  We study an urban bike lane planning problem based on the fine-grained bike
trajectory data, which is made available by smart city infrastructure such as
bike-sharing systems. The key decision is where to build bike lanes in the
existing road network. As bike-sharing systems become widespread in the
metropolitan areas over the world, bike lanes are being planned and constructed
by many municipal governments to promote cycling and protect cyclists.
Traditional bike lane planning approaches often rely on surveys and heuristics.
We develop a general and novel optimization framework to guide the bike lane
planning from bike trajectories. We formalize the bike lane planning problem in
view of the cyclists' utility functions and derive an integer optimization
model to maximize the utility. To capture cyclists' route choices, we develop a
bilevel program based on the Multinomial Logit model. We derive structural
properties about the base model and prove that the Lagrangian dual of the bike
lane planning model is polynomial-time solvable. Furthermore, we reformulate
the route choice based planning model as a mixed integer linear program using a
linear approximation scheme. We develop tractable formulations and efficient
algorithms to solve the large-scale optimization problem. Via a real-world case
study with a city government, we demonstrate the efficiency of the proposed
algorithms and quantify the trade-off between the coverage of bike trips and
continuity of bike lanes. We show how the network topology evolves according to
the utility functions and highlight the importance of understanding cyclists'
route choices. The proposed framework drives the data-driven urban planning
scheme in smart city operations management.
