CityX is a research lab led by Dr. Meead Saberi at University of New South Wales (UNSW) Sydney, under the Research Centre for Integrated Transport Innovation (rCITI). Our research focuses on improving scientific understanding of smart cities through development and application of data-model driven methods to better plan, manage and operate urban transport systems.
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Recently Published
Network traffic instability with automated driving and cooperative mergingTransportation Research Part C: Emerging Technologies 138, 103626
https://doi.org/10.1016/j.trc.2022.103626 Abstract. Connected and/or automated vehicles (CAVs or AVs) have been shown to dampen stop-and-go waves in mixed autonomy traffic, thus improving string stability. However, their effects on network traffic instability due to turning and merging maneuvers are less known. In this paper, we characterize such effects using the macroscopic or network fundamental diagram (MFD or NFD). We first revisit and extend the theoretical two-ring network, and then develop an integrated modeling and simulation framework that explicitly accounts for different microscopic traffic models of human-driven vehicles (HVs), AVs, and CAVs. Results suggest that network traffic instability resulting from turning and merging maneuvers persists even if vehicles become automated and cooperative. When the turning probability is low, the presence of CAVs does not induce a significant change in the bifurcation point of the NFD. Scatter in both link fundamental diagrams (FDs) and NFDs, however, reduces resulting in higher and more stable network flows. When the turning probability is high, AVs without cooperation turn out to worsen network traffic stability, giving rise to an NFD that undergoes bifurcation long before the theoretical critical network density is reached. This is in contrast to the case with CAVs that perform cooperative merging. Results also suggest that, whenever the penetration rate of CAVs is too low or too high, making HVs connected is not as effective in delaying the bifurcation of the NFD as when the penetration rate is moderate. We further compare cooperative merging with adaptive signal control and adaptive driver routing to demonstrate its benefits in improving network flows. Simulation experiments on a real motorway segment in Sydney, Australia are also performed to confirm our findings. |
Urban data visualization, analytics,
modeling, and simulation
SELECTED PROJECTS
We develop advanced dynamic models of large-scale networks, specifically simulation-based dynamic traffic assignment with advanced features such as road pricing and perimeter control.
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This project maps urban greenery and traffic noise in the Sydney CBD using emerging crowd-sourced and mobile phone-based data.
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Changing Sydney uses visual analytics to demonstrate how population and job densities has been changing in Sydney over a decade using data from ABS Census 2006 and 2016.
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This project builds modeling tools to evaluate the impact of different congestion pricing schemes on Melbourne network traffic to inform transport pricing policies.
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Here, we describe the science behind urban network traffic jams, known as network traffic flow theory using our Melbourne DTA model.
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Places by Metro is an interactive application reinventing public transport users experience, connecting businesses to travelers across Melbourne.
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To learn more about our ongoing and completed projects, please visit the Research page.
PhD Positions
We are continuously looking for talented and young students to join our research group to do a PhD in Transportation. UNSW offers postgraduate research scholarships to competitive applicants covering tuition fees and an annual stipend of ~$30,000 AUD plus a possible top up and conference travel grant. If you are interested, please email us your resume.
Our research is recognized and supported by
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