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

61 Pages Posted: 24 Mar 2022

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

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

Date Written: March 12, 2022

Abstract

Urban infrastructure is essential to building 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 vehicle capacity in a road network to cycling is often contentious due to the risk of amplifying traffic congestion. In this paper, we develop a method for planning bike lane networks that accounts for ridership and congestion effects. 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.

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)

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

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

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