Research

Accepted Papers

Computing the Conditional Entry-State Distribution in Erlang Loss Systems (PDF).

with Fernanda Bravo and M. Keith Chen. Operations Research Letters. May 2021.

We consider an Erlang loss system with state-dependent arrival rates. Given the system is in steady-state and there are j customers being served, the system operator may wish to know about the distribution of arrival states for the j customers in service. Specifically, they may want the steady-state probability that any given customer entered when the system had i servers busy, given j customers are currently being served. We term this metric the conditional entry-state distribution and develop an algorithm to compute it.

Submitted / Working Papers

Free Rides in Dockless, Electric Vehicle Sharing Systems (PDF).

with Fernanda Bravo and Jake Feldman. Second Major Revision in M&SOM.

We study free-ride policies as a mechanism to incentivize users of a "dockless" or "free-floating" electric vehicle sharing system (EVSS) to park vehicles at charging stations in order to maintain a charged fleet. A balanced system has a fleet that is adequately charged and evenly dispersed throughout the city. If left to unfold naturally, the system would fall out of balance, and revenue and customer experience might suffer. Most sharing systems use manual repositioning to achieve this balance, but we consider pricing incentives as an alternative method.

We develop an infinite horizon dynamic program to analyze free-ride policies. We focus on an EVSS that offers free rides to customers if they return vehicles to charging stations. We build on this initial formulation to construct a mixed-integer program that outputs intuitive, battery-threshold rules for when to offer free rides. In a discrete-event simulation model using real data from an EVSS, we compare the performance of this simple policy against other sophisticated policies, including the commonly used fine-based policy. Our simulation reveals this three-dimensional trade-off between customer satisfaction, revenue, and operational complexity. Furthermore, we find that the cost of repositioning and the customer heterogeneity in the likelihood to accept a discount are major drivers of the frequency of free-ride offers. Our results are robust under many demand patterns and under a variety of network settings.

Coverage: Scooter Recharging: Should Companies Offer Customers Carrot or Stick?

Willingness-to-Walk: Measuring Spatial Elasticity in Urban Cities

with Fernanda Bravo and M. Keith Chen. Submitted and Draft Available Upon Request.

Using real data from a natural pricing experiment in a large metropolitan city, we estimate the "sensitivity-to-walking" (StW), a value that captures consumers' trade-off between the price of parking and the amount of distance required to walk. The results are robust against varying assumptions on the structural form of utility and when considering factors such as weather (i.e. rain) and time of day.

Price Elasticity of Parking: Estimates from Mobile Phone Payments Data

with Fernanda Bravo and M. Keith Chen. Submitted to Transportation Research Part A. Draft Available Upon Request.

Using a dataset of over half a million transactions spanning over four years from a mid-sized U.S. city's mobile phone application for on-street parking payments, we estimate the price elasticity of parking and the users' response time to price changes. After a price increase of 20% from $1.25 per hour to $1.50 per hour, we find the average price elasticity of parking demand to be between -3.42 and -1.57, which is slightly higher than existing estimates. Our analysis suggests that it takes between 6 to 8 weeks after the price change for users to respond to the price change.

In Progress

Dynamic Pricing of Reusable Resources: An Application to On-street Parking

with Fernanda Bravo and M. Keith Chen. In Progress.

Motivated by real-time, dynamic pricing of on-street parking, we analyze a general framework to determine the optimal prices in a system with multiple zones where each zone contains a finite number of reusable resources. We consider a finite-horizon and a demand model with imperfect substitution, where a price increase for one zone may lead customers to use a different, adjacent zone. We develop a pricing model to determine the near-optimal set of prices. Finally, in a large-scale simulation seeded with real data, we benchmark our pricing policies against the status quo and other related algorithms.

The Impact of Dockless, Electric Vehicle Sharing on Public Transportation Demand

with Kira Stearns. Data Acquisition Complete. In Progress.

We measure how the emergence of dockless, electric vehicle sharing systems (i.e. Bird, Lime, JUMP, etc.) impact public transportation usage. Since these shared electric scooters and bicycles are hailed as last-mile or micro-mile solutions, many contend that they will promote public transportation usage. Using data from U.S. metropolitan cities, we quantify the impact.