English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ride-sourcing services offered by companies like Uber and Didi have grownrapidly in the last decade. Understanding the demand for these services isessential for planning and managing modern transportation systems. Existingstudies develop statistical models for ride-sourcing demand estimation at anaggregate level due to limited data availability. These models lack foundationsin microeconomic theory, ignore competition of ride-sourcing with other travelmodes, and cannot be seamlessly integrated into existing individual-level(disaggregate) activity-based models to evaluate system-level impacts ofride-sourcing services. In this paper, we present and apply an approach forestimating ride-sourcing demand at a disaggregate level using discrete choicemodels and multiple data sources. We first construct a sample of trip-basedmode choices in Chicago, USA by enriching household travel survey with publiclyavailable ride-sourcing and taxi trip records. We then formulate a multivariateextreme value-based discrete choice with sampling and endogeneity correctionsto account for the construction of the estimation sample from multiple datasources and endogeneity biases arising from supply-side constraints and surgepricing mechanisms in ride-sourcing systems. Our analysis of the constructeddataset reveals insights into the influence of various socio-economic, land useand built environment features on ride-sourcing demand. We also deriveelasticities of ride-sourcing demand relative to travel cost and time. Finally,we illustrate how the developed model can be employed to quantify the welfareimplications of ride-sourcing policies and regulations such as terminatingcertain types of services and introducing ride-sourcing taxes.

A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections