustainable development in coastal regions. Full article
(This article belongs to the Special Issue Advanced Technologies for Energy Saving in Sustainable Transportation Engineering)
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