4 research outputs found
Taming Travel Time Fluctuations through Adaptive Stop Pooling
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
Identifying the threshold to sustainable ridepooling
Ridepooling combines trips of multiple passengers in the same vehicle and may
thereby provide a more sustainable option than transport by private cars. The
efficiency and sustainability of ridepooling is typically quantified by key
performance indicators such as the average vehicle occupancy or the total
distance driven by all ridepooling vehicles relative to individual transport.
However, even if the average occupancy is high and rides are shared,
ridepooling services may increase the total distance driven due to additional
detours and deadheading. Moreover, these key performance indicators are
difficult to predict without large-scale simulations or actual ridepooling
operation. Here, we propose a dimensionless parameter to estimate the
sustainability of ridepooling by quantifying the load on a ridepooling service,
relating characteristic timescales of demand and supply. The load bounds the
relative distance driven and uniquely marks the break-even point above which
the total distance driven by all vehicles of a ridepooling service falls below
that of motorized individual transport. Detailed event-based simulations and a
comparison with empirical observations from a ridepooling pilot project in a
rural area of Germany validate the theoretical prediction. Importantly, the
load follows directly from a small set of aggregate parameters of the service
setting and is thus predictable a priori. The load may thus complement standard
key performance indicators and simplify planning, operation and evaluation of
ridepooling services
Dynamic stop pooling for flexible and sustainable ride sharing
Ride sharing—the bundling of simultaneous trips of several people in one vehicle—may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality