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Special Issue "Emerging Transportation Solutions for Safer and Greener Future Mobility"

Special Issue Editors

Dr. Bo Yu
E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: road safety; driving behavior modeling; eco-driving; connected and autonomous vehicle; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Gao
E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, Chalmers University of Technology, SE-412 96 Goteburg, Sweden
Interests: transportation electrification; shared mobility and connected vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Yueru Xu
E-Mail Website
Guest Editor
Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China
Interests: road safety and environment; intelligent transportation system; transportation energy consumption
Dr. You Kong
E-Mail Website
Guest Editor
College of Transport and Communication, Shanghai Maritime University, Shanghai 201306, China
Interests: autonomous vehicles; parking management; intelligent transportation system

Special Issue Information

Dear Colleagues,

With the emergence of new technologies such as connected and automated vehicles, big data, shared mobility, and electric vehicles, intelligent transportation systems (ITS) are constantly being updated and upgraded to provide comprehensive services that are safer, more sustainable, and more efficient. However, opportunities are always accompanied by challenges. New technologies bring a great deal of new research and practical questions as well, which need collective efforts from interdisciplinary fields.

At present, safety issues in ITS have attracted extensive attention, and human factors have been found to be one of the most important sources of risk. More in-depth investigations on potential impacting factors and prevention methods of human-factor-related traffic accidents are still needed—for example, the driving styles and risk compensation behavior under ITS, the effectiveness of advanced driver-assistance systems in preventing traffic accidents, behavior modeling and safety protection of vulnerable road users (e.g., cyclists, pedestrians), dangerous scenarios under autonomous driving, etc. Meanwhile, ITS bring a large amount of multi-source heterogeneous data, including natural driving data, crash data of autonomous vehicles, and wide-range vehicle trajectory data. This leads to the extension of the corresponding analysis methods, from traditional statistical methods to machine learning and deep learning. The application of these novel methods will further improve traffic safety in emerging ITS.

COP27 has further urged the world to take solid actions to reduce greenhouse emissions from different sectors. Electric vehicles, shared mobility, and intelligent connected vehicles are potential sustainable solutions in the transportation sector, but need further scientific planning, operation optimization, promotion strategies, and regulations to fulfill their sustainable merits. For instance, many cities are in the early stages of electrifying transportation systems, with insufficient planning and inadequate charging facilities. The rapid increase in loads from electric vehicle charging is not incorporated into the power grid maintenance and upgrade plans. Additionally, due to range limitations and charging needs, electric buses operate with lower availability than conventional buses, and many users are not willing to shift towards sustainable mobility alternatives. Although some studies have investigated these topics to some extent, more explorations should be made to advance these areas by leveraging emerging methods such as big data, machine learning, and edge computing, which are expected to have superior performances.

This Special Issue is devoted to addressing challenges in the modeling, optimization, analysis, and policymaking of utilizing emerging transportation solutions for improving transportation safety and reducing emissions. This Special Issue especially welcomes studies that utilize machine learning, new data resources, and interdisciplinary solutions to promote the safety and sustainability of transportation systems. It is open to any subject area of the related theme; of particular interest are traffic safety and risk analysis, human factors in ITS, eco-driving, transportation electrification and shared mobility, connected and automated vehicles, advanced driver-assistance systems, etc.

Dr. Bo Yu
Dr. Kun Gao
Dr. Yueru Xu
Dr. You Kong
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. International Journal of Environmental Research and Public Health 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 2500 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

  • driving behavior and risk evaluation
  • traffic safety and human factors in ITS
  • environmental impact analysis
  • transportation electrification
  • shared mobility
  • interdisciplinary solutions
  • travel behavior regarding sustainable transportation modes
  • eco-driving
  • intelligent transportation systems (ITS)
  • connected and automated vehicles (CAV)
  • advanced driver-assistance systems (ADAS)
  • machine learning
  • new data resources
  • edge computing

Published Papers (3 papers)

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Research

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Article
Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation
Int. J. Environ. Res. Public Health 2022, 19(13), 7699; https://doi.org/10.3390/ijerph19137699 - 23 Jun 2022
Viewed by 214
Abstract
Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention [...] Read more.
Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention in recent years. This paper provides a new framework to evaluate the hydroplaning potential based on emerging 3D laser scanning technology and water-film thickness estimation. The 3D information of the road surface was captured using a vehicle-mounted Light Detection and Ranging (LiDAR) system and then processed by a wavelet-based filter to remove the redundant information (surrounding environment: trees, buildings, and vehicles). Then, the water film thickness on the given road surface was estimated based on a proposed numerical algorithm developed by the two-dimensional depth-averaged Shallow Water Equations (2DDA-SWE). The effect of the road surface geometry was also investigated based on several field test data in Shanghai, China, in January 2021. The results indicated that the water-film is more likely to appear on the rutting tracks and the pavement with local unevenness. Based on the estimated water-film, the hydroplaning speeds were then estimated to represent the hydroplaning risk of asphalt pavement in rainy weather. The proposed method provides new insights into the water-film estimation, which can help drivers make effective decisions to maintain safe driving. Full article
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Article
Analysis of the Effect of Human-Machine Co-Driving Vehicle on Pedestrian Crossing Speed at Uncontrolled Mid-Block Road Sections: A VR-Based Case Study
Int. J. Environ. Res. Public Health 2022, 19(12), 7208; https://doi.org/10.3390/ijerph19127208 - 12 Jun 2022
Viewed by 432
Abstract
The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of [...] Read more.
The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of 30 college students are recruited, and each participant is required to complete 5 street-crossing simulator trials facing human-machine co-driving vehicles with varying time headway levels and speeds. The correlations and differences between demographic information, time headway, vehicle speed, and pedestrian crossing speed are analyzed. The results show that gender and pedestrian’s trust in human-machine co-driving vehicles are significantly correlated with pedestrian crossing speed. The pedestrian crossing speed increases with the increase in vehicle speed and decreases with the increase in vehicle time headway. In addition, the time headway has a stronger correlation with the pedestrian crossing speed than the vehicle speed. The findings will provide theoretical and methodological support for the formulation of pedestrian crossing control measures in the stage of human-machine co-driving. Full article
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Review

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Review
Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review
Int. J. Environ. Res. Public Health 2022, 19(12), 7310; https://doi.org/10.3390/ijerph19127310 - 14 Jun 2022
Viewed by 362
Abstract
Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of [...] Read more.
Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised. Full article
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