As traffic becomes more difficult to manage, many cities have begun increasing their network of bike lanes to provide access to alternative forms of transportation. In fact, recent census data showed certain cities doubled their cyclist population over the past 10 years. The need to meet this demand sparked a series of debates between commuters due to the fear of increased car congestion as a result of new bike lanes. 

In a working paper, University of Toronto’s Sheng Liu, UCLA Anderson School of Management Assistant Professor Auyon Siddiq and Jingwei Zhang, a Ph.D. student, developed a method for planning bike lane networks using a data-driven approach along with input from city planners. Their research considers cycling trends and anticipated car traffic, suggesting that it is possible to expand cycling ridership without a dramatic increase in overall car traffic congestion. Furthermore, their research demonstrated the addition of bike lanes can even reduce car congestion in some road segments. 

The tool generates recommendations for the best locations and optimal length of bike lanes for each city using data from over half a dozen sources. This tool also allows city planners to set a budget constraint in its optimization model, creating more customizable parameters that can be utilized by anyone. 

Read more about this data-driven approach to optimizing bike paths at UCLA Anderson Review. 

Study Authors: 

Auyon Siddiq, UCLA Anderson School of Management 

Jingwei Zhang, Ph.D. student, Operations Management at UCLA Anderson School of Management 

Sheng Liu, Operations Management and Statistics at the University of Toronto, Rotman School of Management