Private vs. Pooled Transportation: Customer Preference and Design of Green Transport Policy

Abstract

Problem Definition: Large cities around the globe are facing an alarming growth in traffic congestion and greenhouse gas emissions, to which a significant contributor in recent years are on-demand cabs operated by ride-hailing platforms. Pooled transportation options, such as shuttle services, are cheaper and greener alternatives. However, those alternatives are still new to many customers and policy makers. The design and effectiveness of their promotion policies demand careful investigation. This paper studies how we can reduce the number of on-demand cabs on the road and, therefore, their GHG emissions, by promoting the usage of pooled transportation such as shuttle services.

Practical Relevance: Reducing the number of ride-hailing vehicles on the road has become an important goal in many cities' green transport policy design. For example, cities like New York implemented congestion surcharge policies targeting ride-hailing vehicles in recent years.

Methodology: In this work, we use detailed usage data and build a structural model to study customers' preferences on price and service features when choosing between private cabs and a scheduled shuttle service.

Results: Using the estimated model, we identify and evaluate the efficacy of improving service features like reducing the walking distance to shuttle stops on customers' choices of transport and, therefore, the number of ride-hailing vehicles on the road. We find that a 20\% decrease in walking distance can reduce more than one million dollars worth of CO2 emissions, and achieve 40\% of the benefits attained by commonly adopted congestion surcharge policies. In addition, we demonstrate the implementability of walking distance reduction policies by addition of stops on existing shuttle routes.

Managerial Implications: Our findings suggest that, by changing operations levers such as service features of pooled transport, cities can achieve a substantial amount of the benefits from reducing congestion compared with congestion surcharge policies with essentially zero cost, leading to much more efficient green transport policies.

Keywords: Empirical Operations Management, Ride-hailing Services, Congestion Management, Choice Model, Structural Estimation, Machine Learning

JEL Classification: M1, R4, Y1