Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Sustainable, Resilient and Smart Mobility

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 15016

Share This Special Issue

Special Issue Editors

Dr. Qixiu Cheng

E-Mail Website
Guest Editor

Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China
Interests: transportation network modeling and optimization; macroscopic and microscopic traffic flow theory; smart transportation management

Dr. Kai Huang

E-Mail Website
Guest Editor

School of Instrument Science and Engineering, Southeast University (Wuxi Campus), Wuxi 214061, China
Interests: optimization and simulation of smart transportation systems; data-driven transportation systems analysis

Dr. Yongxiang Zhang

E-Mail Website
Guest Editor

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610032, China
Interests: large-scale railway train timetabling problems; real-time railway train dispatching problems; connected automated vehicles; simulation of transportation system

Dr. Yu Yao

E-Mail Website
Guest Editor

College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China
Interests: vehicle routing problem; urban rail network modeling and optimization; algorithm design

Special Issue Information

Dear Colleagues,

We are calling for papers for a Special Issue of the journal of Sustainability focusing on the research topic of Sustainable, Resilient and Smart Mobility. With the rapid development of urbanization and economic growth, we are suffering from many transport related issues (such as traffic congestion, traffic accidents, energy consumption, and exhaust emission problems) especially in metropolitan cities. Emerging technologies, such as shared mobility, self-driving technology, smart reservation, green transportation, artificial intelligence and big data analytics, provide great opportunities to reform traditional transportation planning theories and practices, and promote more sustainable, more resilient, and smarter urban transportation systems. The main objective of this special issue is to collect innovative contributions to the application of emerging transportation techniques to achieve a sustainable, resilient, and smart mobility system, especially in the post-pandemic era. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Roadmap to carbon neutrality with sustainable transportation planning
Transportation resilience planning for a transformative post-pandemic recovery
Traffic congestion mitigation with smart mobility solutions
Autonomous driving and mixed traffic flow modelling
Transportation electrification and zero-emission vehicles
Shared mobility, mobility-as-a-service (MaaS), and micromobility
AI and big data analytics in transport mobility planning and management
Augmented and virtual reality for future mobility planning and management

We look forward to receiving your contributions.

Dr. Qixiu Cheng
Dr. Kai Huang
Dr. Yongxiang Zhang
Dr. Yu Yao
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 and smart cities
green transportation
sustainable mobility
transportation resilience
transportation engineering
transportation management
active transportation
future transportation

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 1622 KiB

Open AccessArticle

Modeling Impacts of Implementation Policies of Tradable Credit Schemes on Traffic Congestion in the Context of Traveler’s Cognitive Illusion

by Fei Han, Jian Wang, Lingli Huang, Yan Li and Liu He

Sustainability 2023, 15(15), 11643; https://doi.org/10.3390/su151511643 - 27 Jul 2023

Viewed by 819

Abstract

A tradable credit scheme (TCS) is a novel traffic demand management (TDM) measure that can effectively mitigate traffic congestion in a revenue-neutral way. Under a given TCS, the cognitive illusion (CI) would occur when travelers instinctively use a specious thinking logic to estimate travel cost. The traveler’s CI would significantly influence his/her route choice behaviors, and thus the regulation effect of TCS on mitigating traffic congestion. To reveal the impacts of implementation policies of TCS on managing network mobility in the context of the traveler’s CI, this study investigated the traffic equilibrium assignment model with consideration of the traveler’s CI and the specific implementation policies of TCS. By incorporating the two types of factors into the generalized path travel cost (GPTC), the coupled user equilibrium (UE) and market equilibrium (ME) conditions are established to describe the equilibrium state of the traffic network under a given TCS. As the implementation policies of TCS are factored in the GPTC, different types of initial credit distribution scheme (ICDS) and the transaction costs (TC) of trading credits can be analyzed within the unified model framework. The coupled UE and ME conditions are then reformulated as an equivalent variational inequality (VI) model, and the sufficient conditions for the uniqueness of UE link flows and ME credit price are also provided. The system optimal (SO) TCS design problem is further investigated to achieve the minimum total travel time (TTT) of the transportation network, and two analytical methods are proposed to obtain the SO TCS in the context of the traveler’s CI. Numerical experiments are presented to verify the proposed model and methods. The results show that the presence of the traveler’s CI has an effect of lowering the ME credit price, and ICDS and TC have a complex network-wide influence on the ME credit price and UE link flows, which depends on the specific values of the relevant parameters. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

27 pages, 4646 KiB

Open AccessArticle

Carbon Footprint Analysis of the Freight Transport Sector Using a Multi-Region Input–Output Model (MRIO) from 2000 to 2014: Evidence from Industrial Countries

by Kadhim Abbood and Ferenc Meszaros

Sustainability 2023, 15(10), 7787; https://doi.org/10.3390/su15107787 - 9 May 2023

Cited by 1 | Viewed by 1832

Abstract

Freight transportation performs a critical role in the supply networks of the global economy and is heavily influenced by the activities of the industrial and manufacturing sectors, contributing significantly to their global carbon footprint (CFP). This research evaluates the lifecycle-based CFP emissions of freight transport activities in seven selected countries (China, Japan, the United States, Canada, Brazil, Great Britain, and Germany) over fifteen years, considering international trade linkages with the rest of the world. In the literature, most researchers have investigated the CFP of the transportation sector in general or analyzed the CFP of two or three countries, such as the USA and China. However, this research is novel in that it examines the CFP of the freight transport sectors of the seven biggest industrial countries. In addition, a positive relationship was found between the CFP and the gross domestic product (GDP), population, level of urbanization, and area of these countries. Therefore, this study investigates the relationship between global CFP, GDP, population, level of urbanization, and country area. A total of 15 stochastic model-based multi-regional input–output lifecycle assessments were built for each country, comprising 35 key industries. Statistical modeling tools were used to assess carbon emissions. The results show that China is the largest contributor to the freight-related CFP, while the U.S. is the second largest. The manufacture of coke and refined petroleum products represents the dominant sector. In contrast, warehousing and support activities have the most significant contributions in Germany and Great Britain. Land transport and transport via pipelines contribute the most to Canada’s CFP. The results of the regression analysis show that there is a positive relationship between the investigated variables. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

19 pages, 4945 KiB

Open AccessArticle

Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach

by Fayez Alanazi

Sustainability 2023, 15(4), 3158; https://doi.org/10.3390/su15043158 - 9 Feb 2023

Cited by 13 | Viewed by 5595

Abstract

Smart mobility systems offers solutions for traffic congestion, transport management, emergency, and road safety. However, the success of smart mobility lies in the availability of intelligent transportation infrastructure. This paper studied smart mobility systems in three Asia-Pacific countries (South Korea, Singapore, and Japan) to highlight the major strategies leading their successful journey to become smart cities for aspiring countries, such as the Kingdom of Saudi Arabia (KSA), to emulate. A robust framework for evaluating smart mobility systems in the three countries and Saudi Arabia was developed based on the indicators derived from the smart mobility ecosystem and three major types of transport services (private, public, and emergency). Sixty indicators of smart mobility systems were identified through a rigorous search of the literature and other secondary sources. Robots, drones, IoT, 5G, hyperloop tunnels, and self-driving technologies formed part of the indicators in those countries. The study reveals that the three Asia-Pacific countries are moving head-to-head in terms of smart mobility development. Saudi Arabia can join these smarter countries through inclusive development, standardization, and policy-driven strategies with clear commitments to public, private, and research collaborations in the development of its smart mobility ecosystem. Moreover, cybersecurity must be taken seriously because most of the smart mobility systems use wireless and IoT technologies, which may be vulnerable to hacking, and thus impact system safety. In addition, the smart mobility system should include data analytics, machine learning, and artificial intelligence in developing and monitoring the evaluation in terms of user experience and future adaptability. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

16 pages, 764 KiB

Open AccessArticle

Moving toward a More Sustainable Autonomous Mobility, Case of Heterogeneity in Preferences

by Iman Farzin, Mohammadhossein Abbasi, Elżbieta Macioszek, Amir Reza Mamdoohi and Francesco Ciari

Sustainability 2023, 15(1), 460; https://doi.org/10.3390/su15010460 - 27 Dec 2022

Cited by 5 | Viewed by 1508

Abstract

Autonomous vehicles (AVs) have a number of potential advantages, although some research indicates that this technology may increase dependence on private cars. An alternative approach to bringing such technology to market is through autonomous taxis (ATs) and buses, which can assist in making transportation more sustainable. This paper aims at examining the role of attitudinal, travel-related, and individual factors in preferences for a modal shift from conventional cars toward ATs and exclusive-lane autonomous buses (ELABs), exploring the existence of heterogeneity and its possible sources. The proposed mixed logit model with a decomposition of random coefficients uses 1251 valid responses from a stated preference survey distributed in Tehran, in 2019. Results show that there is significant taste variation among individuals with respect to ATs’ travel costs, ELABs’ travel times, and walking distances to ELAB stations. Furthermore, exploring the sources of heterogeneity indicates that women are more sensitive to ATs’ travel costs and walking distances to ELAB stations while they are less sensitive to ELABs’ travel times. Moreover, travel time in discretionary activities reduces the utility of ELABs more than it does in mandatory activities. Transportation authorities can use these findings to establish more effective policies for the successful implementation of AVs. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

16 pages, 1545 KiB

Open AccessArticle

Optimization of Resource Allocation in Automated Container Terminals

by Xiaoju Zhang, Huijuan Li and Meng Wu

Sustainability 2022, 14(24), 16869; https://doi.org/10.3390/su142416869 - 15 Dec 2022

Cited by 2 | Viewed by 1883

Abstract

Automated container terminals have been constructed to reduce emissions and labor cost. Resource allocation problems in automated container terminals have a critical effect on handling efficiency and cost. This paper addresses this problem with quay crane (QC) double cycling in automated container terminals. An optimization model is developed to obtain an optimal resource allocation schedule considering the operation cost, and the cost objective function proves to have convex behavior with optimal solutions. The performance of the operation system and its asymptotic behavior are derived with respect to different resource allocation schedules by formulating the operation processes. Finally, numerical experiments are conducted to verify the system’s performance and validity of the proposed model, and some insights are given about how to increase the terminal’s efficiency. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

16 pages, 2746 KiB

Open AccessArticle

A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation

by Leina Zhao, Yujia Bai, Sishi Zhang, Yanpeng Wang, Jie Kang and Wenxuan Zhang

Sustainability 2022, 14(24), 16361; https://doi.org/10.3390/su142416361 - 7 Dec 2022

Cited by 1 | Viewed by 1031

Abstract

Short-term traffic flow prediction is the basis of and ensures intelligent traffic control. However, the conventional models cannot make accurate predictions due to the strong nonlinearity and randomness in short-term traffic flow data. To this end, the authors of this paper developed a novel hybrid model based on extreme learning machine (ELM), adaptive kernel density estimation (AKDE), and conditional kernel density estimation (CKDE). Specifically, the ELM model was employed for nonlinear prediction. Then, AKDE was established to estimate the bandwidth of CKDE (i.e., AKDE-CKDE), which predicted the training residuals obtained by ELM. Finally, the predicted results of the two models were superimposed to derive the final prediction of the hybrid model. Two case studies based on measured data were conducted to evaluate the performance of the proposed method. The experimental results indicate that the proposed method can realize a significant improvement in terms of forecasting accuracy in comparison with the other concerned models. For instance, it performed better than the single ELM model, with an improvement in the evaluation criterion of a mean relative percentage error of 7.46%. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Figure 1

17 pages, 3265 KiB

Open AccessArticle

A Heuristic Algorithm Based on Travel Demand for Transit Network Design

by Yuan Liu, Heshan Zhang, Tao Xu and Yaping Chen

Sustainability 2022, 14(17), 11097; https://doi.org/10.3390/su141711097 - 5 Sep 2022

Cited by 2 | Viewed by 1481

Abstract

This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for the problem of transit network design to determine the route structure and frequencies simultaneously. The problem is focused on reducing the total travel time and the number of transfers. A heuristic algorithm is developed to solve this problem. In the proposed algorithm, the initial routes are generated according to a changing demand matrix, which can reflect the real-time demand with transfers and ensure that the direction of route generation maximizes the percentage of direct service. A regulating method for a sequence of stops is used during route generation to guarantee the shortest trip time for a formed route. Vehicles are allocated to each route according to the flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process of frequencies based on vehicle difference can ensure that the solution meets the constraints. Two scale networks are used to illustrate the performances of the proposed method. Results show that route structure and frequencies can be optimized simultaneously through the proposed method. Different scenarios are created to test the algorithm properties via various parameter values. The test result indicates that the upper bound is a key parameter to balance the proportion of direct service and average in-vehicle travel time (AIVTT), and the increased number of planning routes can improve the proportion of direct service. Full article

(This article belongs to the Special Issue Sustainable, Resilient and Smart Mobility)

► Show Figures

Journal Menu

Journal Browser

Sustainable, Resilient and Smart Mobility

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI