In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ridehailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

Key findings

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Willingness to pool (WTP) ratio increases as the average duration, distance, and fare difference percentage of trips increase.

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Trips starting or ending in economically disadvantaged areas (zones with low median income) are more likely to have higher WTP ratios.

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In general, it was observed that trips starting or ending in zones with high population or job density have a lower WTP ratio, motivating a need to further explore potential confounding factors in future analyses.

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