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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: closed (10 July 2024) | Viewed by 9506

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

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Published Papers (6 papers)

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Research

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18 pages, 2578 KiB  
Article
Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM
by Jianqi Li, Wenbao Zeng, Weiqi Liu and Rongjun Cheng
Sustainability 2024, 16(13), 5725; https://doi.org/10.3390/su16135725 - 4 Jul 2024
Viewed by 816
Abstract
High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this [...] Read more.
High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this study designs and develops a novel spatiotemporal prediction model with multidimensional inputs (MSACL) by embedding a self-attention memory (SAM) module into a convolutional long short-term memory neural network (ConvLSTM). The SAM module can extract features with long-range spatiotemporal dependencies. The experimental data are derived from the Chengdu City online car-hailing trajectory data set and the external factors data set. Comparative experiments demonstrate that the proposed model has higher accuracy. The proposed model outperforms the Sa-ConvLSTM model and has the highest prediction accuracy, shows a reduction in the mean absolute error (MAE) by 1.72, a reduction in the mean squared error (MSE) by 0.43, and an increase in the R-squared (R2) by 4%. In addition, ablation experiments illustrate the effectiveness of each component, where the external factor inputs have the least impact on the model accuracy, but the removal of the SAM module results in the most significant decrease in model accuracy. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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20 pages, 2812 KiB  
Article
Forecasting Moped Scooter-Sharing Travel Demand Using a Machine Learning Approach
by Tulio Silveira-Santos, Thais Rangel, Juan Gomez and Jose Manuel Vassallo
Sustainability 2024, 16(13), 5305; https://doi.org/10.3390/su16135305 - 21 Jun 2024
Viewed by 1196
Abstract
The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing [...] Read more.
The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing services in Madrid, Spain, based on origin–destination trip data. A comprehensive dataset was utilized, encompassing sociodemographic characteristics, travel attraction centers, transportation network attributes, policy-related variables, and distance impedance. Two supervised machine learning models, linear regression and random forest, were employed to predict travel demand patterns. The results revealed the effectiveness of ensemble learning methods, particularly the random forest model, in accurately predicting travel demand and capturing complex feature relationships. The feature scores emphasize the importance of neighborhood characteristics such as tourist accommodations, public administration centers, regulated parking, and commercial centers, along with the critical role of trip distance. Users’ preference for short-distance trips within the city highlights the appeal of these services for urban mobility. The findings have implications for urban planning and transportation decision-making to better accommodate travel patterns, improve the overall transportation system, and inform policy recommendations to enhance intermodal connectivity and sustainable urban mobility. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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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 911
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 - 1 Mar 2024
Cited by 1 | Viewed by 1264
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 3 | Viewed by 1279
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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Review

Jump to: Research

34 pages, 3117 KiB  
Review
The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends
by Junkai Zhang, Jun Wang, Haoyu Zang, Ning Ma, Martin Skitmore, Ziyi Qu, Greg Skulmoski and Jianli Chen
Sustainability 2024, 16(14), 5879; https://doi.org/10.3390/su16145879 - 10 Jul 2024
Cited by 1 | Viewed by 3224
Abstract
Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This paper conducts a [...] Read more.
Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This paper conducts a systematic review of the intelligent transportation literature using a scientometric analysis, aiming to summarize what is already known, identify current research trends, evaluate academic impacts, and suggest future research directions. The study provides a detailed review by analyzing 113 journal articles from the Web of Science (WoS) database. It examines the growth of publications over time, explores the collaboration patterns of key contributors, such as researchers, countries, and organizations, and employs techniques such as co-authorship analysis and keyword co-occurrence analysis to delve into the publication clusters and identify emerging research topics. Nine emerging sub-topics are identified and qualitatively discussed. The outcomes include recognizing pioneering researchers in intelligent transportation for potential collaboration opportunities, identifying reliable sources of information for publishing new work, and aiding researchers in selecting the best solutions for specific problems. These findings help researchers better understand the application of ML and DL in the intelligent transportation literature and guide research policymakers and editorial boards in selecting promising research topics for further research and development. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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