Planning Bike Lanes with Data: Ridership, Congestion, and Path Selection
Bibliographic record
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, reallocating road capacity to cycling is often contentious because of 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 data sets 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 a 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. This paper was accepted by Karan Girotra, operations management. Funding: Funding: The authors acknowledge funding from the UCLA Anderson Easton Technology Management Center (Siddiq & Zhang) and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950] and TD-MDAL Research Grant from the Rotman School of Management (Liu). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00775 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".