Intelligent Transportation System in the New Normal Era

Dear Colleagues,

As a core use case for smart cities, transportation still poses significant challenges for metropolitan areas. Urban mobility is highly affected by traffic congestion, with significant costs in terms of lost time, productivity, and increased pollution. Alleviating traffic congestion hinges on the ability of Intelligent Transportation Systems (ITSs) to predict traffic states, estimate and optimize vehicle flows by dynamically manipulating traffic signals, and improve passenger flow via public transportation demand prediction. This requires modeling complex spatiotemporal correlations among neighboring regions, while also considering external factors such as traffic events, holidays, and weather conditions. Road traffic accidents are another major factor behind traffic congestion; in this direction, further research efforts are needed on traffic incident detection and prevention, leveraging data mined from traffic or dashboard cameras, and even social media.

Traffic data collection is at the core of ITS applications, with collection methods originally based on infrastructure sensors gradually moving towards mobile sensors found in connected vehicles. Indeed, the advent of Connected and Autonomous Vehicles (CAVs) is precipitating a wide range of novel ITS-centric business models, with autonomous fleets set to completely reshape services such as ride-hailing and transportation of goods. As such, recent research is geared towards improving the efficiency by which individual CAVs sense their environment (e.g., pedestrians, road boundaries, traffic signals), construct scene representations, and take corresponding actions. On a collective level, another critical issue is how to design effective scheduling strategies for autonomous fleets to accomplish a given set of objectives under operational constraints. An example of this is how to optimize the frequency of visiting charging stations while maintaining acceptable service time and financial costs.

By sharing their fine-granularity, high-frequency sensed data, CAVs will also play a pivotal role in improving transportation management. Importantly, the risks associated with sharing such data underline the need for privacy-aware paradigms like federated learning. In a similar vein, it is imperative to secure CAVs from adversarial attacks. Autonomous vehicles are prone to a variety of hardware attacks, as well as jamming, spoofing, sybil, or eavesdropping attacks. Malicious actors may also interfere with how machine learning models deployed on CAVs perform semantic segmentation, object classification, flow estimation, or localization. At the decision layer, examples of safety-critical processes include ego-motion estimation, path planning, and agent trajectory prediction. Compromising any part of these layers could result in severe privacy and security risks not only for the ITS but also the smart city itself.

Considering the above ITS challenges, potential topics for this special edition include but are not limited to the following:

• Machine Learning for Traffic Big Data Analysis in ITS
• Sustainable Management theory and application
• Intelligent traffic control in ITS
• Learning from Homogenous/Heterogeneous Transportation Networks
• Sensing and vehicle driving in ITS environment
• Design of AV multi-modal logistics systems
• CAV fleets for urban logistics and road safety
• The coupling between urban mobility and energy
• Intelligent Transportation planning and system optimization
• Innovative modeling, simulation, and Optimization of ITS networks
• Multi-objective optimization in transportation operations
• Other areas related to ITS

Dr. James J.Q. Yu
Dr. Shuai Wang
Dr. Bo Fan
Dr. Shiyao Zhang
Dr. Christos Markos
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.

• intelligent transportation system
• traffic big data analysis
• transportation management
• intelligent traffic control
• autonomous driving
• connected vehicles
• urban mobility
• transportation operation and control