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In microeconomics, consumer surplus represents the economic gain that consumers experience when they purchase a good or service for less than the highest price they are willing to pay. This surplus arises from the characteristics of the demand function, which links the quantity of a good to the price consumers are willing to pay. As the quantity of a good increases, the price that consumers are willing to pay for each additional unit typically decreases, resulting in a downward-sloping demand...
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Modeling the Demand for Shared E-Scooter Services.

Muntahith Mehadil Orvin1, Jashan Kaur Bachhal2, Mahmudur Rahman Fatmi1

  • 1School of Engineering, Department of Civil Engineering, University of British Columbia, Kelowna, BC, Canada.

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|April 2, 2026
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Summary
This summary is machine-generated.

This study models shared e-scooter service (SES) demand using a zero-inflated negative binomial (ZINB) model. Findings reveal SES demand is influenced by temporal, weather, infrastructure, and neighborhood factors, aiding policy decisions.

Keywords:
demand estimationeffects of information and communication technologies (ICT) on travel choicesemergingmodels/modelingplanning and analysisshared mobilitytransportation demand forecasting

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Area of Science:

  • Transportation Science
  • Urban Planning
  • Data Science

Background:

  • Shared e-scooter services (SES) are increasingly popular urban mobility options.
  • Understanding the spatio-temporal demand patterns of SES is crucial for effective service management and policy.
  • Count data for SES trips often exhibit excess zeros, posing challenges for traditional modeling approaches.

Purpose of the Study:

  • To model the spatio-temporal variation in shared e-scooter service (SES) demand.
  • To identify key factors influencing SES demand at a granular geographic level.
  • To compare the performance of a zero-inflated negative binomial (ZINB) model against other count data models.

Main Methods:

  • Development and application of a zero-inflated negative binomial (ZINB) model using trip origin count data.
  • Estimation and comparison of various count models, including hurdle models.
  • Validation of the best-performing model using a hold-out sample.

Main Results:

  • The ZINB model demonstrated superior goodness-of-fit compared to alternative count models.
  • SES demand is significantly influenced by temporal factors (season, time of day, day of week), weather conditions, transportation infrastructure (cycle tracks), land use patterns, and neighborhood characteristics (hotels, population age).
  • Model validation confirmed satisfactory predictive performance for SES demand.

Conclusions:

  • The ZINB model effectively captures the complexities of SES demand, including excess zeros and over-dispersion.
  • Identifying specific drivers of SES demand provides actionable insights for urban planners and policymakers.
  • Effective policy-making for supporting and managing SES requires a nuanced understanding of when and where demand is highest.