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

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The shareability potential of ride-pooling under alternative spatial demand patterns.

Jaime Soza-Parra¹, Rafał Kucharski², Oded Cats¹

¹Transportation & Planning Department, Delft University of Technology, Delft, The Netherlands.

Transportmetrica. A, Transport Science

|February 28, 2024

Summary

Ride-pooling services are most effective with concentrated destinations and longer trips, potentially reducing vehicle hours by 18-59%. Shifting to polycentric travel patterns has minimal impact on ride-pooling efficiency.

Keywords:

Ride-hailing Ride-pooling Shareability Shared mobility Travel demand

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Area of Science:

Transportation Science
Urban Planning
Operations Research

Background:

Ride-pooling services offer potential environmental and economic benefits by optimizing shared rides.
Understanding the impact of spatial travel demand patterns on ride-pooling effectiveness is crucial for service optimization.
Existing research often simplifies demand patterns, limiting insights into real-world complexities.

Purpose of the Study:

To investigate how diverse spatial patterns of travel demand influence the efficiency of ride-pooling services.
To quantify the shareability potential of ride-pooling across various demand scenarios.
To identify key demand characteristics that maximize ride-pooling benefits.

Main Methods:

Generation of synthetic travel demand patterns with varying numbers of attraction centers, destination dispersion, and trip length distributions.
Application of a strategic ride-pooling algorithm to assess shareability potential.
Utilizing ride-pooling specific metrics to evaluate service effectiveness.

Main Results:

Vehicle-hour reduction due to ride-pooling can range from 18% to 59% under fixed demand levels.
Ride-pooling efficiency increases with longer trip lengths and higher destination concentration around centers.
Transitioning from monocentric to polycentric demand patterns shows a limited effect on ride-pooling prospects.

Conclusions:

Spatial demand patterns, particularly destination concentration and trip length, significantly impact ride-pooling efficiency.
Ride-pooling services can achieve substantial operational improvements by aligning with specific demand characteristics.
Future research should consider the interplay of dynamic demand and network effects on ride-pooling performance.