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Sustainable Traffic and Mobility

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

Deadline for manuscript submissions: 28 May 2024 | Viewed by 4205

Special Issue Editors

Dr. Yanyan Xu

E-Mail Website
Guest Editor
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: human mobility; urban science; energy and environment; smart transportation
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Shen

E-Mail Website
Guest Editor
Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai, China
Interests: shared mobility; public transit; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Chunxiao Li

E-Mail Website
Guest Editor
Department of Data and Business Intelligence, Shanghai Jiao Tong University, Shanghai 200030, China
Interests: fintech; mobility; AI ethics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is witnessing an ever-increasing demand for transportation and mobility solutions that are not only efficient and convenient, but also environmentally sustainable. As we move towards a more sustainable future, it becomes imperative to address the challenges posed by transportation-related emissions, congestion, and resource depletion. To explore innovative approaches and advancements in sustainable transportation and mobility, we are pleased to announce a Special Issue focused on this critical topic.

The Special Issue on Sustainable Transportation and Mobility aims to bring together cutting-edge research and diverse perspectives from scholars, researchers, and practitioners across the globe. We invite original contributions covering, but not limited to, the following topics:

  • Green and electric vehicles: Advances in electric and alternative fuel vehicles, charging infrastructure, battery technologies, and energy efficiency measures.
  • Public transportation: Innovations in public transport systems, including bus rapid transit, light rail, and shared mobility solutions, to reduce congestion and emissions.
  • Active transportation: Studies on walking, cycling, and other non-motorized modes of transport, promoting healthier and eco-friendly options.
  • Smart and intelligent transportation systems: Integration of digital technologies, IoT, and data analytics to optimize transportation networks and improve user experience.
  • Urban planning and design for sustainable mobility: Research on urban planning, infrastructure development, and policies that prioritize sustainable transportation options.
  • Policy and governance: Assessments of governmental policies, regulations, and incentives to accelerate the adoption of sustainable transportation practices.
  • Environmental impact and life cycle analysis: Studies examining the life cycle environmental impacts of various transportation modes and technologies.
  • Future mobility trends: Exploration of emerging trends, such as autonomous vehicles, hyperloop, and other transformative mobility concepts.

You may choose our Joint Special Issue in Vehicles.

Dr. Yanyan Xu
Dr. Yu Shen
Dr. Chunxiao Li
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

  • smart transportation
  • public transportation
  • travel behavior
  • shared mobility
  • environmentally friendly mobility

Published Papers (4 papers)

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Research

15 pages, 9733 KiB  
Article
Quantifying Individual PM2.5 Exposure with Human Mobility Inferred from Mobile Phone Data
by Zhaoping Hu, Le Huang, Xi Zhai, Tao Yang, Yaohui Jin and Yanyan Xu
Sustainability 2024, 16(1), 184; https://doi.org/10.3390/su16010184 - 25 Dec 2023
Viewed by 802
Abstract
Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM2.5 exposure [...] Read more.
Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM2.5 exposure by combining residents’ mobility traces and PM2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM2.5 concentration, we quantify individual PM2.5 exposure and find that the average PM2.5 exposure of residences in Shanghai is 60.37 ug·h·m−3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM2.5 exposure in Shanghai. Our PM2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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16 pages, 2762 KiB  
Article
Exploration of Riding Behaviors of Food Delivery Riders: A Naturalistic Cycling Study in Changsha, China
by Zihao Zhang and Chenhui Liu
Sustainability 2023, 15(23), 16227; https://doi.org/10.3390/su152316227 - 23 Nov 2023
Viewed by 1026
Abstract
Aimed at the riding safety issue of food delivery riders in China who mainly travel by electric bikes, a naturalistic cycling study was conducted by collecting the naturalistic cycling data of dozens of food delivery riders in Changsha, China, to identify their riding [...] Read more.
Aimed at the riding safety issue of food delivery riders in China who mainly travel by electric bikes, a naturalistic cycling study was conducted by collecting the naturalistic cycling data of dozens of food delivery riders in Changsha, China, to identify their riding characteristics. It was found that the participating food delivery riders are mainly undereducated young male adults, and the primary reason for them to take the job is the flexible working hours. Furthermore, they frequently work overtime and admit to often committing risky riding behaviors to deliver food on time. The analysis of their riding trajectories indicates that they delivered orders all day long, rather than just at mealtimes. They mainly work within 3 km of the delivery station, and the average riding radius was 2.39 km. Male riders, riders working less than one year, and riders with high school education had a relatively fast riding speed. These findings provide valuable new insights for agencies to understand the riding characteristics of food delivery riders and to formulate the appropriate countermeasures to improve their occupational safety. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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19 pages, 6025 KiB  
Article
Optimal Predictive Torque Distribution Control System to Enhance Stability and Energy Efficiency in Electric Vehicles
by Arash Mousaei and Yahya Naderi
Sustainability 2023, 15(20), 15155; https://doi.org/10.3390/su152015155 - 23 Oct 2023
Cited by 2 | Viewed by 882
Abstract
This article presents a novel approach to address the critical issues of stable rotation and energy efficiency in electric vehicles (EVs). To achieve these objectives, we propose a comprehensive control system that leverages the power of optimization through optimal predictive control methods. The [...] Read more.
This article presents a novel approach to address the critical issues of stable rotation and energy efficiency in electric vehicles (EVs). To achieve these objectives, we propose a comprehensive control system that leverages the power of optimization through optimal predictive control methods. The central idea revolves around minimizing the predicted tracking error for future time steps by intelligently determining control inputs. In this innovative approach, we emphasize the dynamic adjustment of weight coefficients and optimization of wheel torque to strike a delicate balance between energy consumption and enhanced vehicle stability. The result is an adept controller that not only ensures vehicle stability but also significantly reduces energy consumption. Given the inherent limitations of electric motors, especially in terms of torque during vehicle operation, and the growing importance of energy conservation, our method tailors weight coefficients to generate optimal wheel torque. This ensures that the electric motors operate within their power range, thereby minimizing energy consumption and extending the overall efficiency of EVs. The combination of stable rotation and energy efficiency offered by this control system represents a promising step forward in the realm of electric vehicles, making them more sustainable and environmentally friendly while maintaining the high standards of performance and safety that consumers expect. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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24 pages, 28919 KiB  
Article
Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”
by Hongxia Feng, Yaotong Chen, Jinyi Wu, Zhenqian Zhao, Yuanqing Wang and Zhuoting Wang
Sustainability 2023, 15(20), 15103; https://doi.org/10.3390/su152015103 - 20 Oct 2023
Cited by 2 | Viewed by 950
Abstract
Urban rail transit stations serve as pivotal hubs that facilitate the advancement of diverse economic activities. Based on different types of metro stations, the sustainable and coordinated development of public transport and land use can be achieved through rational land use planning and [...] Read more.
Urban rail transit stations serve as pivotal hubs that facilitate the advancement of diverse economic activities. Based on different types of metro stations, the sustainable and coordinated development of public transport and land use can be achieved through rational land use planning and the rational allocation of urban infrastructure and public service facilities. Drawing upon mobile phone signaling data and land use data, this article presents a complex classification methodology for metro stations, employing the lens of “passenger flow behavior—land use structure—job-housing density” in the context of Xi’an. The stations are categorized into six distinct types, including employment-led stations with a job–housing density balance, as well as stations characterized by job–housing mismatch with a high residential density. The results indicate a low level of coupling between the passenger flow patterns of the stations and the spatial characteristics of the station areas. In addition, the spatial distributions of the stations demonstrate a significant aggregation effect in each station type, while the degree of integration between the different station types remains limited. These findings collectively suggest that the urban rail transit stations in Xi’an have not achieved complementary development, thereby reflecting a notable trend of cross-regional commuter flow in the city. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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