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Sustainability
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Sustainable Transportation and Data Science Application
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Sustainable Transportation and Data Science Application

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

Deadline for manuscript submissions: 10 July 2024 | Viewed by 2041

Special Issue Editors

Dr. Ying Chen

E-Mail Website
Guest Editor
Civil and Environmental Engineering Department, Northwestern University, Evanston, IL 60208, USA
Interests: smart cities and data science; AI and machine learning in transportation; travelers’ behavior analysis; modeling and solution approaches for logistics and complex systems; agent-based modeling and visualization
Dr. Pablo Durango-Cohen

E-Mail Website
Guest Editor
Civil and Environmental Engineering Department, Northwestern University, Evanston, IL 60208, USA
Interests: developing and analyzing optimization and econometric models to support monitoring; management; and operation of transportation infrastructure systems
Dr. Sikai Chen

E-Mail Website
Guest Editor
Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: autonomous vehicle; artificial intelligence; reinforcement learning; econometrics and statistics; highway safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science, the future of artificial intelligence, is transforming the world, as more and more companies utilize data to drive their business growth and success. Its applications are prevalent in every aspect of our lives, including transportation, e-commerce, healthcare, and more. With the emergence of advanced data science technologies, new applications are improving the efficiency of data-related processes.

According to Energy Technology Perspectives report (2020), transportation accounts for around one-fifth of all CO2 emissions on Earth, with three-quarters of those emissions originating from road transportation. Reducing emissions in transportation and creating a sustainable world have become challenging tasks for governments, enterprises, and global organizations. The concept of green transportation is embraced by both the public and private sectors, and its practices include carpooling, carsharing, biking, and autonomous vehicles. These new applications are integrating quickly with the current transportation systems. However, as new opportunities emerge, inevitable challenges arise. The key to solving these challenges efficiently is data. By adopting advanced analytics and machine learning algorithms to learn and extract knowledge from the data, the gap between building a sustainable transportation system and environmental impact is expected to be reduced and closed eventually.

In response to the rapid development of data science and its influence on transportation, particularly sustainable transportation, this Special Issue invites contributions that address research problems related to shared mobility and autonomous vehicles. Utilizing advanced emerging technologies and algorithms in data science, this Special Issue provides a venue to discuss data science and its utilization in sustainable transportation, serving as a good supplement to the current literature. Topics of interest with a general focus on data science applications in sustainable transportation include, but are not limited, to:

  • Social influence on shared mobility
  • Public and private transportation integration with autonomous vehicles
  • Transportation and urban planning for emerging shared mobility
  • Internet of things (IoT) and intelligent transportation systems (ITS)
  • On-demand mobility services
  • Data-driven predictive maintenance for vehicles
  • Real-time traffic management systems based on data analytics
  • Environmental impact assessment of transportation systems using data science.

Dr. Ying Chen
Dr. Pablo Durango-Cohen
Dr. Sikai Chen
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

  • sustainability
  • data science
  • machine learning
  • artificial intelligence
  • shared mobility
  • bike-sharing
  • e-scooters
  • connected and autonomous vehicle

Published Papers (3 papers)

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Research

17 pages, 10583 KiB  
Article
Uncovering the Spatiotemporal Patterns of Regional and Local Driver Sources in a Freeway Network
by Pu Wang, Bin Wang, Rihong Ke, Hu Yang, Shengnan Li and Jianjun Dai
Sustainability 2024, 16(8), 3344; https://doi.org/10.3390/su16083344 - 16 Apr 2024
Viewed by 263
Abstract
We propose a method to identify the congestion driver sources contributing to the major traffic congestion of a regional (Hunan province) freeway network. The results indicate that the congestion driver sources are mostly observed during heavy traffic periods and mainly distributed in the [...] Read more.
We propose a method to identify the congestion driver sources contributing to the major traffic congestion of a regional (Hunan province) freeway network. The results indicate that the congestion driver sources are mostly observed during heavy traffic periods and mainly distributed in the regions surrounding Changsha (the capital of Hunan province) and the regions adjacent to other provinces and freeway interconnecting hubs. Moreover, we develop a method to analyze the major driver sources of a local freeway section. Using the method, the trips affected by traffic accidents or road maintenance works can be identified well. Our findings and the proposed methods could facilitate the deployment of effective traffic control countermeasures and the development of sustainable regional transportation. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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14 pages, 1760 KiB  
Article
Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs
by Yujie Guo, Ying Chen and Yu Zhang
Sustainability 2024, 16(5), 2065; https://doi.org/10.3390/su16052065 - 01 Mar 2024
Viewed by 529
Abstract
Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve [...] Read more.
Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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15 pages, 8121 KiB  
Article
A Highway On-Ramp Control Approach Integrating Percolation Bottleneck Analysis and Vehicle Source Identification
by Shengnan Li, Hu Yang, Minglun Li, Jianjun Dai and Pu Wang
Sustainability 2023, 15(16), 12608; https://doi.org/10.3390/su151612608 - 20 Aug 2023
Cited by 2 | Viewed by 810
Abstract
Identifying the bottleneck segments and developing targeted traffic control strategies can facilitate the mitigation of highway traffic congestion. In this study, we proposed a new method for identifying the bottleneck segment in a large highway network based on the percolation theory. A targeted [...] Read more.
Identifying the bottleneck segments and developing targeted traffic control strategies can facilitate the mitigation of highway traffic congestion. In this study, we proposed a new method for identifying the bottleneck segment in a large highway network based on the percolation theory. A targeted on-ramp control approach was further developed by identifying the major vehicle sources of the bottleneck segment. We found that the identified bottleneck segment played a crucial role in maintaining the functional connectivity of the highway network in terms of meeting the required level of service. The targeted on-ramp control approach can more effectively enhance the service level of the highway network. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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