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Modeling Connected and Automated Vehicles (CAVs) for Sustainable and Intelligent Transportation Systems

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

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 5444

Special Issue Editors

Dr. Jun Liu

E-Mail Website
Guest Editor
Department of Civil, Construction and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: connected and automated vehicles; shared mobility and micro-mobility; urban transportation planning; transportation energy and environment
Prof. Dr. Steven Jones

E-Mail Website
Guest Editor
Deputy Director of Alabama Transportation Institute, James R. Cudworth Professor of Department of Civil, Construction and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: transportation safety; connected and automated vehicles; transportation environment; transportation resilience; transportation in developing countries
Dr. Wei Zhao

E-Mail Website
Guest Editor
University of Wisconsin-Madison, Madison, WI 53705, USA
Interests: connected and automated vehicles; computer vision based on automated vehicle design; mobility with IoT; infrastructure develop for autonomous vehicles

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) are quickly becoming a reality. CAVs are expected to be capable of communicating with other road users (e.g., other vehicles, pedestrians, and bicyclists) and infrastructures (e.g., traffic signals) and will eventually be operated on the road without any intervention or control by human beings. CAVs are anticipated to transform the surface transportation system into a safer, more efficient, and cleaner environment. Before the CAVs and infrastructures are ready for their widespread implementation, the transportation community must plan for their ultimate deployment in a logical and incremental manner. To support the development of CAV implementation plans, researchers play a key role in examining or anticipating the potential impacts of CAVs on transportation systems and the environment.

The impacts of CAVs on the management or operations of transportation systems remain underdiscussed. Intelligent transportation systems (ITS), replying on information and communication technologies, have played an important role in transportation system management and operations (TSMO). ITS applications are expected to advance transportation safety, mobility, and environmental sustainability through electronic and information technology applications. CAVs will likely pose both opportunities and challenges to the ITS. Within the environment of connected vehicles and infrastructures, the electric sensors that support vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-others (V2X) communications will generate high volume, high velocity, and high variety information assets, collectively referred to as Big Data, which will likely boost the current ITS applications (e.g., Traffic Incident Management) and lead to new applications to further assist the management of transportation systems. However, the challenges that CAV-generated Big Data will bring to transportation agencies include data storage, security, sharing, transfer, analysis, and visualization. Research is needed to offer insights into how knowledge and practice of data handling in ITS and the future data environments with CAVs are connected.

This Special Issue will focus on the impacts of CAVs on the management or operation of transportation systems. Specifically, this Special Issue invites researchers and practitioners to discuss, envision, or define the roles of CAVs in the ITS and to examine the networkwide impacts of CAVs on the mobility, safety, and environmental sustainability of the surface transportation systems.  Potential topics include, but are not limited to:

  • Development and applications of CAV technologies in ITS
  • CAV/ITS-related Big Data and machine learning methods and applications
  • CAV cybersecurity and privacy
  • Simulations of CAV deployment
  • Planning and operation of CAVs
  • Shared mobility and micro-mobility supported by CAV/ITS technologies
  • Safety, mobility, and environmental implications of CAVs in ITS
  • Transportation equity research related to CAV/ITS applications
  • Transportation resilience research related to CAV/ITS applications
  • Relevant policies and regulations related to CAV/ITS technologies
  • Augmented application scenarios with CAV/ITS technologies

Dr. Jun Liu
Prof. Dr. Steven Jones
Dr. Wei Zhao
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

  • Connected and Automated Vehicles
  • Intelligent Transportation Systems
  • Big Data
  • Transportation Safety
  • Mobility and Sustainability

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Published Papers (2 papers)

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Research

29 pages, 4042 KiB  
Article
Formal Modeling of Responsive Traffic Signaling System Using Graph Theory and VDM-SL
by Afifa Nawaz, Nazir Ahmad Zafar and Eman H. Alkhammash
Sustainability 2021, 13(21), 11772; https://doi.org/10.3390/su132111772 - 25 Oct 2021
Cited by 2 | Viewed by 2308
Abstract
Internet of things (IoT) is playing a major role in smart cities to make a digital environment. Traffic congestion is a serious road issue because of an increasing number of vehicles in urban areas. Some crucial traffic problems include accidents and traffic jams [...] Read more.
Internet of things (IoT) is playing a major role in smart cities to make a digital environment. Traffic congestion is a serious road issue because of an increasing number of vehicles in urban areas. Some crucial traffic problems include accidents and traffic jams that cause waste of fuel, health diseases, and a waste of time. Present traffic signaling systems are not efficient in resolving congestion problems because of the lack of traffic signals. Nowadays, traffic signaling systems are modeled with fixed time intervals in which no proper mechanism for emergency vehicles is available. Such traffic mechanisms failed to deal with traffic problems effectively. The major objective is to establish a robust traffic monitoring and signaling system that improves signal efficiency by providing a responsive scheme; appropriate routes; a mechanism for emergency vehicles and pedestrians in real-time using Vienna Development Method Specification Language (VDM-SL) formal method and graph theory. A formal model is constructed by considering objects, such as wireless sensors and cameras that are used for collecting information. Graph theory is used to represent the network and find appropriate routes. Unified Modeling Language is used to design the system requirements. The graph-based framework is converted into a formal model by using VDM-SL. The model has been validated and analyzed using many facilities available in the VDM-SL toolbox. Full article
(This article belongs to the Special Issue Modeling Connected and Automated Vehicles (CAVs) for Sustainable and Intelligent Transportation Systems)
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15 pages, 3988 KiB  
Article
Terrain Analytics for Precision Agriculture with Automated Vehicle Sensors and Data Fusion
by Wei Zhao, Tianxin Li, Bozhao Qi, Qifan Nie and Troy Runge
Sustainability 2021, 13(5), 2905; https://doi.org/10.3390/su13052905 - 8 Mar 2021
Cited by 3 | Viewed by 2311
Abstract
Precision agriculture aims to use minimal inputs to generate maximal yields by managing the plant and its environment at a discrete instead of a field level. This new farming methodology requires localized field data including topological terrain attributes, which influence irrigation, field moisture, [...] Read more.
Precision agriculture aims to use minimal inputs to generate maximal yields by managing the plant and its environment at a discrete instead of a field level. This new farming methodology requires localized field data including topological terrain attributes, which influence irrigation, field moisture, nutrient runoff, soil compaction, and traction and stability for traversing agriculture machines. Existing research studies have used different sensors, such as distance sensors and cameras, to collect topological information, which may be constrained by energy cost, performance, price, etc. This study proposed a low-cost method to perform farmland topological analytics using sensor implementation and data processing. Inertial measurement unit sensors, which are widely used in automated vehicle study, and a camera are set up on a robot vehicle. Then experiments are conducted under indoor simulated environments that include five common topographies that would be encountered on farms, combined with validation experiments in a real-world field. A data fusion approach was developed and implemented to track robot vehicle movements, monitor the surrounding environment, and finally recognize the topography type in real time. The resulting method was able to clearly recognize topography changes. This low-cost and easy-mount method will be able to augment and calibrate existing mapping algorithms with multidimensional information. Practically, it can also achieve immediate improvement for the operation and path planning of large agricultural machines. Full article
(This article belongs to the Special Issue Modeling Connected and Automated Vehicles (CAVs) for Sustainable and Intelligent Transportation Systems)
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