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

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.