Taming Travel Time Fluctuations through Adaptive Stop Pooling

TL;DR

This paper proposes adaptive stop pooling in ride sharing to reduce travel time fluctuations and improve service reliability by adjusting walking distances based on demand, leading to more efficient and acceptable services.

Contribution

It introduces an adaptive stop pooling method that dynamically adjusts walking distances to mitigate travel time variability in ride sharing services.

Findings

Travel time fluctuations can be significantly reduced.

Adaptive stop pooling improves average travel times.

Service quality and acceptance are likely to increase.

Abstract

Ride sharing services combine trips of multiple users in the same vehicle and may provide more sustainable transport than private cars. As mobility demand varies during the day, the travel times experienced by passengers may substantially vary as well, making the service quality unreliable. We show through model simulations that such travel time fluctuations may be drastically reduced by stop pooling. Having users walk to meet at joint locations for pick-up or drop-off allows buses to travel more direct routes by avoiding frequent door-to-door detours, especially during high demand. We in particular propose adaptive stop pooling by adjusting the maximum walking distance to the temporally and spatially varying demand. The results highlight that adaptive stop pooling may substantially reduce travel time fluctuations while even improving the average travel time of ride sharing services, especially for high demand. Such quality improvements may in turn increase the acceptance and adoption of ride sharing services.