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Smart and Sustainable Multimodal Transportation

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 14314

Special Issue Editors


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Guest Editor
School of Architecture, Harbin Institute of Technology, Shenzhen, 518055, China
Interests: intelligent transportation management and control; emergency management; complex system analysis, optimization, and control; reinforcement learning for combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: innovation and technology management; technology and public health management; resilience and continuity; supply chain management and logistics; sustainable shipping management; sustainable cities; maritime strategy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Interests: multimodal transportation system; green and smart logistics; system modeling and optimization; data analytics in operations management

Special Issue Information

Dear Colleagues,

Multimodal transportation provides an efficient, flexible, economic, and resilient platform for both freight and passenger transportation. It could support sustainability by making efficient use of resources, improving transport structure, and enabling environmentally friendly transport choices. With the continuous growth of transport/travel demands and the need for sustainable development, multimodal transportation has attracted increasing attention from academic, industrial, and political communities. Tremendous research has been dedicated to establish, manage, and optimize the multimodal transportation system in the past few decades with rich outcomes in both theory and practices.

Recently, the emergence of novel technologies in big data, artificial intelligence, automation, and Internet of Things (IoT), as well as the advances of green technologies (e.g., electric vehicle), have created many promising opportunities for multimodal transportation from strategic, tactical, and operational perspectives. Leveraging these emerging technologies can lead to significant savings in operational costs and carbon emission, as well as boosts in transport efficiency. Nevertheless, these technologies also generate a new set of challenges and research questions from various dimensions of multimodal transportation. Substantial efforts are still needed to develop analytical models, intelligent approaches, and novel applications to further improve the practices of multimodal transportation.

The purposes of this Special Issue are to publish state-of-the-art research on technologies, operations management, policies, and applications for sustainable multimodal transportation. Topics include (but are not limited to) the following:

  • Data analytics for multimodal transportation
  • Network analysis, design, and optimization
  • Policies and practices for sustainable multimodal transportation
  • Risk analysis of multimodal transportation system
  • Operations management in multimodal transportation
  • Modeling and optimization for sustainability
  • Intelligent transportation systems and applications
  • Mobility as a service

Prof. Dr. Gangyan Xu
Prof. Dr. Kum Fai Yuen
Prof. Dr. Xuan Qiu
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

  • Multimodal transport
  • Intelligent transportation systems
  • Green logistics
  • Decision analytics
  • Mobility as a service

Published Papers (5 papers)

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Research

22 pages, 4254 KiB  
Article
IoT-Enabled Sustainable and Cost-Efficient Returnable Transport Management Strategies in Multimodal Transport Systems
by Yanqi Zhang, Xiaofei Kou, Haibin Liu, Shiqing Zhang and Liangliang Qie
Sustainability 2022, 14(18), 11668; https://doi.org/10.3390/su141811668 - 16 Sep 2022
Cited by 3 | Viewed by 1292
Abstract
Returnable transport items (RTIs) are widely used in multimodal transport systems. However, due to the lack of effective tracking methods, RTIs management efficiency is low and RTIs are easily lost, which directly and indirectly causes economic losses to enterprises. Internet of Things (IoT) [...] Read more.
Returnable transport items (RTIs) are widely used in multimodal transport systems. However, due to the lack of effective tracking methods, RTIs management efficiency is low and RTIs are easily lost, which directly and indirectly causes economic losses to enterprises. Internet of Things (IoT) technology is proved to be effective in realizing real-time tracking and tracing of various objects in diverse fields. However, an IoT-enabled RTIs management system in a multimodal transport system has not been widely accepted due to a lack of an effective cost decision model. To address these problems, this research first presents three typical schemes of RTIs management. through extensive field studies on collaborative logistics service providers in multimodal transport systems. Then, the cost–benefit analyses of these three schemes are conducted while the decision models on whether to adopt IoT technologies are built. Finally, based on the decision models, the main factors affecting the application of IoT-RTIs management systems are studied by numerical analysis, based on which several managerial implications are presented. These results can serve as a theoretical basis for enterprises interested in finding out whether IoT technology should be used in RTIs management. Full article
(This article belongs to the Special Issue Smart and Sustainable Multimodal Transportation)
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16 pages, 15502 KiB  
Article
Spatial Models and Neural Network for Identifying Sustainable Transportation Projects with Study Case in Querétaro, an Intermediate Mexican City
by Antonio A. Barreda-Luna, Juvenal Rodríguez-Reséndiz, Omar Rodríguez-Abreo and José Manuel Álvarez-Alvarado
Sustainability 2022, 14(13), 7796; https://doi.org/10.3390/su14137796 - 27 Jun 2022
Cited by 2 | Viewed by 1719
Abstract
The construction of urban and transport indicators aims for a better diagnosis that enables technical and precise decision-making for the public administration or private investment. Therefore, it is common to make comparisons and observe which has better diagnosis results in a diversity of [...] Read more.
The construction of urban and transport indicators aims for a better diagnosis that enables technical and precise decision-making for the public administration or private investment. Therefore, it is common to make comparisons and observe which has better diagnosis results in a diversity of indexes and models. The present study made a comparative analysis of spatial models using artificial intelligence to estimate transport demand. To achieve this goal, the audit field was recollected in specific urban corridors to measure the indicators. A study case in Querétaro, an emergent city in the Mexican region known as El Bajío, is conducted. Two similar urban avenues in width and length and close to each other were selected to apply a group of spatial models, evaluating the avenues by segments and predicting the public transport demand. The resulting database was analyzed using Artificial Neural Networks. It displays specific indicators that have around 80% of correlations. The results facilitate the localization of the avenue segments with the most volume of activity, supporting interventions in urban renewal and sustainable transportation projects. Full article
(This article belongs to the Special Issue Smart and Sustainable Multimodal Transportation)
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17 pages, 3478 KiB  
Article
Contract Design of Logistics Service Supply Chain Based on Smart Transformation
by Hao Liu, Haodong Chen, Hengyi Zhang, Haibin Liu, Xingwang Yu and Shiqing Zhang
Sustainability 2022, 14(10), 6261; https://doi.org/10.3390/su14106261 - 20 May 2022
Cited by 3 | Viewed by 1695
Abstract
A logistics service integrator (LSI) usually requires a logistics service provider (LSP) to carry out smart transformation in order to improve the level of logistics service. However, LSP’s smart transformation faces uncertainty in terms of investments and income, which seriously hinders LSP’s enthusiasm [...] Read more.
A logistics service integrator (LSI) usually requires a logistics service provider (LSP) to carry out smart transformation in order to improve the level of logistics service. However, LSP’s smart transformation faces uncertainty in terms of investments and income, which seriously hinders LSP’s enthusiasm for logistics service innovation. In this paper, we construct a logistics service supply chain (LSSC) consisting of an LSI and an LSP to explore the incentive mechanism for LSPs to undergo smart transformation. As a benchmark for comparison, we first obtain the equilibrium results under centralized decision making and wholesale price (WP) contracts. Then, cost-sharing (CS), revenue-sharing (RS), and cost sharing–revenue sharing (CS-RS) hybrid contracts are proposed. It is found that when the CS coefficient is in a certain interval, the CS contract can increase the profit of LSI and the smart level of logistics service, but it will decrease the profit of LSP. With the exception that the wholesale price of logistics services will decrease, the equilibrium results under the RS contract and WP contract remain consistent. Only the CS-RS hybrid contract can achieve the perfect coordination of LSSC. In addition, by conducting numerical analysis, we find that the enhancement of the smart effect can encourage LSP to improve the smart level and increase the overall revenue of LSSC. To the best of our knowledge, this paper is the first study to explore the incentive mechanism between LSI and LSP in the context of logistics service smart transformation. Our findings guide the LSI in implementing an effective contract. Full article
(This article belongs to the Special Issue Smart and Sustainable Multimodal Transportation)
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15 pages, 1054 KiB  
Article
An Investigation of Multimodal Transport for Last Mile Delivery in Rural Areas
by Xiaofei Kou, Yanqi Zhang, Die Long, Xuanyu Liu and Liangliang Qie
Sustainability 2022, 14(3), 1291; https://doi.org/10.3390/su14031291 - 24 Jan 2022
Cited by 10 | Viewed by 4387
Abstract
High distribution costs constitute one of the major obstacles to the sustainable development of rural logistics. In order to effectively reduce the distribution costs of last mile delivery in rural areas, based on three typical transport modes (local logistics providers, public transport, and [...] Read more.
High distribution costs constitute one of the major obstacles to the sustainable development of rural logistics. In order to effectively reduce the distribution costs of last mile delivery in rural areas, based on three typical transport modes (local logistics providers, public transport, and crowdsourcing logistics), this study first proposes a multimodal transport design for last mile delivery in rural areas. Then, a cost–benefit model for multimodal transport is proposed which uses genetic algorithms (GA) to solve the logistical problems faced. Finally, Shapley value is used to fairly allocate profits and represent the marginal contribution of each mode in multimodal transport. The numerical results show that multimodal transport can effectively reduce the distribution costs of last mile delivery in rural areas. When the order demand of each node tends to be stable, the marginal contribution of crowdsourcing logistics is often greater than that of the other two distribution modes. The marginal contribution of public transport is highest only when the number of orders per node is very small. Full article
(This article belongs to the Special Issue Smart and Sustainable Multimodal Transportation)
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20 pages, 1117 KiB  
Article
Cost and Scenario Analysis of Intermodal Transportation Routes from Korea to the USA: After the Panama Canal Expansion
by Junseung Kim, Kyungku Kim, Kum Fai Yuen and Keun-Sik Park
Sustainability 2020, 12(16), 6341; https://doi.org/10.3390/su12166341 - 06 Aug 2020
Cited by 2 | Viewed by 3255
Abstract
This study is aimed at suggesting the most economical transportation route by comparing seven different Korea–US intermodal transportation routes for automotive parts exported to Southeastern USA. To keep up with the global competition of parts makers, which are expanding their markets based on [...] Read more.
This study is aimed at suggesting the most economical transportation route by comparing seven different Korea–US intermodal transportation routes for automotive parts exported to Southeastern USA. To keep up with the global competition of parts makers, which are expanding their markets based on advanced technology and enormous capital, Korean automotive parts makers also need to actively advance their markets overseas. From this point of view, selecting an efficient transport route and transportation modes for overseas export is essential. To this end, the most efficient transportation route from the perspective of total logistics cost was selected by adapting the inventory-theoretic model, using information such as the logistics status of a specific company and the logistics freight rates and transit time for the third quarter of 2019. Thus, the scenario analysis was conducted assuming that variables—namely transportation cost per unit, commodity value, inventory cost and additional conditions such as terminal free time—were modified. Through this study, the optimal transportation route was selected by fully considering and predicting the total logistics cost component and the variability of the major factors. Full article
(This article belongs to the Special Issue Smart and Sustainable Multimodal Transportation)
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