13,332 research outputs found
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
No-boarding buses: Synchronisation for efficiency
We investigate a no-boarding policy in a system of buses serving bus
stops in a loop, which is an entrainment mechanism to keep buses synchronised
in a reasonably staggered configuration. Buses always allow alighting, but
would disallow boarding if certain criteria are met. For an analytically
tractable theory, buses move with the same natural speed (applicable to
programmable self-driving buses), where the average waiting time experienced by
passengers waiting at the bus stop for a bus to arrive can be calculated. The
analytical results show that a no-boarding policy can dramatically reduce the
average waiting time, as compared to the usual situation without the
no-boarding policy. Subsequently, we carry out simulations to verify these
theoretical analyses, also extending the simulations to typical human-driven
buses with different natural speeds based on real data. Finally, a simple
general adaptive algorithm is implemented to dynamically determine when to
implement no-boarding in a simulation for a real university shuttle bus
service.Comment: 49 pages, 9 figures. Video available here:
https://www.youtube.com/watch?v=SBNqvTr1Aj
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
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