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Sustainable Traffic and Mobility
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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: closed (31 May 2025) | Viewed by 19813

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

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  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (8 papers)

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Research

39 pages, 11225 KiB  
Article
Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models
by Roosmayri Lovina Hermaputi and Chen Hua
Sustainability 2024, 16(19), 8454; https://doi.org/10.3390/su16198454 - 28 Sep 2024
Cited by 1 | Viewed by 1322
Abstract
Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) [...] Read more.
Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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27 pages, 5214 KiB  
Article
Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis
by Yuan Chen and Seok Swoo Cho
Sustainability 2024, 16(17), 7797; https://doi.org/10.3390/su16177797 - 6 Sep 2024
Viewed by 3772
Abstract
In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers [...] Read more.
In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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21 pages, 22221 KiB  
Article
Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing
by Man Zhang, Dongwei Tian, Jingming Liu and Xuehua Li
Sustainability 2024, 16(17), 7386; https://doi.org/10.3390/su16177386 - 27 Aug 2024
Cited by 3 | Viewed by 1850
Abstract
Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban [...] Read more.
Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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18 pages, 3144 KiB  
Article
A Graph-Based Scheme Generation Method for Variable Traffic Organization in Parking Lots
by Jing Cao, Haichao Ling, Tao Li, Shiyu Wang, Shengchuan Jiang and Cong Zhao
Sustainability 2024, 16(11), 4778; https://doi.org/10.3390/su16114778 - 4 Jun 2024
Viewed by 1599
Abstract
To deal with the traffic congestion issues caused by the imbalance between supply and demand in parking lots, this study proposes a graph-based scheme generation method for variable traffic organization in parking lots. A graph-based methodological framework is developed to dynamically generate feasible [...] Read more.
To deal with the traffic congestion issues caused by the imbalance between supply and demand in parking lots, this study proposes a graph-based scheme generation method for variable traffic organization in parking lots. A graph-based methodological framework is developed to dynamically generate feasible traffic organization schemes and adapt the road networks of parking lots based on fluctuating demands. First, we design a parking lot-tailored enhanced primal approach by adding a directedness attribute while maintaining road continuity to ensure correspondence between generated graphs and traffic organization schemes. A graph generation algorithm is then designed to generate all feasible schemes in the scenario, deploying the depth-first search algorithm to check the connectivity of each graph and narrowing down feasible options based on domain knowledge. Finally, the initial parking space distribution and parking demand are used as inputs to calculate the total vehicle cruising time under each scheme, serving as the key indicator to select the optimal organization scheme. A single-level parking lot model is developed to verify the performance of our method under six initial parking space distributions. This model is built using the multi-agent simulation platform AnyLogic version 8.8.6, which enables the quick transformation of organization schemes by customizing the behavior of different agents. The results show that the optimal organization scheme generated by the proposed method can reduce vehicle cruising time by 15–46% compared to conventional traffic organization, varying according to parking space distributions. The significance of this study lies in its potential to mitigate traffic congestion in parking lots, thereby enhancing overall efficiency and user satisfaction. By dynamically adapting to fluctuating parking demands, this method provides a robust solution for urban planners and parking lot operators aiming to optimize traffic flow and reduce unnecessary delays. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility)
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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
Cited by 1 | Viewed by 1634
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
Cited by 4 | Viewed by 3372
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 10 | Viewed by 2293
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 4 | Viewed by 2287
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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