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Sustainability
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Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data
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Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 16054

Special Issue Editors

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
Prof. Dr. Samuel Labi

E-Mail Website
Guest Editor
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47906, USA
Interests: transportation performance modeling; life-cycle cost analysis; connected and automated
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ali Karimoddini

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA
Interests: the interfaces between control, communication, computer science, and life science; large-scale complex systems; cooperative and distributed control of multiagent systems; smart transportation systems; cybersecurity
Prof. Dr. Hualiang (Harry) Teng

E-Mail Website
Guest Editor
Civil and Environmental Engineering and Construction, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
Interests: rail engineering and management; intelligent transportation systems; highway safety; air quality analysis

Special Issue Information

Dear Colleagues,

The concept of smart cities has become increasingly relevant in the past few decades. There exists a plethora of current research and application efforts in the smart cities domain, and the concept continues to attract a great deal of attention from researchers working in areas of emerging technology, including intelligent transportation systems (ITS), artificial intelligence (AI), and big data. For example, advancements in AI and machine learning have greatly enhanced autonomous vehicle (AV) capabilities for safe and efficient traffic operations. Similarly, alternative fuels for vehicles which include electric, natural gas, and hydrogen yield benefits including reduced long-term costs of vehicle operations, enhanced energy efficiency and security, improved air quality, and environmental sustainability. Further, ongoing improvements in vehicle connectivity via advanced communication technologies and cyber infrastructure systems are helping to collect, transmit, analyze, and store big data efficiently.

Therefore, the ongoing research and technological advancements in ITS, AI, and big data continue to provide unparalleled opportunities for realizing smart city concepts. Researchers are also identifying and addressing the interdependencies and synergies between different technologies. For example, electric propulsion is expected to facilitate AV operations in various ways. In addition, regarding AI and connectivity, AVs are being made to communicate not only with each other but also with infrastructure and pedestrians for purposes of operational enhancements. The pursuit of these synergies is leading to the creation of large-scale complex and interconnected systems that must be managed and operated efficiently and therefore is motivating the drive for efficient data sharing and cybersecurity.

In view of the complex and interdisciplinary nature of the topic, this Special Issue welcomes submissions of innovative research that investigate the role and inter-relationships among transportation systems, AI, and big data toward the goal of realization of smart cities.

The topics of interests include but are not limited to:

  • Intelligent transportation systems
  • Artificial intelligence
  • Smart devices and sensors
  • Connected and autonomous vehicles
  • Adaptive traffic signals and platoon control
  • V2V and V2I deployment
  • Big data
  • Electric charging infrastructure planning and design
  • Life cycle cost analysis of smart infrastructure
  • Traveler information systems
  • Highway operations
  • Traffic forecasting and management
  • Intelligent decision support
  • Connectivity of different transportation systems

Dr. Sikai Chen
Prof. Dr. Samuel Labi
Prof. Dr. Ali Karimoddini
Prof. Dr. Hualiang (Harry) Teng
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 cities
  • connected and autonomous transportation
  • artificial intelligence
  • large-scale complex systems
  • multiagent systems
  • big data
  • smart transportation
  • electric vehicles
  • advanced driver assistance systems
  • connectivity of transportation systems

Published Papers (7 papers)

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Research

26 pages, 1837 KiB  
Article
Unveiling the Hidden Effects of Automated Vehicles on “Do No Significant Harm” Components
by Oana Luca, Liliana Andrei, Cristina Iacoboaea and Florian Gaman
Sustainability 2023, 15(14), 11265; https://doi.org/10.3390/su151411265 - 19 Jul 2023
Cited by 2 | Viewed by 2094
Abstract
The deployment of automated vehicles (AVs) has the potential to disrupt and fundamentally transform urban transportation. As their implementation becomes imminent on cities’ streets, it is of great concern that no comprehensive strategies have been formulated to effectively manage and mitigate their potential [...] Read more.
The deployment of automated vehicles (AVs) has the potential to disrupt and fundamentally transform urban transportation. As their implementation becomes imminent on cities’ streets, it is of great concern that no comprehensive strategies have been formulated to effectively manage and mitigate their potential negative impacts, particularly with respect to the components of the do no significant harm (DNSH) framework recently introduced in the EU taxonomy. The methodology employed comprises three steps: (i) An extensive literature review on the impact of AVs on the DNSH components; (ii) exploration of designing a coherent pro-active vision by integrating measures identified in the literature as key elements to mitigate the harm; and (iii) an interdisciplinary focus group (FG) to verify whether the impacts of AVs and potential mitigation measures for Bucharest are similar to those identified by the literature and integrated into the pro-active vision. The results suggest that while there are commonalities, variations exist in focus and perspective, underscoring the necessity of examining the mitigation measures encompassed in the vision through additional focus groups conducted in different cities. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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15 pages, 5502 KiB  
Article
A Methodology to Estimate Functional Vulnerability Using Floating Car Data
by Federico Karagulian, Gaetano Valenti, Carlo Liberto and Matteo Corazza
Sustainability 2023, 15(1), 711; https://doi.org/10.3390/su15010711 - 30 Dec 2022
Cited by 1 | Viewed by 1155
Abstract
In this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation [...] Read more.
In this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation of vulnerability is based on topological data, the proposed methodology is based on spatial-temporal mobility profiles obtained with floating car data (FCD). The algorithm developed for the estimation of vulnerability combines topological properties of the road network with mobility patterns obtained from FCD to evaluate the consequences of failure events on trajectories and their associated travel times. The core operation of the algorithm is based on the computation of all possible travel paths within their assigned geographical zone every time a road link is disrupted. The procedure may prove useful to evaluate wide failure events and to facilitate emergency plans. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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15 pages, 2890 KiB  
Article
Improving Road Safety during Nocturnal Hours by Characterizing Animal Poses Utilizing CNN-Based Analysis of Thermal Images
by Derian Mowen, Yuvaraj Munian and Miltiadis Alamaniotis
Sustainability 2022, 14(19), 12133; https://doi.org/10.3390/su141912133 - 25 Sep 2022
Cited by 5 | Viewed by 1510
Abstract
Animal–vehicle collision is a common danger on highways, especially during nighttime driving. Its likelihood is affected not only by the low visibility during nighttime hours, but also by the unpredictability of animals’ actions when a vehicle is nearby. Extensive research has shown that [...] Read more.
Animal–vehicle collision is a common danger on highways, especially during nighttime driving. Its likelihood is affected not only by the low visibility during nighttime hours, but also by the unpredictability of animals’ actions when a vehicle is nearby. Extensive research has shown that the lack of visibility during nighttime hours can be addressed using thermal imaging. However, to our knowledge, little research has been undertaken on predicting animal action through an animal’s specific poses while a vehicle is moving. This paper proposes a new system that couples the use of a two-dimensional convolutional neural network (2D-CNN) and thermal image input, to determine the risk imposed by an animal in a specific pose to a passing automobile during nighttime hours. The proposed system was tested using a set of thermal images presenting real-life scenarios of animals in specific poses on the roadside and was found to classify animal poses accurately in 82% of cases. Overall, it provides a valuable basis for implementing an automotive tool to minimize animal–vehicle collisions during nighttime hours. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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31 pages, 6266 KiB  
Article
How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method
by Pan Wu, Jinlong Li, Yuzhuang Pian, Xiaochen Li, Zilin Huang, Lunhui Xu, Guilin Li and Ruonan Li
Sustainability 2022, 14(15), 9666; https://doi.org/10.3390/su14159666 - 05 Aug 2022
Cited by 5 | Viewed by 1695
Abstract
Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather [...] Read more.
Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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15 pages, 4772 KiB  
Article
Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System
by Sudhir Kumar Rajput, Jagdish Chandra Patni, Sultan S. Alshamrani, Vaibhav Chaudhari, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot and Ahmed Saeed AlGhamdi
Sustainability 2022, 14(15), 9163; https://doi.org/10.3390/su14159163 - 26 Jul 2022
Cited by 21 | Viewed by 3014
Abstract
Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial [...] Read more.
Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial intelligence has also motivated the implementation of vehicle identification and classification using AI-based techniques, such as machine learning, artificial neural network and deep learning. In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government’s recommendation to implement the automatic vehicle identification and classification for use in the toll management system deployed at toll plazas. For faster processing of the test videos, the frames were saved at a certain interval and then the saved frames were passed through the algorithm. Apart from toll plazas, we also tested the algorithm for vehicle identification and classification on highways and urban areas. Implementing automatic vehicle identification and classification using traditional techniques is a highly proprietary endeavor. Since YOLOv3 is an open-standard-based algorithm, it paves the way to developing sustainable solutions in the area of smart transportation. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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25 pages, 16585 KiB  
Article
Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong
by Fan Wu and Wei Ma
Sustainability 2022, 14(13), 7957; https://doi.org/10.3390/su14137957 - 29 Jun 2022
Cited by 6 | Viewed by 2643
Abstract
Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one [...] Read more.
Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has already taken action to install new on-street parking meters and release real-time parking occupancy information to the public. The data have been released for months, yet, to the best of our knowledge, there has been no study analyzing the data and identifying their unique characteristics for Hong Kong. In view of this, we examined the spatio-temporal patterns of on-street parking in Hong Kong using the data from the new meters. We integrate the t-SNE and k-means methods to simultaneously visualize and cluster the parking occupancy data. We found that the average on-street parking occupancy in Hong Kong is over 80% throughout the day, and three parking patterns are consistently identified by direct data visualization and clustering results. Additionally, the parking patterns in Hong Kong can be explained using land-use factors. Overall, this study can help the government better understand the unique characteristics of on-street parking and develop smart management strategies for Hong Kong. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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20 pages, 5668 KiB  
Article
Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections
by Shuya Zong, Sikai Chen, Majed Alinizzi and Samuel Labi
Sustainability 2022, 14(7), 4034; https://doi.org/10.3390/su14074034 - 29 Mar 2022
Cited by 2 | Viewed by 2137
Abstract
Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in [...] Read more.
Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in using traditional tracking equipment, such prediction can be impaired by occlusion. It has been suggested in recent literature that unmanned aerial vehicles (UAVs) can be deployed to address this issue successfully, given their wide visual field and movement flexibility. This paper presents a methodology that integrates UAVs to track the movement of road users and to assess potential collisions at intersections. The proposed methodology includes an existing deep-learning-based algorithm to identify road users, extract trajectories, and calculate collision risk. The methodology was applied using a case study, and the results show that the methodology can provide beneficial information for the purpose of measuring and analyzing the infrastructure performance. Based on vehicle movements it observes, the UAV can communicate its collision risk to each vehicle so that the vehicle can undertake proactive driving decisions. Finally, the proposed framework can serve as a valuable tool for urban road agencies to develop measures to reduce crash risks. Full article
(This article belongs to the Special Issue Smart Cities—the Role of Transportation, Artificial Intelligence, and Big Data)
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