Researchers develop formula to predict traffic gridlock

  Last updated December 12, 2018 at 3:53 pm

Topics:  

The transition from heavy traffic to complete gridlock follows the same quantifiable pattern in cities around the world, a study from UNSW and University of California Berkeley has found.




Transport authorities will be able to better predict when traffic congestion threatens to become gridlocked and take steps to intervene, following research involving UNSW’s Research Centre for Integrated Transport Innovation (rCITI).


In a paper published in PNAS journal by UNSW’s Dr Meead Saberi in collaboration with University of California Berkley’s Marta Gonzales, data from six major global cities was analysed to discover whether there were recognisable patterns to the development of a traffic snarl.


The research findings reveal new directions for studying urban traffic with a statistical physics framework.


The study examined traffic patterns in Boston, Porto, Lisbon, Rio de Janeiro and the San Francisco Bay area as well as computer modelling of Melbourne’s traffic system.


The researchers compared individual drivers’ travel times and how long it took for them to reach their destinations from the beginning of peak hour with drivers starting their journeys over the next hour of that peak period. The time it took drivers to reach their destinations was labelled ‘recovery time’.


Dr Saberi, who is senior lecturer in UNSW’s School of Civil and Environmental Engineering, said as recovery time passed the critical threshold where cars using the network outweighed the network’s full capacity, traffic started to move beyond congestion to network collapse, or gridlock.


“We have found that this simple “recovery time” measure is directly related to demand and supply. No surprise,” Dr Saberi said.


“What is surprising is that all the six cities that we have studied perform similarly.”


In other words, despite the various differences between cities in topography, population size, infrastructure, demand and other characteristics particular to each city, transition to gridlock happens in every city in a similar fashion.


The point when this transition happens may be unique to each city, but the researchers now have a quantifiable measure for it.


“The demand over supply ratio that we have measured is the ratio of the vehicle kilometres travelled in a city to the total vehicle distance the road network can support per hour,” Dr Saberi said.


“When this ratio exceeds a critical value, we see transition to gridlock. For example, a major global city like London may have a smaller critical value and that’s why it sees gridlock more often than, say, a smaller city like Adelaide.”


Transport authorities and governments could use the findings to understand when and how traffic forms and how likely it could develop into network collapse. Monitoring the number of vehicles entering the network and the recovery time could provide an early indication of whether a gridlock is likely to happen or not.


“This information can be used to intervene in the network by managing travel demand or increasing transport supply when and where needed.”


Dr Saberi said the researchers would like to follow up the study by looking at other patterns of the ‘spreading phenomenon’ that traffic congestion may follow.


“The next step is to go totally out of the box and use methods in public health, such as those to track how infectious diseases spread around the world, to monitor and predict traffic.”


Related


Four ways our cities can cut transport emissions in a hurry


Student-built solar car sets new efficiency world record


Towards a career in motorsport engineering




About the Author

UNSW Newsroom
The latest and best news from the University of New South Wales.

Published By

Featured Videos

Placeholder
Big Questions: Cancer
Placeholder
A future of nanobots in 180 seconds
Placeholder
Multi-user VR opens new worlds for medical research
Placeholder
Precision atom qubits achieve major quantum computing milestone
Placeholder
World's first complete design of a silicon quantum computer chip
Placeholder
Micro-factories - turning the world's waste burden into economic opportunities
Placeholder
Flip-flop qubits: a whole new quantum computing architecture
Placeholder
Ancient Babylonian tablet - world's first trig table
Placeholder
Life on Earth - and Mars?
Placeholder
“Desirable defects: Nano-scale structures of piezoelectrics” – Patrick Tung
Placeholder
Keeping Your Phone Safe from Hackers
Placeholder
Thru Fuze - a revolution in chronic back pain treatment (2015)
Placeholder
Breakthrough for stem cell therapies (2016)
Placeholder
The fortune contained in your mobile phone
Placeholder
Underwater With Emma Johnston
Placeholder
Flip-flop qubits: a whole new quantum computing architecture
Placeholder
The “Dressed Qubit” - breakthrough in quantum state stability (2016)
Placeholder
Pinpointing qubits in a silicon quantum computer (2016)
Placeholder
How to build a quantum computer in silicon (2015)
Placeholder
Quantum computer coding in silicon now possible (2015)
Placeholder
Crucial hurdle overcome for quantum computing (2015)
Placeholder
New world record for silicon quantum computing (2014)
Placeholder
Quantum data at the atom's heart (2013)
Placeholder
Towards a quantum internet (2013)
Placeholder
Single-atom transistor (2012)
Placeholder
Down to the Wire (2012)
Placeholder
Landmark in quantum computing (2012)
Placeholder
1. How Quantum Computers Will Change Our World
Placeholder
Quantum Computing Concepts – What will a quantum computer do?
Placeholder
Quantum Computing Concepts – Quantum Hardware
Placeholder
Quantum Computing Concepts – Quantum Algorithms
Placeholder
Quantum Computing Concepts – Quantum Logic
Placeholder
Quantum Computing Concepts – Entanglement
Placeholder
Quantum Computing Concepts - Quantum Measurement
Placeholder
Quantum Computing Concepts – Spin
Placeholder
Quantum Computing Concepts - Quantum Bits
Placeholder
Quantum Computing Concepts - Binary Logic
Placeholder
Rose Amal - Sustainable fuels from the Sun
Placeholder
Veena Sahajwalla - The E-Waste Alchemist
Placeholder
Katharina Gaus - Extreme Close-up on Immunity
Placeholder
In her element - Professor Emma Johnston
Placeholder
Martina Stenzel - Targeting Tumours with Tiny Assassins
Placeholder
How Did We Get Here? - Why are we all athletes?
Placeholder
How Did We Get Here? - Megafauna murder mystery
Placeholder
How Did We Get Here? - Why are we so hairy?
Placeholder
How Did We Get Here? - Why grannies matter
Placeholder
How Did We Get Here? - Why do only humans experience puberty?
Placeholder
How Did We Get Here? - Evolution of the backside
Placeholder
How Did We Get Here? - Why we use symbols
Placeholder
How Did We Get Here? - Evolutionary MasterChefs
Placeholder
How Did We Get Here? - The Paleo Diet fad
Placeholder
How Did We Get Here? - Are races real?
Placeholder
How Did We Get Here? - Are We Still Evolving?
Placeholder
How Did We Get Here? - Dangly Bits
Placeholder
Catastrophic Science: Climate Migrants
Placeholder
Catastrophic Science: De-Extinction
Placeholder
Catastrophic Science: Nuclear Disasters
Placeholder
Catastrophic Science: Storm Surges
Placeholder
Catastrophic Science: How the Japan tsunami changed science
Placeholder
Catastrophic Science: How the World Trade Centre collapsed
Placeholder
Catastrophic Science: Bushfires