Big Data Analysis and Visualization in Transport Infrastructure: Challenges for Operations, Control, and Planning

Dear colleagues, 

Transport Systems are equipped with multiple systems that collect large quantities of data every day, every hour, or even every second, at various forms and by different technologies. This means the traffic and transport data in many cases are 'voluminous', are generated at a high 'velocity', have a high 'variety', and have a considerable 'veracity'. The challenge today is to process this big data to derive meaningful results and insights. 

All modes of a transport network face a form of this challenge, large amounts of trajectory data, count data, and traffic signal control data are generated in the road network, while transit systems (rail, bus, ferry) generate large volumes of data from their automatic fare collection and vehicle location technologies. Shared mobility and combined mobility solutions such as Mobility as a Service (MaaS) or Infrastructure as a Service (IaaS), generate a multi-modal and door-to-door trip and activity information.

This special issue is calling for papers that address one aspect of this big data challenge whether the data management side or operations and control of an existing system or improving the planning of the transport infrastructure in the future.  Some aspects of the data side and the method side are listed below.

Various Data Sources

• Traffic Sensors for traffic count, occupancy, and speed
• Automatic data collection technologies, such as Bluetooth, WiFi, GPS, Image Processing, and Video Processing
• Smart Phone Data including Global Positioning System (GPS) tracks
• Smart Card Data from public transport Automatic Fare Collection (AFC) Systems
• Automatic Vehicle Location from trains, buses, bikes, and ferries
• Traffic Signal Control Systems
• Autonomous Vehicle and Connected Vehicle Systems

Methodologies

• Visualization of Big Data Sources
• Cleaning, Management, and Storage technologies
• Development of Performance measures, indices, and indicators
• Data Analytics and Analysis Methods
• Application of Artificial Intelligence and Machine Learning methods
• Optimization of Transport Infrastructure
• Data Fusion and Error handling
• Network control methods based on the Macroscopic Fundamental Diagram (MFD)
• Control algorithms to improve safety, efficiency, social welfare, and environmental impacts

Dr. Behrang Assemi
Dr. Ning Zhu
Dr. Mahmoud Mesbah
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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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.

• Big Data
• Machine Learning
• Intelligent Transportation Systems
• Flow Prediction
• Travel Time Estimation
• Travel Behaviour