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User-Centric and Network-Level Analysis of Remote Parking Strategies in Connected and Automated Vehicles

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – Four  years fixed term

Application deadline: 25 April 2024

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2024

For further details, contact: Dr Qian Lu


Introduction

This is a four year collaborative studentship which requires the candidate to spend two full years based at Coventry University (UK) and two years based at A*STAR Research Institute (Singapore). The usual pattern is first and fourth years at Coventry and second and third year at A*STAR Research Institute.

Project details

In the rapidly evolving landscape of urban transportation, Connected and Automated Vehicles (CAVs) have emerged as a groundbreaking innovation, reshaping the paradigms of parking in urban settings. As CAVs can operate without a human driver, they present revolutionary parking options, enabling vehicles to be dispatched to farther, possibly less crowded zones, thereby hinting at a reduced parking strain in central urban areas. This not only reimagines urban mobility but also introduces fresh challenges centred around user convenience and network traffic management.

From the user's perspective, the benefits of CAVs must align with convenience, ensuring CAVs are punctually scheduled for pickups, without unsettling user routines. Equally crucial is the economic aspect. While parking remotely may cut down costs, it may inadvertently increase expenses due to longer distances travelled, On the flip side, the broader traffic network might witness augmented congestion, a direct consequence of these innovative parking behaviours. To circumvent this, a deep dive into intricate routing strategies and comprehensive traffic management solutions tailored to CAVs' unique behaviours is imperative.

Funding

Coventry University and A*STAR jointly offer a fully-funded PhD studentship including tuition fees and stipend/bursary, that is open to both UK/EU and international graduates as part of the A*STAR Research Attachment Programme (ARAP).

Coventry University and A*STAR will only cover the stipend up to a maximum of two years each. Changes to the mobility pattern will only be considered under exceptional circumstances and can impact on the duration of the course and level of funding available. Should a candidate request any changes to mobility which results in the period spent in either the UK or Singapore extending beyond two years then the candidate is responsible for covering the stipend for that period.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.

PLUS

  • The potential to engage in innovative research and to complete the PhD within a 4 years.
  • A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).

Studies, both in the UK and globally, have touched upon individual facets of CAVs and urban traffic dynamics. However, a consolidated exploration delving into user needs, routing optimization, and their cumulative impact on urban traffic remains a niche area. Now, more than ever, as CAVs gain traction, there is an immediate need for this research. This project, by addressing these interlinked concerns, is not just timely but also a pioneering endeavour in the field of urban transportation.

The research questions to be explored by this research include

Q1: What are the specific user requirements and preferences concerning the remote parking strategies of Connected and Autonomous Vehicles (CAVs), and how can these requirements be efficiently met while maintaining cost-effectiveness?
Q2: How can routing strategies be developed and optimized to align with user needs, ensure the timely arrival of CAVs, and balance cost considerations such as fuel expenditure, toll charges, and parking fees?
Q3: What are the potential impacts of new parking behaviours of CAVs on urban network traffic flow, and how can these effects be mitigated to avoid potential congestion and disruptions in the network?

Additional requirements

  • Competent programming skills (in Matlab, Python, C++);
  • Experience in traffic simulation & modelling, route optimisation, human factors, machine learning & data science, and enthusiastic to work on an inter-disciplinary research project.

How to apply

To find out more about the project, please contact Dr Qian Lu

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

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