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Related Experiment Video

Updated: Nov 1, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Excess demand prediction for bike sharing systems.

Xin Liu¹, Konstantinos Pelechrinis¹

¹Department of Informatics and Networked Systems, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States of America.

|June 17, 2021

Summary

This study introduces a new method to estimate unmet demand in shared mobility systems. It helps operators understand true demand patterns for better vehicle rebalancing and system efficiency.

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Area of Science:

Operations Research
Transportation Science
Data Science

Background:

Shared mobility systems (bikes, cars) require constant vehicle availability for success.
Rebalancing is crucial to manage vehicle distribution but relies on incomplete demand data.
Existing methods fail to capture unobserved demand, hindering accurate system analysis.

Purpose of the Study:

To develop a novel method for estimating excess demand and supply rates in shared transportation systems.
To improve the understanding of true demand patterns beyond observed trip data.
To provide insights for optimizing rebalancing strategies in mobility services.

Main Methods:

Identifying "excess demand pulses" (EDPs) in station availability data as indicators of unmet demand.
Utilizing trip and station availability data for demand-supply analysis.
Developing and applying a Skellam regression model to predict net demand.

Main Results:

The proposed method accurately estimates excess demand and supply rates.
Excess demand pulses serve as a reliable signal for unmet demand.
The Skellam regression model effectively predicts the difference between total demand and supply.

Conclusions:

The developed method enhances the analysis of shared mobility system dynamics.
Accurate estimation of excess demand is vital for effective rebalancing operations.
This approach offers a significant improvement over existing methods for understanding system-level demand.