17,147 research outputs found
A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
We propose a ridesharing strategy with integrated transit in which a private
on-demand mobility service operator may drop off a passenger directly
door-to-door, commit to dropping them at a transit station or picking up from a
transit station, or to both pickup and drop off at two different stations with
different vehicles. We study the effectiveness of online solution algorithms
for this proposed strategy. Queueing-theoretic vehicle dispatch and idle
vehicle relocation algorithms are customized for the problem. Several
experiments are conducted first with a synthetic instance to design and test
the effectiveness of this integrated solution method, the influence of
different model parameters, and measure the benefit of such cooperation.
Results suggest that rideshare vehicle travel time can drop by 40-60%
consistently while passenger journey times can be reduced by 50-60% when demand
is high. A case study of Long Island commuters to New York City (NYC) suggests
having the proposed operating strategy can substantially cut user journey times
and operating costs by up to 54% and 60% each for a range of 10-30 taxis
initiated per zone. This result shows that there are settings where such
service is highly warranted
Efficient and Privacy-Preserving Ride Sharing Organization for Transferable and Non-Transferable Services
Ride-sharing allows multiple persons to share their trips together in one
vehicle instead of using multiple vehicles. This can reduce the number of
vehicles in the street, which consequently can reduce air pollution, traffic
congestion and transportation cost. However, a ride-sharing organization
requires passengers to report sensitive location information about their trips
to a trip organizing server (TOS) which creates a serious privacy issue. In
addition, existing ride-sharing schemes are non-flexible, i.e., they require a
driver and a rider to have exactly the same trip to share a ride. Moreover,
they are non-scalable, i.e., inefficient if applied to large geographic areas.
In this paper, we propose two efficient privacy-preserving ride-sharing
organization schemes for Non-transferable Ride-sharing Services (NRS) and
Transferable Ride-sharing Services (TRS). In the NRS scheme, a rider can share
a ride from its source to destination with only one driver whereas, in TRS
scheme, a rider can transfer between multiple drivers while en route until he
reaches his destination. In both schemes, the ride-sharing area is divided into
a number of small geographic areas, called cells, and each cell has a unique
identifier. Each driver/rider should encrypt his trip's data and send an
encrypted ride-sharing offer/request to the TOS. In NRS scheme, Bloom filters
are used to compactly represent the trip information before encryption. Then,
the TOS can measure the similarity between the encrypted trips data to organize
shared rides without revealing either the users' identities or the location
information. In TRS scheme, drivers report their encrypted routes, an then the
TOS builds an encrypted directed graph that is passed to a modified version of
Dijkstra's shortest path algorithm to search for an optimal path of rides that
can achieve a set of preferences defined by the riders
Modeling and Evaluation of a Ridesharing Matching System from Multi-Stakeholders\u27 Perspective
With increasing travel demand and mobility service quality expectations, demand responsive innovative services continue to emerge. Ridesharing is an established, yet evolving, mobility option that can provide more customized, reliable shared service without any new investment in the transportation infrastructure. To maximize the benefits of ridesharing service, efficient matching and distribution of riders among available drivers can provide a reliable mobility option under most operating conditions. Service efficiency of ridesharing depends on the system performance (e.g., trip travel time, trip delay, trip distance, detour distance, and trip satisfaction) acceptable to diverse mobility stakeholders (e.g., riders, drivers, ridesharing operators, and transportation agencies). This research modeled the performance of a ridesharing service system considering four objectives: (i) minimization of system-wide passengers’ waiting time, (ii) minimization of system-wide vehicle miles travelled (VMT), (iii) minimization of system-wide detour distance, and (iv) maximization of system-wide drivers’ profit. Tradeoff evaluation of objectives revealed that system-wide VMT minimization objective performed best with least sacrifices on the other three objectives from their respective best performance level based on set of routes generated in this study. On the other hand, system-wide drivers’ profit maximization objective provided highest monetary incentives for drivers and riders in terms of maximizing profit and saving travel cost respectively. System-wide minimization of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing system modeling and performance evaluation, and can be used in developing and implementing ridesharing service considering multiple stakeholders’ concerns
- …