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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: 10 October 2025 | Viewed by 9631

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Special Issue Editors

Dr. Jie Ma

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Guest Editor

School of Transportation, Southeast University, Nanjing 211189, China
Interests: public, shared, and stereoscopic mobility; multi-modal transportation modeling; electrification and green transportation and logistics
Special Issues, Collections and Topics in MDPI journals

Dr. Jingxu Chen

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Guest Editor

School of Transportation, Southeast University, Nanjing 211189, China
Interests: urban transport planning; sustainable transportation; green logistics
Special Issues, Collections and Topics in MDPI journals

Dr. Xinlian Yu

E-Mail Website
Guest Editor

School of Transportation, Southeast University, Nanjing 211189, China
Interests: transportation network modeling; emerging technologies; planning and operations of transit systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The composition and efficiency of urban public transport systems are intricately linked to numerous sustainable development issues. A high utilization rate of urban public transport systems plays a pivotal role in mitigating travel pollution, alleviating traffic congestion, enhancing transportation network efficiency and land utilization, and ensuring a balanced distribution of benefits among stakeholders in the public transport service industry. Moreover, with the advancements in electric vehicle technology, electric buses have gained increasing prominence within public transportation systems worldwide. As the proportion of electric buses in public transportation continues to rise, the challenges surrounding dispatching and charging in the transportation system have become increasingly apparent. Consequently, it becomes imperative to optimize the existing bus-dispatching strategies in light of the unique characteristics of electric buses, thereby elevating the overall efficiency of the public transport system.

The primary objective of the "Sustainable Transportation and Logistics Optimization" Special Issue is to comprehensively explore the current state and future prospects of sustainable public transportation and logistics optimization from diverse perspectives. This includes investigating how the scheduling strategies of public transportation systems, including electric buses, the matching of ridesharing passengers, and vehicles, can confer competitive advantages to sustainable public transport. The articles featured in this Special Issue will encompass a wide range of methodologies, such as qualitative and quantitative analysis, mathematical modeling, and numerical experiments, among others. We welcome research contributions addressing the following topics:

Optimization methods for buses, ridesharing vehicles, and dispatching strategies;
Site selection for electric-bus charging facilities;
Energy management and optimization techniques for electric vehicles;
Environmental factors associated with electric vehicles;
Safety considerations for electric vehicle travel;
The application of big data in bus planning and scheduling;
The modeling and analysis of urban public transport and shared mobility networks;
The sustainable management of transportation and logistics;
Strategies for coordinating electric vehicles with other vehicles within the public transport system;
Behavioral analyses of electric-vehicle users;
Sustainable transportation and logistics network optimization methodologies.

We encourage researchers to delve into these areas, employing rigorous approaches such as qualitative and quantitative analyses, mathematical modeling, and numerical experiments to contribute to the advancement of sustainable public transportation and logistics optimization.

Dr. Jie Ma
Dr. Jingxu Chen
Dr. Xinlian Yu
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

shared mobility
public transportation
sustainable logistics
electrification transportation and logistics

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Published Papers (8 papers)

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Research

20 pages, 413 KiB

Open AccessArticle

The Green Finance Pilot Policy Suppresses Green Innovation Efficiency: Evidence from Chinese Cities

by Yanqiu Zhu, Ming Zhang, Hongan Chen and Jun Ma

Sustainability 2025, 17(13), 6136; https://doi.org/10.3390/su17136136 - 4 Jul 2025

Viewed by 345

Abstract

Green finance is widely promoted as a tool for supporting low-carbon development, but its effects on innovation efficiency remain unclear. This study examines the impact of China’s Green Finance Reform and Innovation Pilot Zones (GFRIPZ) on green innovation efficiency at the city level. Using the GFRIPZ policy as a quasi-natural experiment, we employ a difference-in-differences approach to identify the causal effects of the policy and explore the underlying mechanisms and contextual moderators. The results indicate that the policy significantly reduces green innovation efficiency in pilot cities, with the negative impact being more pronounced in non-central cities, provincial capitals, and cities in western China. Mechanism analysis reveals two key pathways: increased environmental costs contribute to resource lock-in, and strategic shifts toward quantity-focused innovation reduce overall efficiency. Furthermore, we find that the institutional environment plays a critical role—market integration mitigates the policy’s adverse effects by improving resource allocation, while administrative environmental pressure intensifies distortions. These findings suggest that rigid green finance regulations may unintentionally suppress innovation performance. We propose that more flexible policy design, better cross-regional coordination, and refined local governance incentives are essential for aligning green finance tools with innovation-driven sustainability goals in emerging economies. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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18 pages, 5413 KiB

Open AccessArticle

Dynamic Estimation of Travel Time Reliability for Road Network Using Trajectory Data

by Jiayu Hang, Tianpei Tang and Jiawen Wang

Sustainability 2025, 17(9), 4244; https://doi.org/10.3390/su17094244 - 7 May 2025

Viewed by 708

Abstract

To evaluate the operation of an urban transportation system by accurately analyzing the reliability of a road network, with the aim of reducing the substantial fluctuation of travel time, a method for dynamically estimating the reliability of road network travel time is proposed. First, the definition of travel time reliability is given by referring to system reliability theory: the possibility that all travelers in the road network reach their destination within a predetermined time. The travel time reliability is numerically expressed as the probability that the ratio of delay to travel time (RODT) is less than a certain value. Then, actual data are used to prove that the RODT of vehicles in the road network obeys the normal distribution, based on which a data-driven method of travel time reliability estimation is proposed. The travel time reliability of a real-world network is estimated based on the trajectory. Finally, the variation in travel time reliability under different road network capacities is studied, and the accuracy of the estimated travel time reliability under different trajectory data penetration rates is analyzed. The dynamic estimation method of travel time reliability proposed in this paper supports better understanding of the operation efficiency of urban road traffic systems, to help better evaluate the performance of road network systems and provide a basis for road network reliability optimization. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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14 pages, 2040 KiB

Open AccessArticle

A New Breakthrough in Travel Behavior Modeling Using Deep Learning: A High-Accuracy Prediction Method Based on a CNN

by Xuli Wen and Xin Chen

Sustainability 2025, 17(2), 738; https://doi.org/10.3390/su17020738 - 18 Jan 2025

Cited by 3 | Viewed by 1524

Abstract

Accurately predicting travel mode choice is crucial for effective transportation planning and policymaking. While traditional approaches rely on discrete choice models, recent advancements in machine learning offer promising alternatives. This study proposes a novel convolutional neural network (CNN) architecture optimized using orthogonal experimental design to predict travel mode choice. Using the SwissMetro dataset, which represents a specific intercity travel context in Switzerland, we evaluate our CNN model’s performance and compare it with traditional machine learning algorithms and previous studies. The key innovations of our study include: (1) an optimized CNN architecture designed to capture complex patterns in travel behavior data, and (2) the application of orthogonal experimental design to efficiently identify optimal hyperparameter settings. The results demonstrate that the proposed CNN model significantly outperforms logit models, support vector machines, random forests, gradient boosting, and even state-of-the-art techniques combining discrete choice models with neural networks. The optimized CNN achieves a remarkable 95% accuracy, surpassing the best-performing benchmarks by 14–25%. The proposed methodology offers a powerful tool for understanding travel behavior, improving travel demand forecasting, and informing transportation planning decisions. Our findings contribute to the growing body of literature on machine learning applications in transportation and pave the way for further advancements in this field. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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21 pages, 4658 KiB

Open AccessArticle

Fuel Replenishment Problem of Heterogeneous Fleet in Initiative Distribution Mode

by Jin Li, Hongying Song and Huasheng Liu

Sustainability 2025, 17(2), 685; https://doi.org/10.3390/su17020685 - 16 Jan 2025

Viewed by 1006

Abstract

Petrol, a vital energy source for residents’ consumption and economically sustainable operation, generates substantial distribution demand. To reduce distribution costs, we propose a fuel replenishment problem using a heterogeneous fleet based on the initiative distribution mode. In this mode, the distribution center determines both the delivery orders of customers and the distribution plan. We develop a mathematical model with minimal operational costs, including transport, employment, and penalty costs. A Two-stage heuristic algorithm K-IBKA based on time-space clustering is proposed, which also combines the advantages of the butterfly optimization algorithm in quick convergence and hierarchical mutation strategy in population diversity. The results demonstrate that: (1) Heterogeneous truck distribution exhibits better cost advantages compared to homogeneous distribution, reducing total costs by 13.07%; (2) Compared to passive distribution mode, the total cost of the initiative distribution mode is reduced by 11.03% and 41.80%, respectively, through small and large-scale instances. (3) Compared with the unimproved BKA, ALNS, and GA, the total cost calculated by K-IBKA is reduced by 37.68%, 35.30%, and 27.26%, respectively, thus demonstrating the contribution of this work to reducing the cost of petrol distribution and achieving sustainable development of distribution. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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21 pages, 2510 KiB

Open AccessArticle

Should Charging Stations Provide Service for Plug-In Hybrid Electric Vehicles During Holidays?

by Tianhua Zhang, Xin Li, Yiwen Zhang and Chenhui Shu

Sustainability 2025, 17(1), 336; https://doi.org/10.3390/su17010336 - 4 Jan 2025

Cited by 1 | Viewed by 1190

Abstract

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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25 pages, 8432 KiB

Open AccessArticle

Numerical Investigation of Jet-Propelled Multiple-Vehicle Hyperloop System Considering the Suspension Gap

by Seraj Alzhrani, Mohammed M. Abdulla, Khalid Juhany and Ibraheem AlQadi

Sustainability 2024, 16(21), 9465; https://doi.org/10.3390/su16219465 - 31 Oct 2024

Cited by 1 | Viewed by 1134

Abstract

The Hyperloop system offers revolutionary transportation, aiming for near-sonic speeds in a low-pressure environment. The aerodynamic design challenges of multiple vehicles in a confined tube remain largely unexplored, particularly regarding vehicle spacing and suspension gaps. This study investigates a jet-propelled, multi-vehicle Hyperloop system [...] Read more.

k - ω

turbulence model. Analysis of suspension gaps and vehicle spacing on drag and thrust revealed that suspension gaps cause significant jet deflection, reducing effective thrust and increasing drag. It was found that vehicle suspension, with a 75 mm suspension gap, increased drag by 58% at Mach 0.7 compared to the unsuspended configuration. Meanwhile, smaller vehicle spacing (

X_{v} = 0.25 L_{v}

) reduced the drag by up to 50%, enhancing system efficiency. The results emphasize the need to address the effect of jet deflection and optimize vehicle spacing for maximum energy savings. These findings offer valuable insights for enhancing aerodynamic performance in multi-vehicle Hyperloop systems. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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23 pages, 1838 KiB

Open AccessArticle

Analysis of Factors Affecting the Accuracy of MFD Construction in Multisource Complex Data Scenarios

by Rongrong Hong

Sustainability 2024, 16(18), 8018; https://doi.org/10.3390/su16188018 - 13 Sep 2024

Cited by 1 | Viewed by 1183

Abstract

The macroscopic fundamental diagram (MFD), as a model depicting the correlation between traffic flow parameters at the network level, offers a new way to understand regional traffic state using derived traffic flow data from detectors directly. The accuracy of MFD construction is directly related to factors such as the type of detectors, their distribution, and their quantity within the road network. Understanding these influencing factors and mechanisms is crucial for enhancing the reliability of MFD-based applications such as congestion pricing and threshold control. Present investigations on factors that affect MFD construction’s accuracy have frequently been confined to sensitivity analysis of single-source data and individual influencing factors such as the penetration rate. However, the accuracy of MFD is influenced by a multitude of factors, including the spatial distribution equilibrium, penetration rate, and coverage rate of traffic flow detection equipment. Despite this, this paper utilized the Q-paramics simulation software V6.8.1 to acquire simulated data and employed the orthogonal experimental method from statistics to explore the impact mechanisms of factors on the accuracy of MFD construction. The results of the case study demonstrated that when the penetration rate reaches 20%, the error remains approximately around 10%; once the coverage rate surpasses 45%, the errors stabilize at around 10%. This study provides practical guidance for traffic management and planning decisions aimed at promoting sustainable development through the application of MFD in real-world road networks. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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16 pages, 2713 KiB

Open AccessArticle

Joint Optimal Design of Electric Bus Service and Charging Facilities

by Yuan Liu, Yamin Ding, Pei Jiang, Xugang Jin, Xinlin Wu and Zhanji Zheng

Sustainability 2024, 16(14), 6155; https://doi.org/10.3390/su16146155 - 18 Jul 2024

Cited by 1 | Viewed by 1480

Abstract

With the development of new energy technologies, fuel buses with internal combustion engines are gradually being replaced by electric buses. In order to save on system costs, an optimization model is proposed to jointly design the bus service and charging facilities. Considering the complexity of the original problem, the problem is decomposed into two subproblems, i.e., bus service design and charging facilities design. The bus service design is solved by a genetic algorithm with an embedded enumeration method. The non-linear charging facilities design problem is firstly converted to a linear problem and then solved by existing solving software. Sensitivity analysis of parameters such as passenger flow demand, charging power, and bus stopping time is also conducted to reveal their impact on the optimization of electric bus lines. The results indicate that, compared to the commonly used depot charging strategy, the proposed method reduces the operating cost per unit hour from RMB 16,378.30 to RMB 8677.99, a 47% reduction, and decreases the system cost from RMB 36,386.30 to RMB 29,637.99, an 18.5% reduction. This study addresses the charging and operation problem of electric bus lines. By considering charging vehicles while in operation, a joint optimization model for the operation of electric bus lines and the layout of charging facilities is established. An algorithm based on the combination of a genetic algorithm and enumeration method is designed, combined with a linear programming solver to solve the problem. Full article

(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)

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