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Sustainability
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Intelligent Transportation Systems Applications for Sustainability and Safety
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Intelligent Transportation Systems Applications for Sustainability and Safety

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3196

Special Issue Editors

Dr. Dongdong Wang

E-Mail Website1 Website2
Guest Editor
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: machine learning; intelligent transportation system; traffic safety; natural language processing; computer vision; reinforcement learning; carbon footprint reduction
Dr. Amjad Pervez

E-Mail Website
Guest Editor
SPIRIT, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Interests: driving behavior analysis; autonomous vehicles safety and regulations; vulnerable road user safety; travel behavioral change after disasters caused by climate change and pandemics; transportation sustainability; adoption behavior towards emerging transportation (electric vehicles, electric motorcycles); travel demand forecasting
Dr. Chenzhu Wang

E-Mail Website
Guest Editor
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: transportation safety; data mining; driving behaviors; connected autonomous vehicles; intelligent transportation system; roadway safety analysis; transportation sustainability
Dr. Liqiang Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
Interests: blockchain; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing challenges posed by urbanization, population growth, and environmental concerns, the field of Intelligent Transportation Systems (ITS) has emerged as a critical area of research. ITS encompasses a diverse range of technologies and methodologies aimed at enhancing the sustainability and safety of transportation systems. From advanced traffic management systems to smart infrastructure and vehicle automation, ITS holds immense potential to revolutionize transportation, ensuring more sustainable and safer mobility solutions that benefit both the environment and overall quality of life.

The purpose of this Special Issue is to explore the intersection of ITS sustainability and safety. By focusing on sustainability and safety aspects within the realm of ITS, we aim to foster discussions and innovations that contribute to building more resilient, equitable, and environmentally friendly smart transportation systems. This Special Issue seeks to bring together researchers, practitioners, and policymakers to share insights, exchange ideas, and propose solutions to the complex challenges facing modern transportation.

Potential themes for submissions to this Special Issue include, but are not limited to, the following:

  • Artificial intelligence solutions for enhancing traffic safety;
  • Sustainable smart urban mobility solutions;
  • Carbon footprint reduction for efficient ITS solutions;
  • Environmental impact assessment of emerging intelligent vehicle technologies;
  • Safety and reliability enhancements in autonomous driving systems;
  • Human factors and behavior analysis in traffic safety;
  • Policy and regulatory frameworks for promoting ITS sustainability and safety.

We encourage submissions that present original research, case studies, review articles, and policy analyses contributing to the advancement of knowledge in the field of ITS sustainability and safety. We look forward to receiving your contributions and engaging in fruitful discussions on this important topic.

Respectfully,

Dr. Dongdong Wang
Dr. Amjad Pervez
Dr. Chenzhu Wang
Dr. Liqiang Wang
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

  • intelligent transportation system
  • traffic safety
  • machine learning
  • sustainability

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Published Papers (2 papers)

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Research

16 pages, 2508 KiB  
Article
Modeling the Causes of Urban Traffic Crashes: Accounting for Spatiotemporal Instability in Cities
by Hongwen Xia, Rengkui Liu, Wei Zhou and Wenhui Luo
Sustainability 2024, 16(20), 9102; https://doi.org/10.3390/su16209102 - 21 Oct 2024
Viewed by 1162
Abstract
Traffic crashes have become one of the key public health issues, triggering significant apprehension among citizens and urban authorities. However, prior studies have often been limited by their inability to fully capture the dynamic and complex nature of spatiotemporal instability in urban traffic [...] Read more.
Traffic crashes have become one of the key public health issues, triggering significant apprehension among citizens and urban authorities. However, prior studies have often been limited by their inability to fully capture the dynamic and complex nature of spatiotemporal instability in urban traffic crashes, typically focusing on static or purely spatial effects. Addressing this gap, our study employs a novel methodological framework that integrates an Integrated Nested Laplace Approximation (INLA)-based Stochastic Partial Differential Equation (SPDE) model with spatially adaptive graph structures, which enables the effective handling of vast and intricate geospatial data while accounting for spatiotemporal instability. This approach represents a significant advancement over conventional models, which often fail to account for the fluid interplay between time-varying weather conditions, geographical attributes, and crash severity. We applied this methodology to analyze traffic crashes across three major U.S. cities—New York, Los Angeles, and Houston—using comprehensive crash data from 2016 to 2019. Our findings reveal city-specific disparities in the factors influencing severe traffic crashes, which are defined as incidents resulting in at least one person sustaining serious injury or death. Despite some universal trends, such as the risk-enhancing effect of cold weather and pedestrian crossings, we find marked differences across cities in relation to factors like temperature, precipitation, and the presence of certain traffic facilities. Additionally, the adjustment observed in the spatiotemporal standard deviations, with values such as 0.85 for New York and 0.471 for Los Angeles, underscores the varying levels of annual temporal instability across cities, indicating that the fluctuation in crash severity factors over time differs markedly among cities. These results underscore the limitations of traditional modeling approaches, demonstrating the superiority of our spatiotemporal method in capturing the heterogeneity of urban traffic crashes. This work has important policy implications, suggesting a need for tailored, location-specific strategies to improve traffic safety, thereby aiding authorities in better resource allocation and strategic planning. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems Applications for Sustainability and Safety)
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29 pages, 14993 KiB  
Article
Estimation of Greenhouse Gas Emissions of Taxis and the Nonlinear Influence of Built Environment Considering Spatiotemporal Heterogeneity
by Changwei Yuan, Ningyuan Ma, Xinhua Mao, Yaxin Duan, Jiannan Zhao, Shengxuan Ding and Lu Sun
Sustainability 2024, 16(16), 7040; https://doi.org/10.3390/su16167040 - 16 Aug 2024
Cited by 1 | Viewed by 1384
Abstract
The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis [...] Read more.
The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems Applications for Sustainability and Safety)
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