Special Issue "Advanced Modelling and Control for Cooperative Connected and Automated Mobility"
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electric Vehicles".
Deadline for manuscript submissions: 31 May 2022.
Submit your paper via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies
and select the Journal “Energies” and the Special Issue “Advanced Modelling and Control for Cooperative Connected and Automated Mobility”.
Please contact the guest editor or the journal editor ([email protected]) for any queries.
Special Issue Editors
Interests: cooperative intelligent transportation systems; traffic management and control; within-day traffic flow modelling; models and algorithms for travel demand assignment to congested transportation networks under stationary conditions or with day-to-day or within-day dynamics; smart/sustainable mobility; traveller information systems; discrete choice models and alternative paradigms for travel behaviour analysis and modelling; enhanced paradigms for traveller learning process modelling; sharing mobility; transportation environmental impacts; terminal simulation modelling
Special Issues and Collections in MDPI journals
Interests: transport modeling; energy consumption models; e-mobility
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
In the era of cooperative connected and automated mobility (CCAM), combining the information provided by intelligent vehicles and multiple sources allows more reliable real time traffic management. The field of transportation research is calling for new models, methodologies, and analytical frameworks to better understand complex interactions between transportation networks and urban mobility and to build the smart transportation systems of the future.
In particular, the modelling approaches and the traffic control strategies must be revised in another perspective considering new data sources and vehicle technologies. In case of passengers and freight mobility, CCAM-enabled shared mobility services allow the integration of public transport and Mobility-as-a-Service (MaaS) platforms. Concerning intelligent vehicles (IVs), advanced measurement and control techniques can improve driving safety, riding comfort, travel efficiency, and fuel economy. In particular, we explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events and save energy. Furthermore, intelligent transportation systems have changed from a conventional technology-driven system into a data-driven intelligent transportation system (D2ITS) in which data that are collected from multiple sources will play a key role in ITSs. The models and tools from machine learning and complexity research have shown great potential.
This Special Issue focuses on the development of quantitative modelling and optimization approaches for transportation systems management with the use of advanced sensing, communication, and information technologies.
Potential topics include but are not limited to the following categories:
Modelling and Control
- Dynamic network modelling.
- Simulation and optimization of transportation systems.
- Control and management of transportation systems.
- Traffic flow modelling.
Planning and Operation
- Public transport planning and operation.
- Transportation planning and traffic engineering.
- Multimodal transportation and terminals.
- Sensors and automatic data collection methods.
- Port and maritime operations.
- City logistics.
Innovative Solutions
- ITS for multimodal transportation systems.
- Advanced mobility data collection.
- Cooperative, connected and automated vehicle system applications.
- Advanced vehicular communication technologies.
- Shared mobility.
- Urban mobility innovations.
- Applications of IoT to transportation.
- Smart cities and smart mobility.
Data-Driven Solutions
- Big data in transportation.
- Vision-, multisource-, and learning-driven ITSs.
- Multisensor data fusion techniques.
- AI-enabled travel behavior analysis and prediction in a multimodal environment.
- Machine learning application in measure and control.
Prof. Dr. Roberta Di Pace
Dr. Chiara Fiori
Dr. Facundo Storani
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 papers will be 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. Energies 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 2000 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
- dynamic network modelling
- simulation and optimization of transportation systems
- control and management of transportation systems
- traffic flow modelling
- public transport planning and operation
- transportation planning and traffic engineering
- multimodal transportation and terminals
- sensors and automatic data collection methods
- port and maritime operations
- city logistics
- ITS for multimodal transportation systems
- advanced mobility data collection
- cooperative, connected and automated vehicle system applications
- advanced vehicular communication technologies
- shared mobility
- micromobility
- urban mobility innovations
- applications of IoT to transportation
- MaaS
- smart cities and smart mobility
- big data in transportation
- vision-, multisource-, and learning-driven ITS
- multisensor data fusion techniques
- AI-enabled travel behavior analysis and prediction in multimodal environment
- machine learning application in measure and control