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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: closed (31 March 2025) | Viewed by 28819

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Special Issue Editors

Dr. Mohammad Miralinaghi

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Guest Editor

Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: autonomous transportation; electric transportation; smart mobility; traffic congestion management; sustainable transportation systems
Special Issues, Collections and Topics in MDPI journals

Dr. Wubeshet Woldemariam

E-Mail Website
Guest Editor

Mechanical and Civil Engineering Department, Purdue University Northwest, Hammond, IN 46323, USA
Interests: transportation sustainability; smart cities; connected and autonomous vehicles and their interaction with the built environment; infrastructure design; infrastructure network performance analysis; traffic engineering

Special Issue Information

Dear Colleagues,

The transportation sector is among the top contributors of greenhouses gases (GHGs) and other air pollutants. With the increase in travel demand and traffic congestion, the emission levels of GHGs and other air pollutants are likely to become more damaging in the future. However, the recent innovations in vehicle technologies, transportation electrification, and autonomous driving, coupled with proper planning, have the potential to reduce these emissions and the overall carbon footprint of the transportation sector. Electric vehicles have no tailpipe emissions, and if they are charged using renewable power sources, such as solar, hydro and wind, can represent a significant leap forward towards sustainable transportation. Autonomous vehicles can be programmed to operate in an energy-efficient manner. Shared autonomous vehicles can further increase the efficiency of the transportation system.

An efficient and equitable public transportation system is key to achieving a long-term solution to the problem of traffic congestion. The electrification of both intercity and intracity transit buses can further improve the ecological sustainability of the transportation system. Dynamic wireless charging systems and hydrogen refueling stations for fuel-cell vehicles can play crucial role in transportation electrification, particularly for transit electrification. The operation of electric vehicles is known to be three to four times more economical than internal-combustion-engine vehicles. These operational savings can be translated to reduced fares for bus passengers, thereby making bus services more affordable and equitable.

This Special Issue aims to provide a platform for disseminating the advancement in innovative and sustainable approaches to transportation. With the rapid changes in transportation technologies in the recent years, it is imperative to develop strategies and planning initiates to harness the maximum benefits of these technologies and evaluate how these technologies could contribute to sustainability in the transportation sector. Specifically, this Special Issue focuses on the following topics: (i) presenting current, state-of-the-art, innovative planning approaches under emerging transportation technologies with regard to their potential to improve financial, social or ecological sustainability, and mathematical modeling; and (ii) identifying potential research directions and technologies that will drive innovations in the field of sustainable transportation systems.

The following are the focus areas of this Special Issue:

Connected and autonomous vehicles (CAVs) and their interactions with the built environment;
Environmental impacts of CAV technologies;
Travel demand modeling considering CAVs;
Transportation electrification and sustainability;
Planning electrified public transportation;
Sustainable EV charging infrastructure;
Transportation infrastructure design in the era of smart cities.

This Special Issue will supplement the existing literature on the above-stated topics and provide research results that will help to strengthen existing findings on sustainability related to transportation and infrastructure development. Additionally, this Special Issue will help researchers and practitioners consider the latest findings on transportation technologies and develop a sustainable transportation and infrastructure system as an important component of smart cities.

Dr. Mohammad Miralinaghi
Dr. Wubeshet Woldemariam
Guest Editors

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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.

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Keywords

transportation electrification
sustainability
smart cities
connected and autonomous vehicles
travel demand modeling

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Published Papers (10 papers)

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Research

19 pages, 1090 KiB

Open AccessArticle

Research on the Impact of Autonomous Vehicles on the Environment and Sustainability of Transport Companies

by Laima Naujokienė, Valentina Peleckienė, Kristina Vaičiūtė and Kristina Kulikauskienė

Sustainability 2025, 17(12), 5510; https://doi.org/10.3390/su17125510 - 15 Jun 2025

Cited by 1 | Viewed by 959

Abstract

The significance of sustainable development and the increasing acknowledgment of climate change-related issues necessitate modifications in the transportation sector, the most polluting of all economic sectors, which is highly reliant on fossil fuels. The transportation sector significantly adversely affects the environment and human health. However, there is still a lack of research in scientific literature confirming the effectiveness of autonomous vehicles. The aim of the article is to examine the experience of autonomous vehicles in society. The primary attitudes of individuals regarding autonomous vehicles were assessed. The research technique involves a statistical examination of elements affecting client apprehensions over the usage of autonomous vehicles (AVs). Results of the research show the impact of autonomous vehicles on the quality of service of transport companies. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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20 pages, 11598 KiB

Open AccessArticle

Impact of Regional and Seasonal Characteristics on Battery Electric Vehicle Operational Costs in the U.S.

by Kyung-Ho Kim, Namdoo Kim, Ram Vijayagopal, Kevin Stutenberg and Sung-Ho Hwang

Sustainability 2025, 17(8), 3282; https://doi.org/10.3390/su17083282 - 8 Apr 2025

Viewed by 497

Abstract

This study investigates the operational cost competitiveness of battery electric vehicles (BEVs) in the United States, considering regional climates, energy prices, and driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs), the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled (VMT), and state-specific energy prices. Using detailed simulations, the study evaluates energy consumption across varying temperatures and driving distances. The findings reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs are more economical for long-range trips and cold environments, due to the excessive cost of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low temperatures. HEVs are identified as the most cost-efficient option in regions like New England, characterized by high residential electricity prices. These insights are critical for shaping vehicle electrification strategies, particularly under diverse regional and seasonal conditions, and for advancing policies on alternative energy and fuels. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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25 pages, 7009 KiB

Open AccessArticle

Modular Scheduling Optimization of Multi-Scenario Intelligent Connected Buses Under Reservation-Based Travel

by Wei Shen, Honglu Cao and Jiandong Zhao

Sustainability 2025, 17(6), 2645; https://doi.org/10.3390/su17062645 - 17 Mar 2025

Viewed by 640

Abstract

In the context of big data and intelligent connectivity, optimizing scheduled bus dispatch can enhance urban transit efficiency and passenger experience, which is vital for the sustainable development of urban transportation. This paper, based on existing fixed bus stops, integrates traditional demand-responsive transit and travel booking models, considering the spatiotemporal variations in scheduled travel demands and passenger flows and addressing the combined scheduling issues of fixed-capacity bus models and skip-stop strategies. By leveraging intelligent connected technologies, it introduces a dynamic grouping method, proposes an intelligent connected bus dispatching model, and optimizes bus timetables and dispatch control strategies. Firstly, the inherent travel characteristics of potential reservation users are analyzed based on actual transit data, subsequently extracting demand data from reserved passengers. Secondly, a two-stage optimization program is proposed, detailing passenger boarding and alighting at each stop and section passenger flow conditions. The first stage introduces a precise bus–traveler matching dispatch model within a spatial–temporal–state framework, incorporating ride matching to minimize parking frequency in scheduled travel scenarios. The second stage addresses spatiotemporal variations in passenger demand and station congestion by employing a skip-stop and bus operation control strategy. This strategy enables the creation of an adaptable bus operation optimization model for temporal dynamics and station capacity. Finally, a dual-layer optimization model using an adaptive parameter grid particle swarm optimization algorithm is proposed. Based on Beijing’s Route 300 operational data, the simulation-driven study implements contrasting scenarios of different bus service patterns. The results demonstrate that this networked dispatching system with dynamic vehicle grouping reduces operational costs by 29.7% and decreases passenger waiting time by 44.15% compared to baseline scenarios. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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18 pages, 533 KiB

Open AccessArticle

Breaking Commuting Habits: Are Unexpected Urban Disruptions an Opportunity for Shared Autonomous Vehicles?

by Alessandro La Delfa and Zheng Han

Sustainability 2025, 17(4), 1614; https://doi.org/10.3390/su17041614 - 15 Feb 2025

Cited by 1 | Viewed by 1017

Abstract

While extensive research has examined how major life events affect travel habits, less attention has been paid to the impact of minor environmental changes on commuting behavior, particularly regarding shared autonomous vehicles (SAVs). This study investigated how daily disruptions and incremental environmental changes influence commuter behavior patterns and SAV adoption in Shanghai, applying the theory of interpersonal behavior framework. The study surveyed 517 Shanghai residents, examining travel satisfaction, commuting habits, psychological factors (such as habit strength and satisfaction), and attitudes towards SAVs. Structural equation modeling was employed to test hypotheses about psychological factors influencing SAV adoption, while logistic regression analyzed how these factors affected mode choice across different disruption contexts. Analysis revealed that psychological factors, particularly habit and satisfaction, were stronger predictors of SAV adoption than attitude-based factors. Route obstructions and workplace relocations significantly increased SAV consideration. Even minor, recurring disruptions, such as construction zones, showed strong effects on commuting behavior, supporting the habit discontinuity hypothesis and emphasizing the importance of minor disruptions in driving behavioral change. The study extends the theory of interpersonal behavior by integrating habit discontinuity theory to explain how minor disruptions drive SAV adoption. This research provides actionable insights for urban planners and policymakers, recommending that SAV trials and targeted interventions be implemented during infrastructure changes or other commuting disruptions to promote SAV adoption and foster more sustainable transportation systems. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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21 pages, 3400 KiB

Open AccessArticle

Veterans’ Perceptions of Shared Autonomous Electric Shuttles: A Pre- and Post-Exposure Assessment

by Isabelle Wandenkolk, Sherrilene Classen, Justin Mason and Seung Woo Hwangbo

Sustainability 2025, 17(2), 508; https://doi.org/10.3390/su17020508 - 10 Jan 2025

Viewed by 968

Abstract

Veterans often face transportation barriers, but advances in technology enable real-world testing of shared autonomous electric vehicles as potential energy-efficient solutions. While previous research has assessed civilians’ perceptions of autonomous vehicles (AVs), veterans—due to unique military experiences and health conditions—represent a distinct demographic. This study investigates veterans’ perceptions of autonomous shuttles (ASs) to assess whether these innovations may foster sustainable transportation behaviors. Leveraging data from the Autonomous Vehicle User Perception Survey (AVUPS), this study assessed AS perceptions among 77 veterans across four Florida cities before and after exposure. Results indicated significant increases in intention to use and total acceptance and a decrease in perceived barriers, with no change in well-being. Urban veterans showed improvements across multiple subscales, while rural veterans only showed reduced perceived barriers. Those with initially low total acceptance scores demonstrated greater improvements, particularly in intention to use and perceived barriers. The analysis of survey items showed increased trust, greater willingness to multitask, improved safety perceptions, and reduced concerns about declining driving abilities and hesitations toward AVs, with the latter three items remaining significant after correction. Overall, AS exposure positively influenced veterans’ perceptions, and the results point to the potential of ASs as a sustainable transportation option for veterans. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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22 pages, 2821 KiB

Open AccessArticle

Simulation-Based Resilience Evaluation for Urban Rail Transit Transfer Stations

by Xinyao Yin, Junhua Chen and Yuexuan Li

Sustainability 2024, 16(9), 3790; https://doi.org/10.3390/su16093790 - 30 Apr 2024

Cited by 2 | Viewed by 1901

Abstract

Disturbances often occur in transfer stations; however, little is known about the weaknesses of transfer stations and their ability to cope with passenger flows. Therefore, this paper introduces resilience into the study of transfer stations to enhance their emergency response processes and improve the sustainability of URT networks. It establishes a two-level fuzzy evaluation model, using the G1 weighting method, to assess resilience across various scenarios (daily operation, heavy passenger flow, and emergencies) and identify weaknesses; then, corresponding enhancement strategies are proposed. First, factor sets are established according to resilience stages, including rapidity before disturbance, robustness, redundancy, resourcefulness, and rapidity after disturbance. Using the G1 method, the weight matrix for each factor is calibrated, and a membership degree matrix is determined based on their affiliation with the review set. Multiplying the weight matrix and membership degree matrix yields the resilience value. We apply these steps to a representative station with the assistance of Anylogic simulation in calculating the hard-to-obtain data, yielding a peak-hour resilience value of 0.3425, which indicates a “poor” rating in the review set. By combining the peak-hour resilience with resilience curves under different multiples of peak-hour flows, an enhancement prioritization strategy is proposed for the station, which can act as a reference for the management of URT transfer stations. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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18 pages, 7567 KiB

Open AccessArticle

What Is the Connection? Understanding Shared Micromobility Links to Rail Public Transit Systems in Major California Cities

by Mengying Ju, Elliot Martin and Susan Shaheen

Sustainability 2024, 16(2), 555; https://doi.org/10.3390/su16020555 - 9 Jan 2024

Cited by 5 | Viewed by 2530

Abstract

As shared micromobility (bikes and scooters) has proliferated throughout urban areas, there has been growing interest in how it facilitates connections with rail transit systems. This study explores the magnitude of interactions between shared micromobility and rail public transit systems using shared micromobility trip data and rail transit schedule data. We evaluate over one million trips from October 2019 to February 2020 in four California cities (San Francisco, Los Angeles, Sacramento, and San Jose) and develop criteria to identify trips connecting to rail transit. These include spatial and temporal rules, such as whether a trip starts/terminates close to public transit stations and whether a trip takes place when transit systems are operating. The criteria are examined via sensitivity analyses. The results indicate the degree of interaction between rail public transit and shared micromobility varies across cities and systems (i.e., docked/dockless). Most connections take place in the downtown or around public transit hubs. About 5–20% of all shared micromobility trips are identified as accessing or egressing from rail transit. These connecting trips exhibit commute-driven patterns and greater measured velocities. We conclude by examining the applicability of incorporating schedule information into the identification process of shared micromobility trips connecting to rail transit systems. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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20 pages, 3813 KiB

Open AccessArticle

Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand

by Sahar Koohfar, Wubeshet Woldemariam and Amit Kumar

Sustainability 2023, 15(5), 4258; https://doi.org/10.3390/su15054258 - 27 Feb 2023

Cited by 21 | Viewed by 4699

Abstract

Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addressed efficiently to dispatch energy. Machine learning techniques have been demonstrated to be effective in forecasting loads. This research applies six machine learning methods to predict the charging demand for EVs: RNN, LSTM, Bi-LSTM, GRU, CNN, and transformers. A dataset containing five years of charging events collected from 25 public charging stations in Boulder, Colorado, USA, is used to validate this approach. Compared to other highly applied machine learning models, the transformer method outperforms others in predicting charging demand, demonstrating its ability for time series forecasting problems. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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17 pages, 5822 KiB

Open AccessArticle

Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach

by Sahar Koohfar, Wubeshet Woldemariam and Amit Kumar

Sustainability 2023, 15(3), 2105; https://doi.org/10.3390/su15032105 - 22 Jan 2023

Cited by 66 | Viewed by 9680

Abstract

Electric vehicles have been gaining attention as a cleaner means of transportation that is low-carbon and environmentally friendly and can reduce greenhouse gas emissions and air pollution. Despite EVs’ many advantages, widespread adoption will negatively affect the electric grid due to their random and volatile nature. Consequently, predicting the charging demand for electric vehicles is becoming a priority to maintain a steady supply of electric energy. Time series methodologies are applied to predict the charging demand: traditional and deep learning. RNN, LSTM, and transformers represent deep learning approaches, while ARIMA and SARIMA are traditional techniques. This research represents one of the first attempts to use the Transformer model for predicting EV charging demand. Predictions for 3-time steps are considered: 7 days, 30 days, and 90 days to address both short-term and long-term forecasting of EV charging load. RMSE and MAE were used to compare the model’s performance. According to the results, the Transformer outperforms the other mentioned models in terms of short-term and long-term predictions, demonstrating its ability to address time series problems, especially EV charging predictions. The proposed Transformers framework and the obtained results can be used to manage electricity grids efficiently and smoothly. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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22 pages, 5525 KiB

Open AccessArticle

Conceptualizing Floating Logistics Supporting Facility as Innovative and Sustainable Transport in Remote Areas: Case of Small Islands in Indonesia

by Raja Oloan Saut Gurning, Gunung Hutapea, Edward Marpaung, Johny Malisan, Dedy Arianto, Wilmar Jonris Siahaan, Bagas Bimantoro, Sujarwanto, I Ketut Suastika, Agoes Santoso, Danu Utama, Abdy Kurniawan, Sri Hardianto, Wasis Dwi Aryawan, Miskli Iska Nanda, Ezra Jonathan Simatupang, I Ketut Suhartana and Teguh Pairunan Putra

Sustainability 2022, 14(14), 8904; https://doi.org/10.3390/su14148904 - 20 Jul 2022

Cited by 2 | Viewed by 3698

Abstract

Transportation is the main component that ensures the optimal distribution of goods in the maritime logistics system of small Islands. Therefore, this research developed a Floating Logistics Supporting Facility (FLSF) to overcome the logistics problems on small Islands by implementing sustainable operational systems. The research samples used were Nias, Kisar, and Sangihe Islands in Indonesia, with dimension, propulsion, operation, and mooring utilized as the four primary considerations. An FLSF was applied as a floating terminal capable of accommodating loading and unloading operations, ship mooring, cargo storage, stacking, and dooring services. The result showed that an FLSF can be applied to logistics activities while considering the safety aspects and related regulations. Based on the results, the FLSF can improve the quality of sustainable logistics operations and increase economic growth in remote islands. Full article

(This article belongs to the Special Issue Innovative and Sustainable Development of Transportation)

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