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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: 31 March 2025 | Viewed by 11141

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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

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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

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

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

Published Papers (4 papers)

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Research

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 - 09 Jan 2024

Viewed by 690

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 3 | Viewed by 2137

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 12 | Viewed by 4609

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 1 | Viewed by 2246

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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