Topic
Managing traffic represent a true challenge for all traffic centres around the world, especially under an ever growing urban population living in cities. The increased traffic congestion is linked to an increased data availability from transport modes in the city, whether is regular or public transport, shared or on-demand services, electric scooters, etc.
This PhD project aims to study and apply various Artificial Intelligence algorithms and build a smart modelling framework that would help manage traffic across all available modes in a city, under both regular and disrupted traffic conditions (incidents/public events/travel restrictions due to pandemics, etc.). This implies a combination of multidisciplinary research from computer science and transport modelling.
The first part of the PhD will focus on conducting an intensive state-of-art on the usage of AI techniques for managing transport across the globe, followed by the construction of a modelling framework on a real-city study. This will be alimented by the best performing machine and deep learning models to predict traffic congestion and travel patterns across all travel modes in advance. While recurrent traffic modelling back-up by AI can provide good insights on traffic patterns, incidents/public events or temporary travel restrictions are stochastic events which have unique features changing dynamically in time. The second part of this project will aim at extending the modelling framework for early anomaly detection across all transport modes which can help to release early traffic alarms to operators for taking action. The last part of the PhD will consist in consolidating results and writing up the PhD thesis. Some examples of previous works on this topic can be found in [1]-[5].
Funding
The PhD student will be located within the Future Mobility Lab at UTS (www.fmlab.org) under the supervision of Dr. Simona Mihaita. This work is funded under the ARC Linkage Project LP180100114, a joint collaboration between UTS, Data61, Swinburne University of Technology and 2 major universities in Singapore: NUS (National University of Singapore) and NTU (National technology University of Singapore). Regular meet-ups and workshops will be organising for presenting new findings and learn from new techniques applied both in Australia and Singapore.
Domestic students are highly encouraged to apply – deadline 30th of May for Enrollment in Spring 2020. Deadline for international students: 30th of June 2020 for enrolment in Autumn 2021.
The candidate
Interested candidates must have solid background knowledge in computer science – machine and deep learning/data science and desirably transport modelling. Experience with handling large and complex data sets and strong PyThon programming skills are a big plus. We are looking for a candidate with a master by research qualification and demonstrated research capabilities (preferably through publications). Candidates with publications in major conferences/journals will be prioritised. The position will be open until the ideal candidate is identified.
For more details, please contact: adriana-simona.mihaita@uts.edu.au
References
Research Topicss
Smart public transport is a key element of smart cities. It moves people and services on daily basis contributing to the development and growth of cities. A well-planned and efficiently managed public transport service is a must for any smart city. Thanks to the advancements of information and communication technologies in Intelligent Transport Systems, we are now in the era of having more data than ever before. To help authorities effectively plan and manage public transport, data analytics can turn the data into insight leveraging Artificial Intelligent and Machine Learning technologies.
The successful applicant is expected to conduct a study focusing on data analytics for smart public transport. The research topics include, but not limited to, the following:
Applicants
Please apply if you have:
Steps for the application process:
About the Faculty
The Faculty of Engineering and IT(FEIT) is an innovative and research-intensive faculty with a strong reputation for its practice-based learning programs and industry engagement. The successful PhD student will be advised by Dr. Simona Mihaita and Dr. Yuming Ou within the Future Mobility Lab at University of Technology Sydney, under the Data Science Institute led by Distinguished Professor Fang Chen. The Data Science Institute has both strong ties with industry, as well as world-class research, providing the ideal environment for solving real-world problems, in close proximity to both academia and industry
About the research topics
The research aims to develop a multi-resolution traffic operational strategy for a semi-connected multimodal transport network. The theoretical framework includes adaptive traffic control systems (ATCS), cooperative adaptive cruise control (CACC), and connected and autonomous vehicles (CAV) technologies. The recent advancements in information and communication technologies (ICT) diffuse the international norm of transportation systems to pursue environment-friendly, person-centred and multi-modal systems while maximizing safety, mobility, and road efficiency. To be specific, the norm of a smart city has newly emergedas the ultimate goal of smart systems using artificial intelligenceand connected technology. Connected technology in transportation systems involvesa) a 5-Generation telecommunication technology, which enables higher capacity, lower latency, wider coverage for real-time data processes, and b) automated vehicles equipped with advanced driver assistance systems (ADAS) based on V2X (vehicle-to-everything) communication. These technologies can construct connected and cooperative intelligent transport systems (C-ITS) in the era of the Internet of Things (IoT) to enhance traffic safety and efficiency in urban traffic networks. Many cities around the world have been testing the connected technology in dedicated test-bed areas, but efforts are still far from a large-scale deployment and coordination at a city-scale level due to its complexity, large data sets being generated and difficulties to integratewith existing traffic control systems.
The details of the proposed Ph.D. research topics are listed, but not limited to, in the following areas:
About the applicant
Please apply if you have:
About the Faculty
The Faculty of Engineering and IT(FEIT) is an innovative and research intensive faculty with a strong reputation for its practice-based learning programs and industry engagement.The successful PhD student will be advised by Dr. Seunghyeon Lee (http://shlee.org) within the Future Mobility Lab( http://fmlab.org) at University of Technology Sydney, under Data Science Institute led by Distinguished Professor Fang Chen. The Data Science Institute has both strong ties with industry, aswell as world-class research, providing the ideal environment for solving real-world problems, in close proximity to both academia and industry
We are currently looking for two PhD students (both international or domestic) students to join our Future Mobility Lab here in University of Technology in Sydney, Faculty of Engineering and IT, School of Computer Science.
Please send the announcement further by either forwarding it to your peers or share it throughout your professional network (see Linked post).
Topics:
The positions will be open until filled. In order to start the communication, please send us:
Contact: adriana-simona.mihaita@uts.edu.au
How To Apply Guide:
https://www.uts.edu.au/research-and-teaching/research-degrees/applying-uts/how-apply