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

with Sheng Liu and Auyon Siddiq 

Forthcoming, Management Science

  • WinnerINFORMS Public Sector Operations Research (PSOR) Best Paper Award, 2023 

  • Winner, POMS College of Sustainable Operations Student Paper Competition, 2022

  • Second Place, INFORMS IBM Best Student Paper Award, 2022 

  • Second PlaceSection on Location Analysis (SOLA) Best Student Paper Award, 2023 

  • Finalist, INFORMS Workshop on Data Mining and Decision Analytics Best Paper Award (Applied Track), 2022

  • Accepted to MSOM Sustainable Operations SIG, 2022

  • Media coverage: UCLA Anderson Review, Rotman Research Insights


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.

Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption.

with Auyon Siddiq and Christopher S. Tang

Manufacturing & Service Operations Management (2021), Vol. 24, No. 2: 956 -- 971


Abstract: In this paper, we present and analyze two incentive mechanisms for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a "mixed mode", which involves taking public transit and hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. We present a game-theoretic model to capture the strategic interactions among relevant stakeholders and examine the implications of both mechanisms on the stakeholders. Our findings offer cost-effective prescriptions for improving urban mobility and public transit ridership.

Green Product Development: Consumer Search and Government Regulations.

with Shixin Wang

In preparation


Abstract: In this paper, we consider the design of green products in both traditional quality, e.g., price, which is explicit to consumers, and environmental quality, such as fuel economy and carbon footprint, which is implicit to consumers. We model the strategic interactions between producers and consumers considering consumer search behavior and solve for optimal product price and environmental effort level of the producer to minimize carbon footprint. We also investigate the effect of government regulations on the minimum level of product environmental quality in equilibrium. 

Traffic and Emissions: Environmental Impact of Large-Scale Bike Lane Network Expansion.

with Sheng Liu, Xiaoyun Niu and Keji Wei

In preparation

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