Report Title: Planning Bike Lanes with Data: Ridership, Congestion, and Path Selection

Time: May 26, 2023, 11:00 am -12:30 am

Location: 217, Main Building, Zhongguancun Campus

Reported by: Assistant Professor Liu Sheng, University of Toronto

Reported by:

Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management. His research interests lie in supply chain and logistics, smart city operations (especially sustainable/climate-resilient system design), and data-driven decision-making (the integration of predictive and prescriptive analytics). His recent work explores the effective use of data to prescribe operational decisions for logistics and mobility systems. His research has been published in Management Science, Operations Research, Manufacturing & Service Operations Management, among others. He received a PhD in Operations Research from UC Berkeley in 2019 and a BSc in Industrial Engineering from Tsinghua University in 2014. He has contributed to the development of advanced decision-making tools for leading companies, including Amazon, Lyft, JD.com, and CNPC.

Introduction to report content:

In this paper, we present a method and empirical study for planning bike lane networks using data. We first present an estimator for recovering unknown parameters of a traffic equilibrium model from features of a road network and observed vehicle flows, which we show asymptotically recovers ground-truth parameters as the network grows large. We then present a prescriptive model that recommends paths in a road network for bike lane construction while endogenizing cycling demand, driver route choice, and driving travel times. In an empirical study on the City of Chicago, we bring together data on the road and bike lane networks, vehicle flows, travel mode choices, bike share trips, driving and cycling routes, and taxi trips to estimate the impact of expanding Chicago's bike lane network. We estimate that adding 25 miles of bike lanes as prescribed by our model can lift ridership from 3.9% to 6.9%, with at most an 8% increase in driving times. We also find that three intuitive heuristics for bike lane planning can lead to lower ridership and worse congestion outcomes, which highlights the value of a holistic and data-driven approach to urban infrastructure planning.

(Undertaken by: Department of Management Engineering, Research and Academic Center)