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Can data pave the way to expand city bike lane networks?

September 1, 2022

More bike lanes doesn’t have to mean more traffic congestion. A study by Rotman Prof. Sheng Liu offers a data-driven way to increase bike ridership for the masses.

In 2021, the City of Chicago had grand plans to add 100 miles (161 kilometres) of new bike lanes over two years. But a new study by Rotman’s Sheng Liu suggests that building even just a quarter of that can increase bike ridership by more than 50 per cent, while adding less than 75 seconds to an average 15-minute car commute.

Professor Sheng Liu


Under a proposed model, Liu estimates that if Chicago added just 25 miles to their bike lane network, about 6.9 per cent of the population would use cycling as a main mode of transportation, up from the current 3.9 per cent. Meanwhile, increases in time spent on the road for drivers caps at just 8 per cent — and some streets even see faster driving times with the addition of bike lanes.

“The interaction between cars and bikes is important because bikes lanes ultimately take up space from drivers,” says Liu, an assistant professor of operations management and statistics.

While the pressure for more bike lanes in cities across North America is mounting, few studies have attempted to estimate the impact of bike lane expansion on system-wide congestion, says Liu.

“Can a city introduce more bike lanes while limiting travel time for cars? Can cities strike a balance between lower traffic congestion and bike ridership? Our paper aims to answer those questions,” he says.

Liu and his co-authors arrived at their model by analyzing and testing robust datasets on Chicago’s existing roads and bike lanes, travel mode preferences, bike share trips, taxi rides and traffic flow. The paper — co-authored by two researchers from the University of California, Los Angeles — is under revision for the journal Management Science.

“Our framework is especially valuable for municipalities with limited budgets to maximize the benefits of bike lane expansion, or in cities where the cycling infrastructure has matured past the point of ‘low hanging fruit’ and would benefit from more rigorous planning methods,” says Liu. At the Rotman School, Liu works on projects across a variety of topics, from improving accessibility for public services like Service Ontario to making arrival estimates for online deliveries more accurate. He has also contributed to the development of advanced decision-making tools for companies like JD.com, Amazon and Lyft.


“Can a city introduce more bike lanes while limiting travel time for cars? Can cities strike a balance between lower traffic congestion and bike ridership? Our paper aims to answer those questions.”

Sheng Liu, Assistant Professor, Operations Management and Statistics


In the classroom, he teaches operations management for Rotman Commerce students and works with Rotman PhD candidates to develop their data-driven research methods.

“I hope more cities will collect and share information on vehicle flow and cycling routes to make city improvements that are driven by data,” says Liu. Currently, he says many decisions about bike lane expansion lean into survey data, which is often riddled with biases that come with small sample sizes.

The researchers also explored how protective features in bike lanes — like barriers that offer separation from adjacent car lanes — will increase the utility and overall ridership for cyclists.

“Safety is a huge concern for cyclists, and the more protected bike lanes there are, the more people are incentivized to bike instead of taking a car or an Uber,” says Liu.

He says their framework may also be useful in planning other types of transportation infrastructure where congestion impacts are a key concern, such as the addition of dedicated bus or carpool lanes.

“Our method can also be extended to study the effect of bike lanes on greenhouse gas emissions,” says Liu.

“It may also be worth examining how bike lane expansion affects different populations, such as low-income and underserved neighborhoods, or groups for whom cycling may not be feasible, including seniors or people with disabilities.”

 


Written by Jessie Park | More Faculty Research Profiles »


Meet the researcher

Sheng Liu

Assistant Professor, Operations Management and Statistics

Read his full biography