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

80 Pages Posted: 24 Mar 2022 Last revised: 11 Feb 2024

See all articles by Sheng Liu

Sheng Liu

Rotman School of Management

Auyon Siddiq

University of California, Los Angeles (UCLA) - Anderson School of Management

Jingwei Zhang

Cornell University

Date Written: March 12, 2022

Abstract

Urban infrastructure is vital for sustainable cities. In recent years, municipal governments have invested heavily in the expansion of bike lane networks to meet growing demand, promote ridership, and reduce emissions. However, re-allocating road capacity to cycling is often contentious due to the risk of amplifying traffic congestion. In this paper, we develop a method for planning bike lanes that accounts for ridership and congestion effects. We first present a procedure for estimating parameters of a traffic equilibrium model, which combines an inverse optimization method for predicting driving times with an instrumental variables method for estimating a commuter mode choice model. We then formulate a prescriptive model that selects paths in a road network for bike lane installation while endogenizing cycling demand and driving travel times. We conduct an empirical study on the City of Chicago that brings together several datasets that describe the urban environment -- including the road and bike lane networks, vehicle flows, commuter mode choices, bike share trips, driving and cycling routes, demographic features, and points of interest -- with the goal of estimating 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 cycling ridership from 3.6% to 6.1%, with at most an 9.4% increase in driving times. We also find that three intuitive heuristics for bike lane planning can lead to lower ridership and worse congestion outcomes, highlighting the value of a holistic and data-driven approach to urban infrastructure planning.

Keywords: Transportation, urban planning, network design, estimation, analytics

Suggested Citation

Liu, Sheng and Siddiq, Auyon and Zhang, Jingwei, Planning Bike Lanes with Data: Ridership, Congestion, and Path Selection (March 12, 2022). Available at SSRN: https://ssrn.com/abstract=4055703 or http://dx.doi.org/10.2139/ssrn.4055703

Sheng Liu

Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada

Auyon Siddiq

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Jingwei Zhang (Contact Author)

Cornell University ( email )

Cornell Univeristy
Ithaca, NY 14850
United States

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