9,769 research outputs found
Study on k-shortest paths with behavioral impedance domain from the intermodal public transportation system perspective
Behavioral impedance domain consists of a theory on route planning for pedestrians, within which constraint management is considered. The goal of this paper is to present the k-shortest path model using the behavioral impedance approach. After the mathematical model building, optimization problem and resolution problem by a behavioral impedance algorithm, it is discussed how behavioral impedance cost function is embedded in the k-shortest path model. From the pedestrian's route planning perspective, the behavioral impedance cost function could be used to calculate best subjective paths in the objective way.Postprint (published version
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem
In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P)
ride-matching problem as a binary program. We formulate this problem as a
many-to-many problem in which a rider can travel by transferring between
multiple drivers, and a driver can carry multiple riders. We propose a
pre-processing procedure to reduce the size of the problem, and devise a
decomposition algorithm to solve the original ride-matching problem to
optimality by means of solving multiple smaller problems. We conduct extensive
numerical experiments to demonstrate the computational efficiency of the
proposed algorithm and show its practical applicability to reasonably-sized
dynamic ride-matching contexts. Finally, in the interest of even lower solution
times, we propose heuristic solution methods, and investigate the trade-offs
between solution time and accuracy
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs
Traversing environments with arbitrary obstacles poses significant challenges
for bipedal robots. In some cases, whole body motions may be necessary to
maneuver around an obstacle, but most existing footstep planners can only
select from a discrete set of predetermined footstep actions; they are unable
to utilize the continuum of whole body motion that is truly available to the
robot platform. Existing motion planners that can utilize whole body motion
tend to struggle with the complexity of large-scale problems. We introduce a
planning method, called the "Randomized Possibility Graph", which uses
high-level approximations of constraint manifolds to rapidly explore the
"possibility" of actions, thereby allowing lower-level motion planners to be
utilized more efficiently. We demonstrate simulations of the method working in
a variety of semi-unstructured environments. In this context,
"semi-unstructured" means the walkable terrain is flat and even, but there are
arbitrary 3D obstacles throughout the environment which may need to be stepped
over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation
201
Coverage and mobile sensor placement for vehicles on predetermined routes: a greedy heuristic approach
Road potholes are not only nuisance but can also damage vehicles and pose serious safety risks for drivers. Recently, a number of approaches have been developed for automatic pothole detection using equipment such as accelerometers, image sensors or LIDARs. Mounted on vehicles, such as taxis or buses, the sensors can automatically detect potholes as the vehicles carry out their normal operation. While prior work focused on improving the performance of a standalone device, it simply assumed that the sensors would be installed on the entire fleet of vehicles. When the number of sensors is limited it is important to select an optimal set of vehicles to make sure that they do not cover similar routes in order to maximize the total coverage of roads inspected by sensors. The paper investigates this problem for vehicles that follow pre-determined routes, formulates it as a linear optimization problem and proposes a solution based on a greedy heuristic. The proposed approach has been tested on an official London bus route dataset containing 713 routes and showed up to 78% improvement compared to a random sensor placement selected as a baseline algorithm
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