4,280 research outputs found
The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services
The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces
Asymptotically Optimal Algorithms for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems
The Stacker Crane Problem is NP-Hard and the best known approximation
algorithm only provides a 9/5 approximation ratio. The objective of this paper
is threefold. First, by embedding the problem within a stochastic framework, we
present a novel algorithm for the SCP that: (i) is asymptotically optimal,
i.e., it produces, almost surely, a solution approaching the optimal one as the
number of pickups/deliveries goes to infinity; and (ii) has computational
complexity O(n^{2+\eps}), where is the number of pickup/delivery pairs
and \eps is an arbitrarily small positive constant. Second, we asymptotically
characterize the length of the optimal SCP tour. Finally, we study a dynamic
version of the SCP, whereby pickup and delivery requests arrive according to a
Poisson process, and which serves as a model for large-scale demand-responsive
transport (DRT) systems. For such a dynamic counterpart of the SCP, we derive a
necessary and sufficient condition for the existence of stable vehicle routing
policies, which depends only on the workspace geometry, the stochastic
distributions of pickup and delivery points, the arrival rate of requests, and
the number of vehicles. Our results leverage a novel connection between the
Euclidean Bipartite Matching Problem and the theory of random permutations,
and, for the dynamic setting, exhibit novel features that are absent in
traditional spatially-distributed queueing systems.Comment: 27 pages, plus Appendix, 7 figures, extended version of paper being
submitted to IEEE Transactions of Automatic Contro
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
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